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Posted to commits@tvm.apache.org by lm...@apache.org on 2020/12/26 08:11:56 UTC
[tvm-site] branch asf-site updated: Docs build at Sat Dec 26
00:11:28 PST 2020
This is an automated email from the ASF dual-hosted git repository.
lmzheng pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 36ac1ca Docs build at Sat Dec 26 00:11:28 PST 2020
36ac1ca is described below
commit 36ac1cab6e80c4b4520df7f54d162fe30cd92629
Author: Lianmin Zheng <li...@gmail.com>
AuthorDate: Sat Dec 26 00:11:29 2020 -0800
Docs build at Sat Dec 26 00:11:28 PST 2020
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605 files changed, 20145 insertions(+), 23061 deletions(-)
diff --git a/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py b/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py
index db199fc..d7d43c7 100644
--- a/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py
+++ b/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py
@@ -41,7 +41,7 @@ __name__ == "__main__":` block.
#
# .. code-block:: bash
#
-# pip3 install --user psutil xgboost
+# pip3 install --user psutil xgboost cloudpickle
#
# To make TVM run faster in tuning, it is recommended to use cython
# as FFI of TVM. In the root directory of TVM, execute
diff --git a/docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb b/docs/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
similarity index 58%
copy from docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb
copy to docs/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
index e03fb03..4254721 100644
--- a/docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb
+++ b/docs/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nAuto-scheduling a Neural Network for x86 CPU\n============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_\n\nAuto-tuning for specific devices and workloads is critical for getting the\nbest performance. This is a tutorial on how to tune a whole neural\nnetwork for x86 CPU with the auto-scheduler.\n\nTo auto-tune a neural network, we partition the network into small subgraphs and \ntune them independently. Each subgraph is treated [...]
+ "\nAuto-scheduling a Neural Network for mali GPU\n=============================================\n**Author**: `Zhao Wu <https://github.com/FrozenGene>`_\n\nAuto-tuning for specific devices and workloads is critical for getting the\nbest performance. This is a tutorial on how to tune a whole neural\nnetwork for mali GPU with the auto-scheduler.\n\nTo auto-tune a neural network, we partition the network into small subgraphs and \ntune them independently. Each subgraph is treated as [...]
]
},
{
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "import numpy as np\n\nimport tvm\nfrom tvm import relay, auto_scheduler\nimport tvm.relay.testing\nfrom tvm.contrib import graph_runtime"
+ "import numpy as np\n\nimport tvm\nfrom tvm import relay, auto_scheduler\nimport tvm.relay.testing\nfrom tvm.contrib import graph_runtime\nimport os"
]
},
{
@@ -44,14 +44,14 @@
},
"outputs": [],
"source": [
- "def get_network(name, batch_size, layout=\"NHWC\", dtype=\"float32\"):\n \"\"\"Get the symbol definition and random weight of a network\"\"\"\n\n # auto-scheduler prefers NHWC layout\n if layout == \"NHWC\":\n image_shape = (224, 224, 3)\n elif layout == \"NCHW\":\n image_shape = (3, 224, 224)\n else:\n raise ValueError(\"Invalid layout: \" + layout)\n\n input_shape = (batch_size,) + image_shape\n output_shape = (batch_size, 1000)\n\n [...]
+ "def get_network(name, batch_size, layout=\"NHWC\", dtype=\"float32\"):\n \"\"\"Get the symbol definition and random weight of a network\"\"\"\n\n # auto-scheduler prefers NHWC layout\n if layout == \"NHWC\":\n image_shape = (224, 224, 3)\n elif layout == \"NCHW\":\n image_shape = (3, 224, 224)\n else:\n raise ValueError(\"Invalid layout: \" + layout)\n\n input_shape = (batch_size,) + image_shape\n output_shape = (batch_size, 1000)\n\n [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Extract Search Tasks\n--------------------\nNext, we extract the search tasks and their weights from a network.\nThe weight of a task is the number of appearances of the task's subgraph\nin the whole network.\nBy using the weight, we can approximate the end-to-end latency of the network\nas :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the\nlatency of a task and :code:`weight[t]` is the weight of the task.\nThe task scheduler will just optimize this objective.\n\n"
+ "Start an RPC Tracker and Register Devices to the Tracker\n--------------------------------------------------------\nPlease refer to the \"Start RPC Tracker\" and \"Register Devices to RPC Tracker\" setions\nin this `tutorial <tutorials-autotvm-start-rpc-tracker>` to start an RPC tracker\nand register devices to the tracker.\n\n"
]
},
{
@@ -62,14 +62,14 @@
},
"outputs": [],
"source": [
- "# Extract tasks from the network\nprint(\"Extract tasks...\")\nmod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)\ntasks, task_weights = auto_scheduler.extract_tasks(mod[\"main\"], params, target)\n\nfor idx, task in enumerate(tasks):\n print(\"========== Task %d (workload key: %s) ==========\" % (idx, task.workload_key))\n print(task.compute_dag)"
+ "# Replace this with the device key in your tracker\ndevice_key = \"rk3399\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Begin Tuning\n------------\nNow, we set some options for tuning and launch the search tasks\n\n* :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.\n You can set it to a small number (e.g., 200) for a fast demonstrative run.\n In practice, we recommend setting it around :code:`800 * len(tasks)`,\n which is typically enough for the search to converge.\n For example, there are 29 tasks in resnet-50, so we can set it as 20000.\n You ca [...]
+ "Extract Search Tasks\n--------------------\nNext, we extract the search tasks and their weights from a network.\nThe weight of a task is the number of appearances of the task's subgraph\nin the whole network.\nBy using the weight, we can approximate the end-to-end latency of the network\nas :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the\nlatency of a task and :code:`weight[t]` is the weight of the task.\nThe task scheduler will just optimize this objective.\n\n"
]
},
{
@@ -80,28 +80,21 @@
},
"outputs": [],
"source": [
- "def run_tuning():\n print(\"Begin tuning...\")\n tuner = auto_scheduler.TaskScheduler(tasks, task_weights)\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=200, # change this to 20000 to achieve the best performance\n runner=auto_scheduler.LocalRunner(repeat=10, enable_cpu_cache_flush=True),\n measure_callbacks=[auto_scheduler.RecordToFile(log_file)],\n )\n\n tuner.tune(tune_option)\n\n\n# We do not run the tuning in our web [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>Explain the printed information during tuning\n\n During the tuning, a lot of information will be printed on the console.\n They are used for debugging purposes. The most important info is the output\n of the task scheduler. The following table is a sample output.\n\n .. code-block:: c\n\n ----------------------------------------------------------------------\n ------------------------------ [ Task Scheduler ]\n ----- [...]
+ "# Extract tasks from the network\nprint(\"Extract tasks...\")\nmod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)\ntasks, task_weights = auto_scheduler.extract_tasks(mod[\"main\"], params, target, target_host)\n\nfor idx, task in enumerate(tasks):\n print(\"========== Task %d (workload key: %s) ==========\" % (idx, task.workload_key))\n print(task.compute_dag)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>Terminate the tuning earlier\n\n You can terminate the tuning earlier by forcibly killing this process.\n As long as you get at least one valid schedule for each task in the log file,\n you should be able to do the compilation (the secion below).</p></div>\n\n\n"
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>How to get the hardware parameters from remote device\n\n .. code-block:: python\n\n from tvm.auto_scheduler.utils import request_remote\n remote = request_remote(device_key, \"0.0.0.0\", 9190)\n ctx = remote.cl()\n max_shared_memory_per_block = ctx.max_shared_memory_per_block\n # There is no explicit local memory limition\n # so we can use INT32_MAX to disalbe the check on local_memory.\n max_local_memory_per [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Compile and Evaluate\n--------------------\nAfter auto-tuning, we can compile the network with the best schedules we found.\nAll measurement records are dumped into the log file during auto-tuning,\nso we can read the log file and load the best schedules.\n\n"
+ "Tuning and Evaluate\n-------------------\nNow, we set some options for tuning, launch the search tasks and evaluate the end-to-end performance\n\n* :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.\n You can set it to a small number (e.g., 200) for a fast demonstrative run.\n In practice, we recommend setting it around :code:`800 * len(tasks)`,\n which is typically enough for the search to converge.\n For example, there are 29 tasks [...]
]
},
{
@@ -112,14 +105,28 @@
},
"outputs": [],
"source": [
- "# Compile with the history best\nprint(\"Compile...\")\nwith auto_scheduler.ApplyHistoryBest(log_file):\n with tvm.transform.PassContext(opt_level=3, config={\"relay.backend.use_auto_scheduler\": True}):\n lib = relay.build(mod, target=target, params=params)\n\n# Create graph runtime\nctx = tvm.context(str(target), 0)\nmodule = graph_runtime.GraphModule(lib[\"default\"](ctx))\ndata_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))\nmodule.set_inpu [...]
+ "def tune_and_evaluate():\n print(\"Begin tuning...\")\n tuner = auto_scheduler.TaskScheduler(tasks, task_weights)\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=200, # change this to 20000 to achieve the best performance\n builder=auto_scheduler.LocalBuilder(build_func=\"ndk\" if use_ndk else \"default\"),\n runner=auto_scheduler.RPCRunner(\n device_key, host=\"0.0.0.0\", port=9190, repeat=3, timeout=50\n ),\n [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Explain the printed information during tuning\n\n During the tuning, a lot of information will be printed on the console.\n They are used for debugging purposes. The most important info is the output\n of the task scheduler. The following table is a sample output.\n\n .. code-block:: c\n\n ----------------------------------------------------------------------\n ------------------------------ [ Task Scheduler ]\n ----- [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Terminate the tuning earlier\n\n You can terminate the tuning earlier by forcibly killing this process.\n As long as you get at least one valid schedule for each task in the log file,\n you should be able to do the compilation (the secion below).</p></div>\n\n\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
+ "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
]
}
],
diff --git a/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb b/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb
index 63b2965..f003ad0 100644
--- a/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb
+++ b/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Install dependencies\n--------------------\nTo use autotvm package in TVM, we need to install some extra dependencies.\nThis step (installing xgboost) can be skipped as it doesn't need XGBoost\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost\n\nTo make TVM run faster in tuning, it is recommended to use cython\nas FFI of TVM. In the root directory of TVM, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-bl [...]
+ "Install dependencies\n--------------------\nTo use autotvm package in TVM, we need to install some extra dependencies.\nThis step (installing xgboost) can be skipped as it doesn't need XGBoost\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost cloudpickle\n\nTo make TVM run faster in tuning, it is recommended to use cython\nas FFI of TVM. In the root directory of TVM, execute\n(change \"3\" to \"2\" if you use python2):\n [...]
]
},
{
diff --git a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
index 06ec1b5..7c7b428 100644
--- a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
+++ b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Install dependencies\n--------------------\nTo use autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado\n\nTo make TVM run faster in tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cython3\n\nNow return to python code. Import packages.\n\n"
+ "Install dependencies\n--------------------\nTo use autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado cloudpickle\n\nTo make TVM run faster in tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cython3\n\nNow return to python code. Impor [...]
]
},
{
diff --git a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
index 76a30ec..148ebbf 100644
--- a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
+++ b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
@@ -47,7 +47,7 @@ __name__ == "__main__":` block.
#
# .. code-block:: bash
#
-# pip3 install --user psutil xgboost tornado
+# pip3 install --user psutil xgboost tornado cloudpickle
#
# To make TVM run faster during tuning, it is recommended to use cython
# as FFI of tvm. In the root directory of tvm, execute:
@@ -311,12 +311,12 @@ def tune_and_evaluate(tuning_opt):
#
# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
+#################################################################
+# .. _tutorials-autotvm-scale-up-rpc-tracker:
#################################################################
# Scale up measurement by using multiple devices
# ----------------------------------------------
-# .. _tutorials-autotvm-rpc-tracker:
-#
# If you have multiple devices, you can use all of them for measurement.
# TVM uses the RPC Tracker to manage distributed devices.
# The RPC Tracker is a centralized controller node. We can register all devices to
@@ -337,8 +337,8 @@ def tune_and_evaluate(tuning_opt):
#
# INFO:RPCTracker:bind to 0.0.0.0:9190
#
-# Then open another new terminal for the RPC server. We need to start one server
-# for each dedicated device. We use a string key to distinguish the types of devices.
+# Then open another new terminal for the RPC server. We need to start one dedicated server
+# for each device. We use a string key to distinguish the types of devices.
# You can pick a name you like.
# (Note: For rocm backend, there are some internal errors with the compiler,
# we need to add `--no-fork` to the argument list.)
diff --git a/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py b/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
index 3da9f3f..b098869 100644
--- a/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
+++ b/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
@@ -306,7 +306,7 @@ print("Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), n
# in function :code:`run_tuning`. Say,
# :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
# 4. If you have multiple target GPUs, you can use all of them for measurements to
-# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
# to learn how to use the RPC Tracker and RPC Server.
# To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
# with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
index 1a78641..705ba34 100644
--- a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
+++ b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cy [...]
+ "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado cloudpickle\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user cython\n [...]
]
},
{
@@ -58,6 +58,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"Start RPC Tracker\n-----------------\nTVM uses RPC session to communicate with ARM boards.\nDuring tuning, the tuner will send the generated code to the board and\nmeasure the speed of code on the board.\n\nTo scale up the tuning, TVM uses RPC Tracker to manage distributed devices.\nThe RPC Tracker is a centralized controller node. We can register all devices to\nthe tracker. For example, if we have 10 phones, we can register all of them\nto the tracker, and run 10 measurements [...]
]
},
@@ -65,7 +72,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Register devices to RPC Tracker\n-----------------------------------\nNow we can register our devices to the tracker. The first step is to\nbuild the TVM runtime for the ARM devices.\n\n* For Linux:\n Follow this section `build-tvm-runtime-on-device` to build\n the TVM runtime on the device. Then register the device to tracker by\n\n .. code-block:: bash\n\n python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rk3399\n\n (replace :code:`[HOST_IP]` with the IP addr [...]
+ "Register Devices to RPC Tracker\n-----------------------------------\nNow we can register our devices to the tracker. The first step is to\nbuild the TVM runtime for the ARM devices.\n\n* For Linux:\n Follow this section `build-tvm-runtime-on-device` to build\n the TVM runtime on the device. Then register the device to tracker by\n\n .. code-block:: bash\n\n python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rk3399\n\n (replace :code:`[HOST_IP]` with the IP addr [...]
]
},
{
diff --git a/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py b/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
index b662baf..c320495 100644
--- a/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
+++ b/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
@@ -36,7 +36,7 @@ __name__ == "__main__":` block.
#
# .. code-block:: bash
#
-# pip3 install --user psutil xgboost tornado
+# pip3 install --user psutil xgboost tornado cloudpickle
#
# To make TVM run faster in tuning, it is recommended to use cython
# as FFI of tvm. In the root directory of tvm, execute
diff --git a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
index 9846b89..410d180 100644
--- a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
+++ b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado mxnet requests \"Pillow<7\"\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of TVM. In the root directory of TVM, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install - [...]
+ "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado mxnet requests \"Pillow<7\" cloudpickle\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of TVM. In the root directory of TVM, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pi [...]
]
},
{
@@ -144,7 +144,7 @@
},
"outputs": [],
"source": [
- "def tune_and_evaluate(tuning_opt):\n\n if env.TARGET != \"sim\":\n # Get remote from fleet node\n remote = autotvm.measure.request_remote(\n env.TARGET, tracker_host, tracker_port, timeout=10000\n )\n # Reconfigure the JIT runtime and FPGA.\n vta.reconfig_runtime(remote)\n vta.program_fpga(remote, bitstream=None)\n else:\n # In simulation mode, host the RPC server locally.\n remote = rpc.LocalSession()\n\n [...]
+ "def tune_and_evaluate(tuning_opt):\n\n # Register VTA tuning tasks\n register_vta_tuning_tasks()\n\n # Perform task extraction on Relay program\n print(\"Extract tasks...\")\n relay_prog, params = compile_network(env, target, network, start_pack, stop_pack)\n mod = tvm.IRModule.from_expr(relay_prog)\n tasks = autotvm.task.extract_from_program(\n mod,\n params=params,\n ops=(relay.op.get(\"nn.conv2d\"),),\n target=target,\n [...]
]
},
{
diff --git a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
index 7f04424..273f0af 100644
--- a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
+++ b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
@@ -40,7 +40,7 @@ log file to get the best knob parameters.
#
# .. code-block:: bash
#
-# pip3 install --user psutil xgboost tornado mxnet requests "Pillow<7"
+# pip3 install --user psutil xgboost tornado mxnet requests "Pillow<7" cloudpickle
#
# To make TVM run faster during tuning, it is recommended to use cython
# as FFI of TVM. In the root directory of TVM, execute
@@ -340,18 +340,6 @@ def register_vta_tuning_tasks():
def tune_and_evaluate(tuning_opt):
- if env.TARGET != "sim":
- # Get remote from fleet node
- remote = autotvm.measure.request_remote(
- env.TARGET, tracker_host, tracker_port, timeout=10000
- )
- # Reconfigure the JIT runtime and FPGA.
- vta.reconfig_runtime(remote)
- vta.program_fpga(remote, bitstream=None)
- else:
- # In simulation mode, host the RPC server locally.
- remote = rpc.LocalSession()
-
# Register VTA tuning tasks
register_vta_tuning_tasks()
@@ -407,6 +395,19 @@ def tune_and_evaluate(tuning_opt):
print("Tuning...")
tune_tasks(tasks, **tuning_opt)
+ # evaluate with tuning history
+ if env.TARGET != "sim":
+ # Get remote from fleet node
+ remote = autotvm.measure.request_remote(
+ env.TARGET, tracker_host, tracker_port, timeout=10000
+ )
+ # Reconfigure the JIT runtime and FPGA.
+ vta.reconfig_runtime(remote)
+ vta.program_fpga(remote, bitstream=None)
+ else:
+ # In simulation mode, host the RPC server locally.
+ remote = rpc.LocalSession()
+
# compile kernels with history best records
with autotvm.tophub.context(target, extra_files=[log_file]):
# Compile network
@@ -425,9 +426,9 @@ def tune_and_evaluate(tuning_opt):
# Export library
print("Upload...")
temp = utils.tempdir()
- lib.save(temp.relpath("graphlib.o"))
- remote.upload(temp.relpath("graphlib.o"))
- lib = remote.load_module("graphlib.o")
+ lib.export_library(temp.relpath("graphlib.tar"))
+ remote.upload(temp.relpath("graphlib.tar"))
+ lib = remote.load_module("graphlib.tar")
# Generate the graph runtime
ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
diff --git a/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py b/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py
index 103ceb4..396bdb0 100644
--- a/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py
+++ b/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py
@@ -186,18 +186,24 @@ print(task.print_best(log_file, print_mode="cuda"))
# and resume the status of search policy and cost model with the log file.
# In the example below we resume the status and do more 5 trials.
-cost_model = auto_scheduler.XGBModel()
-cost_model.update_from_file(log_file)
-search_policy = auto_scheduler.SketchPolicy(
- task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
-)
-measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
-tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=5,
- runner=measure_ctx.runner,
- measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
-)
-task.tune(tune_option, search_policy=search_policy)
-# Kill the measurement process
-del measure_ctx
+def resume_search(task, log_file):
+ print("Resume search:")
+ cost_model = auto_scheduler.XGBModel()
+ cost_model.update_from_file(log_file)
+ search_policy = auto_scheduler.SketchPolicy(
+ task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
+ )
+ measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
+ tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=5,
+ runner=measure_ctx.runner,
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+ )
+ task.tune(tune_option, search_policy=search_policy)
+
+ # Kill the measurement process
+ del measure_ctx
+
+
+resume_search(task, log_file)
diff --git a/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py b/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
index 1557ea5..1b969bc 100644
--- a/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
+++ b/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
@@ -60,7 +60,11 @@ onnx_model = onnx.load(model_path)
######################################################################
# Load a test image
# ---------------------------------------------
-# A single cat dominates the examples!
+# A single cat dominates the examples! This model takes a single input image of size
+# 224x224 and outputs a scaled image that is 3x greater than the input along each
+# axis, a 672x672 image. Re-scale the cat image to fit this input shape then
+# convert to `YCbCr`. The super resolution model will then be applied to the
+# luminance (`Y`) channel.
from PIL import Image
img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
@@ -73,6 +77,14 @@ x = np.array(img_y)[np.newaxis, np.newaxis, :, :]
######################################################################
# Compile the model with relay
# ---------------------------------------------
+# Typically ONNX models mix model input values with parameter values, with
+# the input having the name `1`. This model dependent, and you should check
+# with the documentation for your model to determine the full input and
+# parameter name space.
+#
+# Passing in the shape dictionary to the `relay.frontend.from_onnx` method
+# tells relay which ONNX parameters are inputs, and which are parameters, and
+# provides a static definition of the input size.
target = "llvm"
input_name = "1"
@@ -91,7 +103,9 @@ tvm_output = intrp.evaluate()(tvm.nd.array(x.astype(dtype)), **params).asnumpy()
######################################################################
# Display results
# ---------------------------------------------
-# We put input and output image neck to neck
+# We put input and output image neck to neck. The luminance channel, `Y` is the output
+# from the model. The chroma channels `Cb` and `Cr` are resized to match with a simple
+# bicubic algorithm. The image is then recombined and converted back to `RGB`.
from matplotlib import pyplot as plt
out_y = Image.fromarray(np.uint8((tvm_output[0, 0]).clip(0, 255)), mode="L")
@@ -112,3 +126,8 @@ plt.show()
# into a static shapes at compile time. If this fails, there may still be dynamic
# operations in the model. Not all TVM kernels currently support dynamic shapes,
# please file an issue on discuss.tvm.apache.org if you hit an error with dynamic kernels.
+#
+# This particular model was build using an older version of ONNX. During the import
+# phase ONNX importer will run the ONNX verifier, which may throw a `Mismatched attribute type`
+# warning. Because TVM supports a number of different ONNX versions, the Relay model
+# will still be valid.
diff --git a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
index fe0f765..82a5712 100644
--- a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
+++ b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute:\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cython3\n\nNow return to python code. Import p [...]
+ "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado cloudpickle\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute:\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cython3\n\nNow return to python co [...]
]
},
{
@@ -133,7 +133,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Scale up measurement by using multiple devices\n----------------------------------------------\n\nIf you have multiple devices, you can use all of them for measurement.\nTVM uses the RPC Tracker to manage distributed devices.\nThe RPC Tracker is a centralized controller node. We can register all devices to\nthe tracker. For example, if we have 10 GPU cards, we can register all of them\nto the tracker, and run 10 measurements in parallel, accelerating the tuning process.\n\nTo st [...]
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Scale up measurement by using multiple devices\n----------------------------------------------\nIf you have multiple devices, you can use all of them for measurement.\nTVM uses the RPC Tracker to manage distributed devices.\nThe RPC Tracker is a centralized controller node. We can register all devices to\nthe tracker. For example, if we have 10 GPU cards, we can register all of them\nto the tracker, and run 10 measurements in parallel, accelerating the tuning process.\n\nTo star [...]
]
},
{
diff --git a/docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py b/docs/_downloads/78bebde8ea0f8558ac1a6fe12999f99f/tune_network_mali.py
similarity index 75%
copy from docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py
copy to docs/_downloads/78bebde8ea0f8558ac1a6fe12999f99f/tune_network_mali.py
index a491759..d3fefa7 100644
--- a/docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py
+++ b/docs/_downloads/78bebde8ea0f8558ac1a6fe12999f99f/tune_network_mali.py
@@ -15,13 +15,13 @@
# specific language governing permissions and limitations
# under the License.
"""
-Auto-scheduling a Neural Network for x86 CPU
-============================================
-**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_
+Auto-scheduling a Neural Network for mali GPU
+=============================================
+**Author**: `Zhao Wu <https://github.com/FrozenGene>`_
Auto-tuning for specific devices and workloads is critical for getting the
best performance. This is a tutorial on how to tune a whole neural
-network for x86 CPU with the auto-scheduler.
+network for mali GPU with the auto-scheduler.
To auto-tune a neural network, we partition the network into small subgraphs and
tune them independently. Each subgraph is treated as one search task.
@@ -50,6 +50,7 @@ import tvm
from tvm import relay, auto_scheduler
import tvm.relay.testing
from tvm.contrib import graph_runtime
+import os
#################################################################
# Define a Network
@@ -131,15 +132,30 @@ def get_network(name, batch_size, layout="NHWC", dtype="float32"):
# Define the neural network and compilation target.
-# If the target machine supports avx512 instructions, replace the
-# "llvm -mcpu=core-avx2" with "llvm -mcpu=skylake-avx512"
-network = "resnet-50"
+network = "mobilenet"
batch_size = 1
layout = "NHWC"
-target = tvm.target.Target("llvm -mcpu=core-avx2")
+# Set this to True if you use ndk tools for cross compiling
+use_ndk = True
+# Path to cross compiler
+os.environ["TVM_NDK_CC"] = "/usr/bin/aarch64-linux-gnu-g++"
+target_host = tvm.target.Target("llvm -mtriple=aarch64-linux-gnu")
+target = tvm.target.Target("opencl -device=mali")
dtype = "float32"
log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
+
+#################################################################
+# Start an RPC Tracker and Register Devices to the Tracker
+# --------------------------------------------------------
+# Please refer to the "Start RPC Tracker" and "Register Devices to RPC Tracker" setions
+# in this :ref:`tutorial <tutorials-autotvm-start-rpc-tracker>` to start an RPC tracker
+# and register devices to the tracker.
+
+# Replace this with the device key in your tracker
+device_key = "rk3399"
+
+
#################################################################
# Extract Search Tasks
# --------------------
@@ -154,16 +170,41 @@ log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
# Extract tasks from the network
print("Extract tasks...")
mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)
-tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
+tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target, target_host)
for idx, task in enumerate(tasks):
print("========== Task %d (workload key: %s) ==========" % (idx, task.workload_key))
print(task.compute_dag)
+######################################################################
+# .. note:: How to get the hardware parameters from remote device
+#
+# .. code-block:: python
+#
+# from tvm.auto_scheduler.utils import request_remote
+# remote = request_remote(device_key, "0.0.0.0", 9190)
+# ctx = remote.cl()
+# max_shared_memory_per_block = ctx.max_shared_memory_per_block
+# # There is no explicit local memory limition
+# # so we can use INT32_MAX to disalbe the check on local_memory.
+# max_local_memory_per_block = 2147483647 # INT32_MAX
+# max_threads_per_block = ctx.max_threads_per_block
+# max_vthread_extent = int(ctx.warp_size / 4) if int(ctx.warp_size / 4) > 1 else ctx.warp_size
+# warp_size = ctx.warp_size
+# hardware_params = auto_scheduler.HardwareParams(-1, 16, 64,
+# max_shared_memory_per_block, max_local_memory_per_block,
+# max_threads_per_block, max_vthread_extent, warp_size)
+#
+# Now you could pass it to search task and tune
+#
+# .. code-block:: python
+#
+# tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target, target_host, hardware_params)
+#
#################################################################
-# Begin Tuning
-# ------------
-# Now, we set some options for tuning and launch the search tasks
+# Tuning and Evaluate
+# -------------------
+# Now, we set some options for tuning, launch the search tasks and evaluate the end-to-end performance
#
# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.
# You can set it to a small number (e.g., 200) for a fast demonstrative run.
@@ -179,23 +220,60 @@ for idx, task in enumerate(tasks):
#
-def run_tuning():
+def tune_and_evaluate():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=200, # change this to 20000 to achieve the best performance
- runner=auto_scheduler.LocalRunner(repeat=10, enable_cpu_cache_flush=True),
+ builder=auto_scheduler.LocalBuilder(build_func="ndk" if use_ndk else "default"),
+ runner=auto_scheduler.RPCRunner(
+ device_key, host="0.0.0.0", port=9190, repeat=3, timeout=50
+ ),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
+ # Compile the whole network
+ print("Compile...")
+ with auto_scheduler.ApplyHistoryBest(log_file):
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.backend.use_auto_scheduler": True}
+ ):
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
+
+ # Create graph runtime
+ print("=============== Request Remote ===============")
+ from tvm.auto_scheduler.utils import request_remote
+
+ remote = request_remote(device_key, "0.0.0.0", 9190)
+ ctx = remote.cl()
+ from tvm.contrib import utils, ndk
+
+ temp = utils.tempdir()
+ filename = "deploy_lib.so"
+ path_lib = temp.relpath(filename)
+ lib.export_library(path_lib, ndk.create_shared)
+ remote.upload(path_lib)
+ loaded_lib = remote.load_module(filename)
+ module = graph_runtime.GraphModule(loaded_lib["default"](ctx))
+ data = (np.random.uniform(size=input_shape)).astype(dtype)
+ data_tvm = tvm.nd.array(data)
+ module.set_input("data", data_tvm)
+
+ # Evaluate
+ print("Evaluate inference time cost...")
+ ftimer = module.module.time_evaluator("run", ctx, repeat=3, min_repeat_ms=500)
+ prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
+ print(
+ "Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), np.std(prof_res))
+ )
-# We do not run the tuning in our webpage server since it takes too long.
-# Uncomment the following line to run it by yourself.
-# run_tuning()
+# We do not run the tuning in our webpage server since server doesn't have mali gpu.
+# Uncomment the following line to run it by yourself.
+# tune_and_evaluate()
######################################################################
# .. note:: Explain the printed information during tuning
@@ -265,33 +343,6 @@ def run_tuning():
# you should be able to do the compilation (the secion below).
#
-
-#################################################################
-# Compile and Evaluate
-# --------------------
-# After auto-tuning, we can compile the network with the best schedules we found.
-# All measurement records are dumped into the log file during auto-tuning,
-# so we can read the log file and load the best schedules.
-
-# Compile with the history best
-print("Compile...")
-with auto_scheduler.ApplyHistoryBest(log_file):
- with tvm.transform.PassContext(opt_level=3, config={"relay.backend.use_auto_scheduler": True}):
- lib = relay.build(mod, target=target, params=params)
-
-# Create graph runtime
-ctx = tvm.context(str(target), 0)
-module = graph_runtime.GraphModule(lib["default"](ctx))
-data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
-module.set_input("data", data_tvm)
-
-# Evaluate
-print("Evaluate inference time cost...")
-ftimer = module.module.time_evaluator("run", ctx, repeat=3, min_repeat_ms=500)
-prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
-print("Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), np.std(prof_res)))
-
-
#################################################################
# Other Tips
# ----------
@@ -304,8 +355,8 @@ print("Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), n
# add a new argument :code:`load_log_file` when creating the task scheduler
# in function :code:`run_tuning`. Say,
# :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
-# 4. If you have multiple target CPUs, you can use all of them for measurements to
-# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+# 4. If you have multiple target GPUs, you can use all of them for measurements to
+# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
# to learn how to use the RPC Tracker and RPC Server.
# To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
# with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py b/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py
index 9bc15ae..084f5ae 100644
--- a/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py
+++ b/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py
@@ -174,36 +174,17 @@ print(task.print_best(log_file))
# In the example below we resume the status and do more 5 trials.
-def resume_search(task, log_file_name):
+def resume_search(task, log_file):
+ print("Resume search:")
cost_model = auto_scheduler.XGBModel()
- cost_model.update_from_file(log_file_name)
+ cost_model.update_from_file(log_file)
search_policy = auto_scheduler.SketchPolicy(
- task,
- cost_model,
- init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file_name)],
+ task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
)
tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file_name)]
+ num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]
)
task.tune(tune_option, search_policy=search_policy)
-# resume_search(task, log_file)
-
-######################################################################
-# .. note::
-# We cannot run the line above because of the conflict between
-# python's multiprocessing and tvm's thread pool.
-# After running a tvm generated binary the python's multiprocessing library
-# will hang forever. You have to make sure that you don't run any tvm
-# generated binaries before calling auot-scheduler's search.
-# To run the function above, you should comment out all code in
-# "Check correctness and evaluate performance" section.
-#
-# You should be careful about this problem in your applications.
-# There are other workarounds for this problem.
-# For example, you can start a new thread/process (with the builtin python library
-# threading or multiprocessing) and run the tvm binaries in the new thread/process.
-# This provides an isolation and avoids the conflict in the main thread/process.
-# You can also use :any:`auto_scheduler.LocalRPCMeasureContext` for auto-scheduler,
-# as shown in the GPU tutorial (:ref:`auto-scheduler-conv-gpu`).
+resume_search(task, log_file)
diff --git a/docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb b/docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb
index e03fb03..99b970b 100644
--- a/docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb
+++ b/docs/_downloads/afa7f0ecb19178546f310a1dfa66281f/tune_network_x86.ipynb
@@ -119,7 +119,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
+ "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
]
}
],
diff --git a/docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py b/docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py
index a491759..7f96254 100644
--- a/docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py
+++ b/docs/_downloads/b3eb5454a38ef6a663c9e4a7a3e61896/tune_network_x86.py
@@ -305,7 +305,7 @@ print("Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), n
# in function :code:`run_tuning`. Say,
# :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
# 4. If you have multiple target CPUs, you can use all of them for measurements to
-# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
# to learn how to use the RPC Tracker and RPC Server.
# To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
# with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
index 317af5f..2b38923 100644
--- a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
+++ b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
@@ -49,7 +49,7 @@ __name__ == "__main__":` block.
#
# .. code-block:: bash
#
-# pip3 install --user psutil xgboost tornado
+# pip3 install --user psutil xgboost tornado cloudpickle
#
# To make TVM run faster during tuning, it is recommended to use cython
# as FFI of TVM. In the root directory of TVM, execute
@@ -148,7 +148,7 @@ def get_network(name, batch_size):
# INFO:RPCTracker:bind to 0.0.0.0:9190
#################################################################
-# Register devices to RPC Tracker
+# Register Devices to RPC Tracker
# -----------------------------------
# Now we can register our devices to the tracker. The first step is to
# build the TVM runtime for the ARM devices.
diff --git a/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb b/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb
index 807f070..725f881 100644
--- a/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb
+++ b/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb
@@ -177,7 +177,7 @@
},
"outputs": [],
"source": [
- "cost_model = auto_scheduler.XGBModel()\ncost_model.update_from_file(log_file)\nsearch_policy = auto_scheduler.SketchPolicy(\n task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]\n)\nmeasure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)\ntune_option = auto_scheduler.TuningOptions(\n num_measure_trials=5,\n runner=measure_ctx.runner,\n measure_callbacks=[auto_scheduler.RecordToFile(log_file)],\n)\ntask.tune(tune_opt [...]
+ "def resume_search(task, log_file):\n print(\"Resume search:\")\n cost_model = auto_scheduler.XGBModel()\n cost_model.update_from_file(log_file)\n search_policy = auto_scheduler.SketchPolicy(\n task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]\n )\n measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=5,\n runner=mea [...]
]
}
],
diff --git a/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb b/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
index 5b26c9e..ff7698e 100644
--- a/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
+++ b/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "TARGET = tvm.target.target.micro(\"host\")\n\nwith tvm.transform.PassContext(\n opt_level=3, config={\"tir.disable_vectorize\": True}, disabled_pass=[\"FuseOps\"]\n):\n graph, c_mod, c_params = relay.build(mod, target=TARGET, params=params)\n\n\n# %%\n# Running on simulated device\n# ----------------------------------------------\n#\n# First, compile a static microTVM runtime for the targeted device. In this case, the host simulated\n# device is used.\nworkspace = tvm.micr [...]
+ "TARGET = tvm.target.target.micro(\"host\")\n\nwith tvm.transform.PassContext(\n opt_level=3, config={\"tir.disable_vectorize\": True}, disabled_pass=[\"FuseOps\"]\n):\n graph, c_mod, c_params = relay.build(mod, target=TARGET, params=params)\n\n\n# %%\n# Running on simulated device\n# ----------------------------------------------\n#\n# First, compile a static microTVM runtime for the targeted device. In this case, the host simulated\n# device is used.\nworkspace = tvm.micr [...]
]
},
{
diff --git a/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb b/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb
index ab57869..3161dc9 100644
--- a/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb
+++ b/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb
@@ -119,7 +119,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
+ "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
]
}
],
diff --git a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
index 5e97273..859ac58 100644
--- a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
+++ b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
@@ -47,7 +47,7 @@ __name__ == "__main__":` block.
#
# .. code-block:: bash
#
-# pip3 install --user psutil xgboost tornado
+# pip3 install --user psutil xgboost tornado cloudpickle
#
# To make TVM run faster during tuning, it is recommended to use cython
# as FFI of tvm. In the root directory of tvm, execute
@@ -121,6 +121,9 @@ def get_network(name, batch_size):
#################################################################
+# .. _tutorials-autotvm-start-rpc-tracker:
+
+#################################################################
# Start RPC Tracker
# -----------------
# TVM uses RPC session to communicate with ARM boards.
@@ -147,7 +150,7 @@ def get_network(name, batch_size):
# INFO:RPCTracker:bind to 0.0.0.0:9190
#################################################################
-# Register devices to RPC Tracker
+# Register Devices to RPC Tracker
# -----------------------------------
# Now we can register our devices to the tracker. The first step is to
# build the TVM runtime for the ARM devices.
diff --git a/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb b/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
index 8953d2d..24b37aa 100644
--- a/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
+++ b/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
@@ -51,7 +51,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Load a test image\n---------------------------------------------\nA single cat dominates the examples!\n\n"
+ "Load a test image\n---------------------------------------------\nA single cat dominates the examples! This model takes a single input image of size\n224x224 and outputs a scaled image that is 3x greater than the input along each\naxis, a 672x672 image. Re-scale the cat image to fit this input shape then\nconvert to `YCbCr`. The super resolution model will then be applied to the\nluminance (`Y`) channel.\n\n"
]
},
{
@@ -69,7 +69,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Compile the model with relay\n---------------------------------------------\n\n"
+ "Compile the model with relay\n---------------------------------------------\nTypically ONNX models mix model input values with parameter values, with\nthe input having the name `1`. This model dependent, and you should check\nwith the documentation for your model to determine the full input and\nparameter name space.\n\nPassing in the shape dictionary to the `relay.frontend.from_onnx` method\ntells relay which ONNX parameters are inputs, and which are parameters, and\nprovides a [...]
]
},
{
@@ -105,7 +105,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Display results\n---------------------------------------------\nWe put input and output image neck to neck\n\n"
+ "Display results\n---------------------------------------------\nWe put input and output image neck to neck. The luminance channel, `Y` is the output\nfrom the model. The chroma channels `Cb` and `Cr` are resized to match with a simple\nbicubic algorithm. The image is then recombined and converted back to `RGB`.\n\n"
]
},
{
@@ -123,7 +123,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Notes\n---------------------------------------------\nBy default, ONNX defines models in terms of dynamic shapes. The ONNX importer\nretains that dynamism upon import, and the compiler attemps to convert the model\ninto a static shapes at compile time. If this fails, there may still be dynamic\noperations in the model. Not all TVM kernels currently support dynamic shapes,\nplease file an issue on discuss.tvm.apache.org if you hit an error with dynamic kernels.\n\n"
+ "Notes\n---------------------------------------------\nBy default, ONNX defines models in terms of dynamic shapes. The ONNX importer\nretains that dynamism upon import, and the compiler attemps to convert the model\ninto a static shapes at compile time. If this fails, there may still be dynamic\noperations in the model. Not all TVM kernels currently support dynamic shapes,\nplease file an issue on discuss.tvm.apache.org if you hit an error with dynamic kernels.\n\nThis particular [...]
]
}
],
diff --git a/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb b/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb
index 2640a03..63e191d 100644
--- a/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb
+++ b/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb
@@ -177,14 +177,7 @@
},
"outputs": [],
"source": [
- "def resume_search(task, log_file_name):\n cost_model = auto_scheduler.XGBModel()\n cost_model.update_from_file(log_file_name)\n search_policy = auto_scheduler.SketchPolicy(\n task,\n cost_model,\n init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file_name)],\n )\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file_name)]\n )\n task.tune(tune_op [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>We cannot run the line above because of the conflict between\n python's multiprocessing and tvm's thread pool.\n After running a tvm generated binary the python's multiprocessing library\n will hang forever. You have to make sure that you don't run any tvm\n generated binaries before calling auot-scheduler's search.\n To run the function above, you should comment out all code in\n \"Check correctness and evaluate performance\ [...]
+ "def resume_search(task, log_file):\n print(\"Resume search:\")\n cost_model = auto_scheduler.XGBModel()\n cost_model.update_from_file(log_file)\n search_policy = auto_scheduler.SketchPolicy(\n task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]\n )\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]\n )\n task.tune(tune_option, se [...]
]
}
],
diff --git a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
index 47a08d4..0e61aa9 100644
--- a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
+++ b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of TVM. In the root directory of TVM, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cy [...]
+ "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado cloudpickle\n\nTo make TVM run faster during tuning, it is recommended to use cython\nas FFI of TVM. In the root directory of TVM, execute\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user cython\n [...]
]
},
{
@@ -65,7 +65,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Register devices to RPC Tracker\n-----------------------------------\nNow we can register our devices to the tracker. The first step is to\nbuild the TVM runtime for the ARM devices.\n\n* For Linux:\n Follow this section `build-tvm-runtime-on-device` to build\n the TVM runtime on the device. Then register the device to tracker by\n\n .. code-block:: bash\n\n python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rk3399\n\n (replace :code:`[HOST_IP]` with the IP addr [...]
+ "Register Devices to RPC Tracker\n-----------------------------------\nNow we can register our devices to the tracker. The first step is to\nbuild the TVM runtime for the ARM devices.\n\n* For Linux:\n Follow this section `build-tvm-runtime-on-device` to build\n the TVM runtime on the device. Then register the device to tracker by\n\n .. code-block:: bash\n\n python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rk3399\n\n (replace :code:`[HOST_IP]` with the IP addr [...]
]
},
{
diff --git a/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py b/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
index 293f95c..7ec5506 100644
--- a/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
+++ b/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
@@ -184,6 +184,9 @@ micro_binary = tvm.micro.build_static_runtime(
c_mod,
lib_opts=opts["bin_opts"],
bin_opts=opts["bin_opts"],
+ # Use the microTVM memory manager. If, in your main.cc, you change TVMPlatformMemoryAllocate and
+ # TVMPlatformMemoryFree to use e.g. malloc() and free(), you can omit this extra library.
+ extra_libs=[os.path.join(tvm.micro.build.CRT_ROOT_DIR, "memory")],
)
diff --git a/docs/_images/sphx_glr_tune_network_mali_thumb.png b/docs/_images/sphx_glr_tune_network_mali_thumb.png
new file mode 100644
index 0000000..233f8e6
Binary files /dev/null and b/docs/_images/sphx_glr_tune_network_mali_thumb.png differ
diff --git a/docs/_sources/install/from_source.rst.txt b/docs/_sources/install/from_source.rst.txt
index 3cf0a78..f6be4e3 100644
--- a/docs/_sources/install/from_source.rst.txt
+++ b/docs/_sources/install/from_source.rst.txt
@@ -248,7 +248,7 @@ like ``virtualenv``.
.. code:: bash
- pip3 install --user tornado psutil xgboost
+ pip3 install --user tornado psutil xgboost cloudpickle
Install Contrib Libraries
diff --git a/docs/_sources/langref/relay_pattern.rst.txt b/docs/_sources/langref/relay_pattern.rst.txt
index 8b34b76..ff02e50 100644
--- a/docs/_sources/langref/relay_pattern.rst.txt
+++ b/docs/_sources/langref/relay_pattern.rst.txt
@@ -167,6 +167,19 @@ The next example is matching a pattern of batch_norm -> get(0) -> relu. Note tha
out = relay.nn.relu(tuple_get_item_node)
pat.match(out)
+If we have a pattern that crosses a function boundary, we might want to match the Function itself
+
+
+.. code-block:: python
+
+ def test_match_func():
+ x = relay.var("x")
+ y = relay.var("y")
+ wc1 = wildcard()
+ wc2 = wildcard()
+ func_pattern = FunctionPattern([wc1, wc2], wc1 + wc2)
+ assert func_pattern.match(relay.Function([x, y], x + y))
+
The next example is matching a constant node regarding its values. This is useful to check
if a specific parameter in a subgraph has been bound or not.
@@ -283,6 +296,7 @@ The high level design is to introduce a language of patterns for now we propose
| is_tuple_get_item(pattern, index = None)
| pattern1 `|` pattern2
| dominates(parent_pattern, path_pattern, child_pattern)
+ | FunctionPattern(params, body)
The above language then provides a matching interface with both can select sub-graphs as well as verify that the graph does match the pattern.
@@ -332,6 +346,11 @@ Domination
Match child pattern, find a match for the parent pattern, insuring that the child ultimately dominates the parrent (i.e., no nodes outside the pattern use outputs of the parent), and that ever node betwen the child and the pattern matches the path pattern.
+Function Pattern
+****************
+
+Match a Function with a body and parameters
+
Applications
============
diff --git a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
index cb40e53..85589b9 100644
--- a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
@@ -5,9 +5,10 @@
Computation times
=================
-**04:05.271** total execution time for **tutorials_auto_scheduler** files:
+**02:15.304** total execution time for **tutorials_auto_scheduler** files:
-- **01:50.410**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_matmul_x86.py` (``tune_matmul_x86.py``)
-- **01:34.433**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **00:23.367**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:17.061**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:56.863**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **00:45.779**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_matmul_x86.py` (``tune_matmul_x86.py``)
+- **00:17.821**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:12.409**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:02.431**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_mali.py` (``tune_network_mali.py``)
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
index ea80d89..2620c3f 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
@@ -220,482 +220,67 @@ cooperative fetching, unrolling and operator fusion.
kernel: Buffer(kernel_2: Pointer(float32), float32, [512, 512, 3, 3], []),
data: Buffer(data_2: Pointer(float32), float32, [1, 512, 7, 7], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
- attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 28;
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 112;
attr [compute_3: Pointer(float32)] "storage_scope" = "local";
- allocate(compute_3, float32, [14]);
+ allocate(compute_3, float32, [4]);
attr [pad_temp.shared: Pointer(float32)] "storage_scope" = "shared";
- allocate(pad_temp.shared, float32, [72]);
+ allocate(pad_temp.shared, float32, [54]);
attr [kernel.shared: Pointer(float32)] "storage_scope" = "shared";
- allocate(kernel.shared, float32, [3072]);
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
+ allocate(kernel.shared, float32, [576]);
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56 {
compute_3[0] = 0f32
- compute_3[1] = 0f32
compute_3[2] = 0f32
+ compute_3[1] = 0f32
compute_3[3] = 0f32
- compute_3[4] = 0f32
- compute_3[5] = 0f32
- compute_3[6] = 0f32
- compute_3[7] = 0f32
- compute_3[8] = 0f32
- compute_3[9] = 0f32
- compute_3[10] = 0f32
- compute_3[11] = 0f32
- compute_3[12] = 0f32
- compute_3[13] = 0f32
- for (rc.outer.outer: int32, 0, 64) {
- for (ry.outer.outer: int32, 0, 3) {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared[(threadIdx.x_1*4)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1*4), 9))) && (floormod((threadIdx.x_1*4), 9) < 8)), (float32*)data_2[((((((rc.outer.outer*392) + (floordiv((threadIdx.x_1*4), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1*4), 9)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared[((threadIdx.x_1*4) + 1)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 1), 9))) && (floormod(((threadIdx.x_1*4) + 1), 9) < 8)), (float32*)data_2[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 1), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 1), 9)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared[((threadIdx.x_1*4) + 2)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 2), 9))) && (floormod(((threadIdx.x_1*4) + 2), 9) < 8)), (float32*)data_2[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 2), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 2), 9)) - 8)], 0f32, dtype=float32)
- }
- if @tir.likely((threadIdx.x_1 < 18), dtype=bool) {
- pad_temp.shared[((threadIdx.x_1*4) + 3)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(((threadIdx.x_1*4) + 3), 9))) && (floormod(((threadIdx.x_1*4) + 3), 9) < 8)), (float32*)data_2[((((((rc.outer.outer*392) + (floordiv(((threadIdx.x_1*4) + 3), 9)*49)) + (ry.outer.outer*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(((threadIdx.x_1*4) + 3), 9)) - 8)], 0f32, dtype=float32)
- }
+ for (rc.outer.outer: int32, 0, 256) {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ if @tir.likely((threadIdx.x_1 < 54), dtype=bool) {
+ pad_temp.shared[threadIdx.x_1] = @tir.if_then_else(((((1 <= (floordiv(floormod(threadIdx.x_1, 27), 9) + floormod(blockIdx.x, 7))) && ((floordiv(floormod(threadIdx.x_1, 27), 9) + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[((((((rc.outer.outer*98) + (floordiv(threadIdx.x_1, 27)*49)) + (floordiv(floormod(threadIdx.x_1, 27), 9)*7)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x_1, 9)) - 8)], 0 [...]
+ }
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[threadIdx.x_2] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv(threadIdx.x_2, 18)*4608)) + (rc.outer.outer*18)) + floormod(threadIdx.x_2, 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 56)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 56), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 2), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 112)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 112), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 4), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 168)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 168), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 6), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 224)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 224), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 8), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 280)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 280), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 10), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 336)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 336), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 12), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 392)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 392), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 14), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 448)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 448), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 16), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 504)] = (float32*)kernel_2[(((((floordiv(blockIdx.x, 7)*147456) + (floordiv(threadIdx.x_2, 18)*4608)) + (rc.outer.outer*18)) + floormod(threadIdx.x_2, 18)) + 129024)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ if @tir.likely((threadIdx.x_2 < 16), dtype=bool) {
+ kernel.shared[(threadIdx.x_2 + 560)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 560), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 2), 18))]
+ }
+ for (rc.outer.inner: int32, 0, 2) {
+ for (rx.outer.inner: int32, 0, 3) {
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 288)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 3)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 291)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 6)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 294)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 18)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 306)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 21)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 309)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 24)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((((rc.outer.inner*27) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*36) + (rc.outer.inner*9)) + rx.outer.inner) + 312)]))
}
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[threadIdx.x_2] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 64)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 64), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 128)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 128), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 192)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 36864)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 256)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 256), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 320)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 320), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 384)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 73728)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 448)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 448), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 512)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 512), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 576)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 110592)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 640)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 640), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 704)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 704), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 768)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 147456)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 832)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 832), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 896)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 896), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 960)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 184320)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1024)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1024), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1088)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1088), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1152)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 221184)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1216)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1216), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1280)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1280), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1344)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 258048)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1408)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1408), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1472)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1472), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1536)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 294912)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1600)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1600), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1664)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1664), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1728)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 331776)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1792)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1792), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1856)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1856), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1920)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 368640)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 1984)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 1984), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2048)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2048), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2112)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 405504)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2176)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2176), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2240)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2240), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2304)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 442368)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2368)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2368), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2432)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2432), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2496)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 479232)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2560)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2560), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2624)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2624), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2688)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 516096)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2752)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2752), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2816)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2816), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2880)] = (float32*)kernel_2[(((((((floordiv(blockIdx.x, 7)*589824) + (floordiv(threadIdx.x_2, 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3)) + 552960)]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 2944)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 2944), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 16), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
- kernel.shared[(threadIdx.x_2 + 3008)] = (float32*)kernel_2[((((((floordiv(blockIdx.x, 7)*589824) + (floordiv((threadIdx.x_2 + 3008), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 8), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[0]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[9]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[1]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[(threadIdx.x*48)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*48) + 3)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[0]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[9]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[1]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*48) + 24)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*48) + 27)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[1]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*48) + 1)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*48) + 4)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[1]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*48) + 25)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*48) + 28)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[8]*(float32*)kernel.shared[((threadIdx.x*48) + 2)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[17]*(float32*)kernel.shared[((threadIdx.x*48) + 5)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[8]*(float32*)kernel.shared[((threadIdx.x*48) + 26)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[17]*(float32*)kernel.shared[((threadIdx.x*48) + 29)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[18]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[27]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[28]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*48) + 6)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*48) + 9)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[18]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[27]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[28]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*48) + 30)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*48) + 33)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[28]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[25]*(float32*)kernel.shared[((threadIdx.x*48) + 7)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*48) + 10)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[28]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[25]*(float32*)kernel.shared[((threadIdx.x*48) + 31)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*48) + 34)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[25]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[26]*(float32*)kernel.shared[((threadIdx.x*48) + 8)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[35]*(float32*)kernel.shared[((threadIdx.x*48) + 11)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[25]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[26]*(float32*)kernel.shared[((threadIdx.x*48) + 32)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[35]*(float32*)kernel.shared[((threadIdx.x*48) + 35)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[36]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[45]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*48) + 12)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*48) + 15)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[36]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[45]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*48) + 36)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*48) + 39)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*48) + 13)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[52]*(float32*)kernel.shared[((threadIdx.x*48) + 16)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*48) + 37)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[52]*(float32*)kernel.shared[((threadIdx.x*48) + 40)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[52]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[44]*(float32*)kernel.shared[((threadIdx.x*48) + 14)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[53]*(float32*)kernel.shared[((threadIdx.x*48) + 17)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[52]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[44]*(float32*)kernel.shared[((threadIdx.x*48) + 38)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[53]*(float32*)kernel.shared[((threadIdx.x*48) + 41)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[54]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[63]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[55]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[64]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[56]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[65]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[57]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[66]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[58]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[67]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[59]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[68]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[60]*(float32*)kernel.shared[((threadIdx.x*48) + 18)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[69]*(float32*)kernel.shared[((threadIdx.x*48) + 21)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[54]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[63]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[55]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[64]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[56]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[65]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[57]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[66]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[58]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[67]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[59]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[68]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[60]*(float32*)kernel.shared[((threadIdx.x*48) + 42)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[69]*(float32*)kernel.shared[((threadIdx.x*48) + 45)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[55]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[64]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[56]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[65]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[57]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[66]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[58]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[67]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[59]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[68]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[60]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[69]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[61]*(float32*)kernel.shared[((threadIdx.x*48) + 19)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[70]*(float32*)kernel.shared[((threadIdx.x*48) + 22)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[55]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[64]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[56]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[65]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[57]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[66]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[58]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[67]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[59]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[68]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[60]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[69]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[61]*(float32*)kernel.shared[((threadIdx.x*48) + 43)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[70]*(float32*)kernel.shared[((threadIdx.x*48) + 46)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[56]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[65]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[57]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[66]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[58]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[67]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[59]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[68]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[60]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[69]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[61]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[70]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[62]*(float32*)kernel.shared[((threadIdx.x*48) + 20)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[71]*(float32*)kernel.shared[((threadIdx.x*48) + 23)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[56]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[65]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[57]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[66]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[58]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[67]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[59]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[68]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[60]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[69]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[61]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[70]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[62]*(float32*)kernel.shared[((threadIdx.x*48) + 44)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[71]*(float32*)kernel.shared[((threadIdx.x*48) + 47)]))
}
}
for (i1.inner: int32, 0, 2) {
- for (i3.inner: int32, 0, 7) {
- compute_2[(((((floordiv(blockIdx.x, 7)*6272) + (threadIdx.x*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + i3.inner)] = max(((float32*)compute_3[((i1.inner*7) + i3.inner)] + (float32*)bias_2[(((floordiv(blockIdx.x, 7)*128) + (threadIdx.x*2)) + i1.inner)]), 0f32)
- }
+ compute_2[(((((floordiv(blockIdx.x, 7)*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x, 7))] = max(((float32*)compute_3[i1.inner] + (float32*)bias_2[(((floordiv(blockIdx.x, 7)*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
+ compute_2[((((((floordiv(blockIdx.x, 7)*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(blockIdx.x, 7)*7)) + floormod(threadIdx.x, 7)) + 784)] = max(((float32*)compute_3[(i1.inner + 2)] + (float32*)bias_2[((((floordiv(blockIdx.x, 7)*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner) + 16)]), 0f32)
}
}
}
@@ -748,7 +333,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.417 ms
+ Execution time of this operator: 0.289 ms
@@ -794,19 +379,19 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
compute_nn_o_o_o_o, compute_nn_o_o_o_i = s[compute].split(compute_nn_o_o_o_i, factor=1)
compute_ff_o_i, compute_ff_i = s[compute].split(compute_ff, factor=1)
compute_ff_o_o_i, compute_ff_o_i = s[compute].split(compute_ff_o_i, factor=2)
- compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=64)
- compute_ff_o_o_o_o, compute_ff_o_o_o_i = s[compute].split(compute_ff_o_o_o_i, factor=1)
+ compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=8)
+ compute_ff_o_o_o_o, compute_ff_o_o_o_i = s[compute].split(compute_ff_o_o_o_i, factor=2)
compute_yy_o_i, compute_yy_i = s[compute].split(compute_yy, factor=1)
compute_yy_o_o_i, compute_yy_o_i = s[compute].split(compute_yy_o_i, factor=1)
compute_yy_o_o_o_i, compute_yy_o_o_i = s[compute].split(compute_yy_o_o_i, factor=1)
compute_yy_o_o_o_o, compute_yy_o_o_o_i = s[compute].split(compute_yy_o_o_o_i, factor=1)
compute_xx_o_i, compute_xx_i = s[compute].split(compute_xx, factor=1)
- compute_xx_o_o_i, compute_xx_o_i = s[compute].split(compute_xx_o_i, factor=7)
- compute_xx_o_o_o_i, compute_xx_o_o_i = s[compute].split(compute_xx_o_o_i, factor=1)
+ compute_xx_o_o_i, compute_xx_o_i = s[compute].split(compute_xx_o_i, factor=1)
+ compute_xx_o_o_o_i, compute_xx_o_o_i = s[compute].split(compute_xx_o_o_i, factor=7)
compute_xx_o_o_o_o, compute_xx_o_o_o_i = s[compute].split(compute_xx_o_o_o_i, factor=1)
- compute_rc_o_i, compute_rc_i = s[compute].split(compute_rc, factor=2)
- compute_rc_o_o, compute_rc_o_i = s[compute].split(compute_rc_o_i, factor=4)
- compute_ry_o_i, compute_ry_i = s[compute].split(compute_ry, factor=1)
+ compute_rc_o_i, compute_rc_i = s[compute].split(compute_rc, factor=1)
+ compute_rc_o_o, compute_rc_o_i = s[compute].split(compute_rc_o_i, factor=2)
+ compute_ry_o_i, compute_ry_i = s[compute].split(compute_ry, factor=3)
compute_ry_o_o, compute_ry_o_i = s[compute].split(compute_ry_o_i, factor=1)
compute_rx_o_i, compute_rx_i = s[compute].split(compute_rx, factor=1)
compute_rx_o_o, compute_rx_o_i = s[compute].split(compute_rx_o_i, factor=3)
@@ -815,13 +400,13 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=64)
- compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=8)
+ compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
- compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=7)
- compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+ compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
+ compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[compute].compute_at(s[compute], compute_i3_o_i)
@@ -841,441 +426,64 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
+ kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=56)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=4)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=64)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=56)
s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
- s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 512)
+ s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 16)
s[compute].pragma(compute_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
extern "C" __global__ void default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
- float compute1[14];
- __shared__ float pad_temp_shared[72];
- __shared__ float kernel_shared[3072];
+ float compute1[4];
+ __shared__ float pad_temp_shared[54];
+ __shared__ float kernel_shared[576];
compute1[(0)] = 0.000000e+00f;
- compute1[(1)] = 0.000000e+00f;
compute1[(2)] = 0.000000e+00f;
+ compute1[(1)] = 0.000000e+00f;
compute1[(3)] = 0.000000e+00f;
- compute1[(4)] = 0.000000e+00f;
- compute1[(5)] = 0.000000e+00f;
- compute1[(6)] = 0.000000e+00f;
- compute1[(7)] = 0.000000e+00f;
- compute1[(8)] = 0.000000e+00f;
- compute1[(9)] = 0.000000e+00f;
- compute1[(10)] = 0.000000e+00f;
- compute1[(11)] = 0.000000e+00f;
- compute1[(12)] = 0.000000e+00f;
- compute1[(13)] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 64; ++rc_outer_outer) {
- for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
- __syncthreads();
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[((((int)threadIdx.x) * 4))] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) * 4) % 9))) && (((((int)threadIdx.x) * 4) % 9) < 8)) ? data[(((((((rc_outer_outer * 392) + (((((int)threadIdx.x) * 4) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) * 4) % 9)) - 8))] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[(((((int)threadIdx.x) * 4) + 1))] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 1) % 9))) && ((((((int)threadIdx.x) * 4) + 1) % 9) < 8)) ? data[(((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 1) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 1) % 9)) - 8))] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[(((((int)threadIdx.x) * 4) + 2))] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 2) % 9))) && ((((((int)threadIdx.x) * 4) + 2) % 9) < 8)) ? data[(((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 2) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 2) % 9)) - 8))] : 0.000000e+00f);
- }
- if (((int)threadIdx.x) < 18) {
- pad_temp_shared[(((((int)threadIdx.x) * 4) + 3))] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((((int)threadIdx.x) * 4) + 3) % 9))) && ((((((int)threadIdx.x) * 4) + 3) % 9) < 8)) ? data[(((((((rc_outer_outer * 392) + ((((((int)threadIdx.x) * 4) + 3) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((((int)threadIdx.x) * 4) + 3) % 9)) - 8))] : 0.000000e+00f);
+ for (int rc_outer_outer = 0; rc_outer_outer < 256; ++rc_outer_outer) {
+ __syncthreads();
+ if (((int)threadIdx.x) < 54) {
+ pad_temp_shared[(((int)threadIdx.x))] = (((((1 <= (((((int)threadIdx.x) % 27) / 9) + (((int)blockIdx.x) % 7))) && ((((((int)threadIdx.x) % 27) / 9) + (((int)blockIdx.x) % 7)) < 8)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((((rc_outer_outer * 98) + ((((int)threadIdx.x) / 27) * 49)) + (((((int)threadIdx.x) % 27) / 9) * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 9)) - 8))] : 0.000000e+00f);
+ }
+ kernel_shared[(((int)threadIdx.x))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + ((((int)threadIdx.x) / 18) * 4608)) + (rc_outer_outer * 18)) + (((int)threadIdx.x) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 56))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 56) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 2) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 112))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 112) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 4) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 168))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 168) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 6) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 224))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 224) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 8) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 280))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 280) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 10) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 336))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 336) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 12) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 392))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 392) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 14) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 448))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 448) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 16) % 18)))];
+ kernel_shared[((((int)threadIdx.x) + 504))] = kernel[(((((((((int)blockIdx.x) / 7) * 147456) + ((((int)threadIdx.x) / 18) * 4608)) + (rc_outer_outer * 18)) + (((int)threadIdx.x) % 18)) + 129024))];
+ if (((int)threadIdx.x) < 16) {
+ kernel_shared[((((int)threadIdx.x) + 560))] = kernel[((((((((int)blockIdx.x) / 7) * 147456) + (((((int)threadIdx.x) + 560) / 18) * 4608)) + (rc_outer_outer * 18)) + (((int)threadIdx.x) + 2)))];
+ }
+ __syncthreads();
+ for (int rc_outer_inner = 0; rc_outer_inner < 2; ++rc_outer_inner) {
+ for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)))] * kernel_shared[(((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 288))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 9))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 3))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 9))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 291))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 18))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 6))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 18))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 294))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 18))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 306))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 9))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 21))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 9))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 309))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 18))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 24))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(((((rc_outer_inner * 27) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 18))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 36) + (rc_outer_inner * 9)) + rx_outer_inner) + 312))]));
}
- kernel_shared[(((int)threadIdx.x))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 64))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 64) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 128))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 128) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 192))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 36864))];
- kernel_shared[((((int)threadIdx.x) + 256))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 256) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 320))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 320) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 384))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 73728))];
- kernel_shared[((((int)threadIdx.x) + 448))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 448) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 512))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 512) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 576))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 110592))];
- kernel_shared[((((int)threadIdx.x) + 640))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 640) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 704))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 704) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 768))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 147456))];
- kernel_shared[((((int)threadIdx.x) + 832))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 832) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 896))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 896) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 960))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 184320))];
- kernel_shared[((((int)threadIdx.x) + 1024))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1024) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1088))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1088) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1152))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 221184))];
- kernel_shared[((((int)threadIdx.x) + 1216))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1216) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1280))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1280) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1344))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 258048))];
- kernel_shared[((((int)threadIdx.x) + 1408))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1408) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1472))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1472) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1536))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 294912))];
- kernel_shared[((((int)threadIdx.x) + 1600))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1600) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1664))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1664) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1728))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 331776))];
- kernel_shared[((((int)threadIdx.x) + 1792))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1792) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1856))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1856) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 1920))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 368640))];
- kernel_shared[((((int)threadIdx.x) + 1984))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 1984) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2048))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2048) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2112))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 405504))];
- kernel_shared[((((int)threadIdx.x) + 2176))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2176) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2240))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2240) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2304))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 442368))];
- kernel_shared[((((int)threadIdx.x) + 2368))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2368) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2432))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2432) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2496))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 479232))];
- kernel_shared[((((int)threadIdx.x) + 2560))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2560) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2624))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2624) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2688))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 516096))];
- kernel_shared[((((int)threadIdx.x) + 2752))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2752) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2816))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2816) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 2880))] = kernel[(((((((((((int)blockIdx.x) / 7) * 589824) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 552960))];
- kernel_shared[((((int)threadIdx.x) + 2944))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 2944) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)))];
- kernel_shared[((((int)threadIdx.x) + 3008))] = kernel[((((((((((int)blockIdx.x) / 7) * 589824) + (((((int)threadIdx.x) + 3008) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)))];
- __syncthreads();
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(0)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(9)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(1)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(10)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(2)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(11)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(3)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(12)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(4)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(13)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(5)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(14)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(6)] * kernel_shared[((((int)threadIdx.x) * 48))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(15)] * kernel_shared[(((((int)threadIdx.x) * 48) + 3))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(0)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(9)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(1)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(10)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(2)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(11)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(3)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(12)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(4)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(13)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(5)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(14)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(6)] * kernel_shared[(((((int)threadIdx.x) * 48) + 24))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(15)] * kernel_shared[(((((int)threadIdx.x) * 48) + 27))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(1)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(10)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(2)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(11)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(3)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(12)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(4)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(13)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(5)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(14)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(6)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(15)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(7)] * kernel_shared[(((((int)threadIdx.x) * 48) + 1))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(16)] * kernel_shared[(((((int)threadIdx.x) * 48) + 4))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(1)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(10)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(2)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(11)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(3)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(12)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(4)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(13)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(5)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(14)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(6)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(15)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(7)] * kernel_shared[(((((int)threadIdx.x) * 48) + 25))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(16)] * kernel_shared[(((((int)threadIdx.x) * 48) + 28))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(2)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(11)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(3)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(12)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(4)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(13)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(5)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(14)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(6)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(15)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(7)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(16)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(8)] * kernel_shared[(((((int)threadIdx.x) * 48) + 2))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(17)] * kernel_shared[(((((int)threadIdx.x) * 48) + 5))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(2)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(11)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(3)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(12)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(4)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(13)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(5)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(14)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(6)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(15)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(7)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(16)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(8)] * kernel_shared[(((((int)threadIdx.x) * 48) + 26))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(17)] * kernel_shared[(((((int)threadIdx.x) * 48) + 29))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(18)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(27)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(19)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(28)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(20)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(29)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(21)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(30)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(22)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(31)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(23)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(32)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(24)] * kernel_shared[(((((int)threadIdx.x) * 48) + 6))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(33)] * kernel_shared[(((((int)threadIdx.x) * 48) + 9))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(18)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(27)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(19)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(28)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(20)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(29)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(21)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(30)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(22)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(31)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(23)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(32)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(24)] * kernel_shared[(((((int)threadIdx.x) * 48) + 30))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(33)] * kernel_shared[(((((int)threadIdx.x) * 48) + 33))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(19)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(28)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(20)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(29)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(21)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(30)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(22)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(31)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(23)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(32)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(24)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(33)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(25)] * kernel_shared[(((((int)threadIdx.x) * 48) + 7))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(34)] * kernel_shared[(((((int)threadIdx.x) * 48) + 10))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(19)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(28)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(20)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(29)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(21)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(30)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(22)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(31)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(23)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(32)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(24)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(33)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(25)] * kernel_shared[(((((int)threadIdx.x) * 48) + 31))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(34)] * kernel_shared[(((((int)threadIdx.x) * 48) + 34))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(20)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(29)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(21)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(30)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(22)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(31)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(23)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(32)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(24)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(33)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(25)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(34)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(26)] * kernel_shared[(((((int)threadIdx.x) * 48) + 8))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(35)] * kernel_shared[(((((int)threadIdx.x) * 48) + 11))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(20)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(29)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(21)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(30)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(22)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(31)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(23)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(32)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(24)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(33)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(25)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(34)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(26)] * kernel_shared[(((((int)threadIdx.x) * 48) + 32))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(35)] * kernel_shared[(((((int)threadIdx.x) * 48) + 35))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(36)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(45)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(37)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(46)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(38)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(47)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(39)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(48)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(40)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(49)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(41)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(50)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(42)] * kernel_shared[(((((int)threadIdx.x) * 48) + 12))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(51)] * kernel_shared[(((((int)threadIdx.x) * 48) + 15))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(36)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(45)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(37)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(46)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(38)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(47)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(39)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(48)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(40)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(49)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(41)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(50)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(42)] * kernel_shared[(((((int)threadIdx.x) * 48) + 36))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(51)] * kernel_shared[(((((int)threadIdx.x) * 48) + 39))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(37)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(46)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(38)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(47)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(39)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(48)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(40)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(49)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(41)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(50)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(42)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(51)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(43)] * kernel_shared[(((((int)threadIdx.x) * 48) + 13))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(52)] * kernel_shared[(((((int)threadIdx.x) * 48) + 16))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(37)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(46)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(38)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(47)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(39)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(48)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(40)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(49)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(41)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(50)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(42)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(51)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(43)] * kernel_shared[(((((int)threadIdx.x) * 48) + 37))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(52)] * kernel_shared[(((((int)threadIdx.x) * 48) + 40))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(38)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(47)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(39)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(48)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(40)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(49)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(41)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(50)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(42)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(51)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(43)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(52)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(44)] * kernel_shared[(((((int)threadIdx.x) * 48) + 14))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(53)] * kernel_shared[(((((int)threadIdx.x) * 48) + 17))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(38)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(47)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(39)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(48)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(40)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(49)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(41)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(50)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(42)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(51)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(43)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(52)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(44)] * kernel_shared[(((((int)threadIdx.x) * 48) + 38))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(53)] * kernel_shared[(((((int)threadIdx.x) * 48) + 41))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(54)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(63)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(55)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(64)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(56)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(65)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(57)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(66)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(58)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(67)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(59)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(68)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(60)] * kernel_shared[(((((int)threadIdx.x) * 48) + 18))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(69)] * kernel_shared[(((((int)threadIdx.x) * 48) + 21))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(54)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(63)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(55)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(64)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(56)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(65)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(57)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(66)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(58)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(67)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(59)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(68)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(60)] * kernel_shared[(((((int)threadIdx.x) * 48) + 42))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(69)] * kernel_shared[(((((int)threadIdx.x) * 48) + 45))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(55)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(64)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(56)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(65)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(57)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(66)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(58)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(67)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(59)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(68)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(60)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(69)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(61)] * kernel_shared[(((((int)threadIdx.x) * 48) + 19))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(70)] * kernel_shared[(((((int)threadIdx.x) * 48) + 22))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(55)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(64)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(56)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(65)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(57)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(66)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(58)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(67)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(59)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(68)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(60)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(69)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(61)] * kernel_shared[(((((int)threadIdx.x) * 48) + 43))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(70)] * kernel_shared[(((((int)threadIdx.x) * 48) + 46))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(56)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(65)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(57)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[(66)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(58)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[(67)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(59)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[(68)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(60)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[(69)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(61)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[(70)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(62)] * kernel_shared[(((((int)threadIdx.x) * 48) + 20))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[(71)] * kernel_shared[(((((int)threadIdx.x) * 48) + 23))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(56)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(7)] = (compute1[(7)] + (pad_temp_shared[(65)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(57)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(8)] = (compute1[(8)] + (pad_temp_shared[(66)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(58)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(9)] = (compute1[(9)] + (pad_temp_shared[(67)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(59)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(10)] = (compute1[(10)] + (pad_temp_shared[(68)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(60)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(11)] = (compute1[(11)] + (pad_temp_shared[(69)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(61)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(12)] = (compute1[(12)] + (pad_temp_shared[(70)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(62)] * kernel_shared[(((((int)threadIdx.x) * 48) + 44))]));
- compute1[(13)] = (compute1[(13)] + (pad_temp_shared[(71)] * kernel_shared[(((((int)threadIdx.x) * 48) + 47))]));
}
}
for (int i1_inner = 0; i1_inner < 2; ++i1_inner) {
- for (int i3_inner = 0; i3_inner < 7; ++i3_inner) {
- compute[(((((((((int)blockIdx.x) / 7) * 6272) + (((int)threadIdx.x) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + i3_inner))] = max((compute1[(((i1_inner * 7) + i3_inner))] + bias[(((((((int)blockIdx.x) / 7) * 128) + (((int)threadIdx.x) * 2)) + i1_inner))]), 0.000000e+00f);
- }
+ compute[(((((((((int)blockIdx.x) / 7) * 1568) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 7)))] = max((compute1[(i1_inner)] + bias[(((((((int)blockIdx.x) / 7) * 32) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner))]), 0.000000e+00f);
+ compute[((((((((((int)blockIdx.x) / 7) * 1568) + ((((int)threadIdx.x) / 7) * 98)) + (i1_inner * 49)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) % 7)) + 784))] = max((compute1[((i1_inner + 2))] + bias[((((((((int)blockIdx.x) / 7) * 32) + ((((int)threadIdx.x) / 7) * 2)) + i1_inner) + 16))]), 0.000000e+00f);
}
}
@@ -1292,21 +500,27 @@ In the example below we resume the status and do more 5 trials.
.. code-block:: default
- cost_model = auto_scheduler.XGBModel()
- cost_model.update_from_file(log_file)
- search_policy = auto_scheduler.SketchPolicy(
- task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
- )
- measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
- tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=5,
- runner=measure_ctx.runner,
- measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
- )
- task.tune(tune_option, search_policy=search_policy)
- # Kill the measurement process
- del measure_ctx
+ def resume_search(task, log_file):
+ print("Resume search:")
+ cost_model = auto_scheduler.XGBModel()
+ cost_model.update_from_file(log_file)
+ search_policy = auto_scheduler.SketchPolicy(
+ task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
+ )
+ measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
+ tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=5,
+ runner=measure_ctx.runner,
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+ )
+ task.tune(tune_option, search_policy=search_policy)
+
+ # Kill the measurement process
+ del measure_ctx
+
+
+ resume_search(task, log_file)
@@ -1317,17 +531,13 @@ In the example below we resume the status and do more 5 trials.
.. code-block:: none
+ Resume search:
Get devices for measurement successfully!
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 34.433 seconds)
-
-
.. _sphx_glr_download_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py:
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
index 814fe56..865775b 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
@@ -169,7 +169,7 @@ file and apply it.
.. code-block:: none
- *T*T*T*T*T*T*T*T*T*T
+
@@ -198,8 +198,8 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
primfn(A_1: handle, B_1: handle, C_1: handle, out_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {out: Buffer(out_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C, out_1: out} {
attr [auto_scheduler_layout_transform: Pointer(float32)] "storage_scope" = "global";
@@ -279,7 +279,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 22.426 ms
+ Execution time of this operator: 16.683 ms
@@ -347,49 +347,34 @@ In the example below we resume the status and do more 5 trials.
- def resume_search(task, log_file_name):
+ def resume_search(task, log_file):
+ print("Resume search:")
cost_model = auto_scheduler.XGBModel()
- cost_model.update_from_file(log_file_name)
+ cost_model.update_from_file(log_file)
search_policy = auto_scheduler.SketchPolicy(
- task,
- cost_model,
- init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file_name)],
+ task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
)
tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file_name)]
+ num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]
)
task.tune(tune_option, search_policy=search_policy)
- # resume_search(task, log_file)
+ resume_search(task, log_file)
+.. rst-class:: sphx-glr-script-out
+ Out:
+
+ .. code-block:: none
+
+ Resume search:
+ *T
-.. note::
- We cannot run the line above because of the conflict between
- python's multiprocessing and tvm's thread pool.
- After running a tvm generated binary the python's multiprocessing library
- will hang forever. You have to make sure that you don't run any tvm
- generated binaries before calling auot-scheduler's search.
- To run the function above, you should comment out all code in
- "Check correctness and evaluate performance" section.
-
- You should be careful about this problem in your applications.
- There are other workarounds for this problem.
- For example, you can start a new thread/process (with the builtin python library
- threading or multiprocessing) and run the tvm binaries in the new thread/process.
- This provides an isolation and avoids the conflict in the main thread/process.
- You can also use :any:`auto_scheduler.LocalRPCMeasureContext` for auto-scheduler,
- as shown in the GPU tutorial (:ref:`auto-scheduler-conv-gpu`).
-
-
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 50.410 seconds)
.. _sphx_glr_download_tutorials_auto_scheduler_tune_matmul_x86.py:
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
index 2110410..afab490 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
@@ -614,7 +614,7 @@ so we can read the log file and load the best schedules.
Compile...
Evaluate inference time cost...
- Mean inference time (std dev): 3.28 ms (0.01 ms)
+ Mean inference time (std dev): 2.42 ms (0.00 ms)
@@ -630,7 +630,7 @@ Other Tips
in function :code:`run_tuning`. Say,
:code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
4. If you have multiple target GPUs, you can use all of them for measurements to
- parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+ parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt
similarity index 57%
copy from docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
copy to docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt
index 9965acf..8e73cb9 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt
@@ -1,19 +1,19 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_tutorials_auto_scheduler_tune_network_x86.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorials_auto_scheduler_tune_network_mali.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_tutorials_auto_scheduler_tune_network_x86.py:
+.. _sphx_glr_tutorials_auto_scheduler_tune_network_mali.py:
-Auto-scheduling a Neural Network for x86 CPU
-============================================
-**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_
+Auto-scheduling a Neural Network for mali GPU
+=============================================
+**Author**: `Zhao Wu <https://github.com/FrozenGene>`_
Auto-tuning for specific devices and workloads is critical for getting the
best performance. This is a tutorial on how to tune a whole neural
-network for x86 CPU with the auto-scheduler.
+network for mali GPU with the auto-scheduler.
To auto-tune a neural network, we partition the network into small subgraphs and
tune them independently. Each subgraph is treated as one search task.
@@ -45,6 +45,7 @@ __name__ == "__main__":` block.
from tvm import relay, auto_scheduler
import tvm.relay.testing
from tvm.contrib import graph_runtime
+ import os
@@ -135,12 +136,15 @@ You can use :ref:`ConvertLayout <convert-layout-usage>` pass to do the layout co
# Define the neural network and compilation target.
- # If the target machine supports avx512 instructions, replace the
- # "llvm -mcpu=core-avx2" with "llvm -mcpu=skylake-avx512"
- network = "resnet-50"
+ network = "mobilenet"
batch_size = 1
layout = "NHWC"
- target = tvm.target.Target("llvm -mcpu=core-avx2")
+ # Set this to True if you use ndk tools for cross compiling
+ use_ndk = True
+ # Path to cross compiler
+ os.environ["TVM_NDK_CC"] = "/usr/bin/aarch64-linux-gnu-g++"
+ target_host = tvm.target.Target("llvm -mtriple=aarch64-linux-gnu")
+ target = tvm.target.Target("opencl -device=mali")
dtype = "float32"
log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
@@ -150,6 +154,27 @@ You can use :ref:`ConvertLayout <convert-layout-usage>` pass to do the layout co
+
+Start an RPC Tracker and Register Devices to the Tracker
+--------------------------------------------------------
+Please refer to the "Start RPC Tracker" and "Register Devices to RPC Tracker" setions
+in this :ref:`tutorial <tutorials-autotvm-start-rpc-tracker>` to start an RPC tracker
+and register devices to the tracker.
+
+
+.. code-block:: default
+
+
+ # Replace this with the device key in your tracker
+ device_key = "rk3399"
+
+
+
+
+
+
+
+
Extract Search Tasks
--------------------
Next, we extract the search tasks and their weights from a network.
@@ -167,7 +192,7 @@ The task scheduler will just optimize this objective.
# Extract tasks from the network
print("Extract tasks...")
mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)
- tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
+ tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target, target_host)
for idx, task in enumerate(tasks):
print("========== Task %d (workload key: %s) ==========" % (idx, task.workload_key))
@@ -176,7 +201,6 @@ The task scheduler will just optimize this objective.
-
.. rst-class:: sphx-glr-script-out
Out:
@@ -184,6 +208,7 @@ The task scheduler will just optimize this objective.
.. code-block:: none
Extract tasks...
+
...2%, 0.01 MB, 43 KB/s, 0 seconds passed
...5%, 0.02 MB, 86 KB/s, 0 seconds passed
...7%, 0.02 MB, 128 KB/s, 0 seconds passed
...10%, 0.03 MB, 171 KB/s, 0 seconds passed
...12%, 0.04 MB, 214 KB/s, 0 seconds passed
...15%, 0.05 MB, 256 KB/s, 0 seconds passed
...18%, 0.05 MB, 299 KB/s, 0 seconds passed
...20%, 0.06 MB, 341 KB/s, 0 seconds passed
...23%, 0.07 MB, 383 KB/s, 0 seconds passed
...25%, 0.08 MB, 425 KB/s, 0 seconds passed
...28%, 0.09 MB, 467 KB/s, 0 seconds passed
...30%, 0.09 MB, 509 KB/s, 0 seconds passed
...33%, 0.10 MB, 550 KB/s, 0 seconds passed
...36%, 0.11 MB, 592 KB/s, 0 seconds passed
...38%, 0.12 MB, 634 KB/s, 0 seconds passed
...41%, 0.12 MB, 676 KB/s, 0 seconds passed
...43%, 0.13 MB, 717 KB/s, 0 seconds passed
...46%, 0.14 MB, 759 KB/s, 0 seconds passed
...48%, 0.15 MB, 800 KB/s, 0 seconds passed
...51%, 0.16 MB, 842 KB/s, 0 seconds passed
...54%, 0.16 MB, 883 KB/s, 0 seconds
passed
...56%, 0.17 MB, 924 KB/s, 0 seconds passed
...59%, 0.18 MB, 966 KB/s, 0 seconds passed
...61%, 0.19 MB, 1007 KB/s, 0 seconds passed
...64%, 0.20 MB, 1048 KB/s, 0 seconds passed
...66%, 0.20 MB, 1089 KB/s, 0 seconds passed
...69%, 0.21 MB, 1128 KB/s, 0 seconds passed
...72%, 0.22 MB, 1169 KB/s, 0 seconds passed
...74%, 0.23 MB, 1210 KB/s, 0 seconds passed
...77%, 0.23 MB, 1251 KB/s, 0 seconds passed
...79%, 0.24 MB, 1292 KB/s, 0 seconds passed
...82%, 0.25 MB, 1333 KB/s, 0 seconds passed
...84%, 0.26 MB, 1372 KB/s, 0 seconds passed
...87%, 0.27 MB, 1413 KB/s, 0 seconds passed
...90%, 0.27 MB, 1453 KB/s, 0 seconds passed
...92%, 0.28 MB, 1494 KB/s, 0 seconds passed
...95%, 0.29 MB, 1534 KB/s, 0 seconds passed
...97%, 0.30 MB, 1575 KB/s, 0 seconds passed
...100%, 0.30 MB, 1614 KB/s, 0 seconds passed
========== Task 0 (workload key: ["b32ed43fb351136894c322ee49097a1a"]) ==========
placeholder = PLACEHOLDER [1, 1000]
T_softmax_maxelem(i0) max= placeholder[i0, k]
@@ -191,252 +216,222 @@ The task scheduler will just optimize this objective.
T_softmax_expsum(i0) += T_softmax_exp[i0, k]
T_softmax_norm(i0, i1) = (T_softmax_exp[i0, i1]/T_softmax_expsum[i0])
- ========== Task 1 (workload key: ["6129df1a3d5f6326c8393a8d17160199"]) ==========
- placeholder = PLACEHOLDER [1, 2048]
- placeholder = PLACEHOLDER [1000, 2048]
- compute(z, y, x) += (placeholder[z, ((k*16) + x)]*placeholder[y, ((k*16) + x)])
- compute(y, x) += compute[y, x, kk]
+ ========== Task 1 (workload key: ["35552028f3076f68df3063174e40b59f"]) ==========
+ placeholder = PLACEHOLDER [1, 1024]
+ placeholder = PLACEHOLDER [1000, 1024]
+ T_dense(i, j) += (placeholder[i, k]*placeholder[j, k])
placeholder = PLACEHOLDER [1000]
- T_add(ax0, ax1) = (compute[ax0, ax1] + placeholder[ax1])
+ T_add(ax0, ax1) = (T_dense[ax0, ax1] + placeholder[ax1])
- ========== Task 2 (workload key: ["36ee2798ed60bae3bcd1bb89a0285fe8"]) ==========
- placeholder = PLACEHOLDER [1, 7, 7, 2048]
+ ========== Task 2 (workload key: ["cf95f3a14294b5393f63b280d0ec0ab6"]) ==========
+ placeholder = PLACEHOLDER [1, 7, 7, 1024]
tensor(ax0, ax1, ax2, ax3) += placeholder[ax0, ((ax1*7) + rv0), ((ax2*7) + rv1), ax3]
tensor(ax0, ax1, ax2, ax3) = (tensor[ax0, ax1, ax2, ax3]/(float32((select((bool)1, ((ax1 + 1)*7), (((ax1 + 1)*7) + 1)) - (ax1*7)))*float32((select((bool)1, ((ax2 + 1)*7), (((ax2 + 1)*7) + 1)) - (ax2*7)))))
- ========== Task 3 (workload key: ["dcf6fcf5f56fa614bf9aef0c82382caf"]) ==========
- placeholder = PLACEHOLDER [1, 7, 7, 512]
+ ========== Task 3 (workload key: ["baa3a42d3cb6ab30685b0a7894b95da9"]) ==========
+ placeholder = PLACEHOLDER [1, 7, 7, 1024]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 512, 2048]
+ placeholder = PLACEHOLDER [1, 1, 1024, 1024]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 7, 7, 2048]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
- placeholder = PLACEHOLDER [1, 1, 1, 2048]
- T_multiply(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3]*placeholder[ax0, 0, 0, ax3])
- placeholder = PLACEHOLDER [1, 1, 1, 2048]
- T_add(ax0, ax1, ax2, ax3) = (T_multiply[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ placeholder = PLACEHOLDER [1, 1, 1, 1024]
+ T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 4 (workload key: ["7e3f0cf5a6dd80d36dab1a3dad92674a"]) ==========
- placeholder = PLACEHOLDER [1, 7, 7, 512]
+ ========== Task 4 (workload key: ["089861a00a7dfcc7196c2b6b5c807855"]) ==========
+ placeholder = PLACEHOLDER [1, 7, 7, 1024]
PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
- placeholder = PLACEHOLDER [3, 3, 512, 512]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 1, 1, 512]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ placeholder = PLACEHOLDER [3, 3, 1024, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])
+ placeholder = PLACEHOLDER [1, 1, 1, 1024]
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 5 (workload key: ["e0a9eb3795b531085e0ebb772e7e800c"]) ==========
- placeholder = PLACEHOLDER [1, 7, 7, 2048]
+ ========== Task 5 (workload key: ["e7ff95f121397b87a0ca12ef428aef59"]) ==========
+ placeholder = PLACEHOLDER [1, 7, 7, 512]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 2048, 512]
+ placeholder = PLACEHOLDER [1, 1, 512, 1024]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 1, 1, 512]
+ placeholder = PLACEHOLDER [1, 1, 1, 1024]
T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 6 (workload key: ["03614e726dc588d11887eb0953a77e53"]) ==========
- placeholder = PLACEHOLDER [1, 7, 7, 512]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 512, 2048]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 7, 7, 2048]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
+ ========== Task 6 (workload key: ["c3831fcb49bfdfc679be0bbfb987da82"]) ==========
+ placeholder = PLACEHOLDER [1, 14, 14, 512]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 512, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])
+ placeholder = PLACEHOLDER [1, 1, 1, 512]
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 7 (workload key: ["7657f886f5e9d8b5f19a5fd2c5b90d8d"]) ==========
- placeholder = PLACEHOLDER [1, 14, 14, 1024]
+ ========== Task 7 (workload key: ["33bb900cb60276282852b4b9c1346fe9"]) ==========
+ placeholder = PLACEHOLDER [1, 14, 14, 512]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 1024, 512]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
+ placeholder = PLACEHOLDER [1, 1, 512, 512]
+ Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 512]
T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 8 (workload key: ["7e09b626cf077cd419190fee02091dd6"]) ==========
+ ========== Task 8 (workload key: ["f2a48dd923600da67abb78b4895f8f7b"]) ==========
+ placeholder = PLACEHOLDER [1, 14, 14, 512]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 512, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])
+ placeholder = PLACEHOLDER [1, 1, 1, 512]
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
+
+ ========== Task 9 (workload key: ["f6906ccbe2258e70648ea15f3c037ca0"]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 256, 1024]
+ placeholder = PLACEHOLDER [1, 1, 256, 512]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 14, 14, 1024]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
- placeholder = PLACEHOLDER [1, 1, 1, 1024]
- T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ placeholder = PLACEHOLDER [1, 1, 1, 512]
+ T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 9 (workload key: ["95bf49cc8cf7a351e974b2359702aac0"]) ==========
- placeholder = PLACEHOLDER [1, 14, 14, 256]
- PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
- placeholder = PLACEHOLDER [3, 3, 256, 256]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
+ ========== Task 10 (workload key: ["381331b022e1b4ddc705aa66c2cb90c8"]) ==========
+ placeholder = PLACEHOLDER [1, 28, 28, 256]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 256, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])
placeholder = PLACEHOLDER [1, 1, 1, 256]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 10 (workload key: ["e043f834cc7f19597227e09dc7f59503"]) ==========
- placeholder = PLACEHOLDER [1, 14, 14, 1024]
+ ========== Task 11 (workload key: ["413e7c2a210f0fbf2fadeb2686aba8ee"]) ==========
+ placeholder = PLACEHOLDER [1, 28, 28, 256]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 1024, 256]
+ placeholder = PLACEHOLDER [1, 1, 256, 256]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 256]
T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 11 (workload key: ["cd7c4a374fb2bbc0d075c8cae638ad14"]) ==========
- placeholder = PLACEHOLDER [1, 14, 14, 256]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 256, 1024]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 14, 14, 1024]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
-
- ========== Task 12 (workload key: ["1dce2c5e4269b8a12dfc50cd4dd23ff1"]) ==========
- placeholder = PLACEHOLDER [1, 28, 28, 512]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 512, 256]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
+ ========== Task 12 (workload key: ["2b4a9c43c1bcbb5c68742378a4e72f74"]) ==========
+ placeholder = PLACEHOLDER [1, 28, 28, 256]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 256, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])
placeholder = PLACEHOLDER [1, 1, 1, 256]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 13 (workload key: ["d3b36ce001dc24d693facfbdae1979b4"]) ==========
+ ========== Task 13 (workload key: ["017340b550a0bda8bd8ec1933bc32756"]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 128, 512]
+ placeholder = PLACEHOLDER [1, 1, 128, 256]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 28, 28, 512]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
- placeholder = PLACEHOLDER [1, 1, 1, 512]
- T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ placeholder = PLACEHOLDER [1, 1, 1, 256]
+ T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 14 (workload key: ["0fb1dfcdb5b755e2dab290ed0129dcf2"]) ==========
- placeholder = PLACEHOLDER [1, 28, 28, 128]
- PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
- placeholder = PLACEHOLDER [3, 3, 128, 128]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
+ ========== Task 14 (workload key: ["539b0d6ae7b6e1610e29ae571b8b8c25"]) ==========
+ placeholder = PLACEHOLDER [1, 56, 56, 128]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 128, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])
placeholder = PLACEHOLDER [1, 1, 1, 128]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 15 (workload key: ["45acfc473c772458684f36a34549d8aa"]) ==========
- placeholder = PLACEHOLDER [1, 28, 28, 512]
+ ========== Task 15 (workload key: ["80b2e789f7bce126bde2176640ca76a4"]) ==========
+ placeholder = PLACEHOLDER [1, 56, 56, 128]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 512, 128]
+ placeholder = PLACEHOLDER [1, 1, 128, 128]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 128]
T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 16 (workload key: ["5e3ceb6e23ae8c351d5a1770d5fc6c7c"]) ==========
- placeholder = PLACEHOLDER [1, 28, 28, 128]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 128, 512]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 28, 28, 512]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
-
- ========== Task 17 (workload key: ["a085717fb3dcb046e5c4c2c04d3dc541"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 256]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 256, 128]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
+ ========== Task 16 (workload key: ["3dba2989a90e19861af284d74e40f5cd"]) ==========
+ placeholder = PLACEHOLDER [1, 56, 56, 128]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 128, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])
placeholder = PLACEHOLDER [1, 1, 1, 128]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 18 (workload key: ["691feef049c8693bbe91bd5e7c9cdf34"]) ==========
+ ========== Task 17 (workload key: ["3b9a17584b6afa25229ef34c6f417660"]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 64, 256]
+ placeholder = PLACEHOLDER [1, 1, 64, 128]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 56, 56, 256]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
- placeholder = PLACEHOLDER [1, 1, 1, 256]
- T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
- T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
-
- ========== Task 19 (workload key: ["a9e632e5167afb60fbe29e7aeef1d152"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 64]
- PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
- placeholder = PLACEHOLDER [3, 3, 64, 64]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 1, 1, 64]
+ placeholder = PLACEHOLDER [1, 1, 1, 128]
T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 20 (workload key: ["b51e06c1131d4cded40d1b215f722a4e"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 256]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 256, 64]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
+ ========== Task 18 (workload key: ["a4553ca6a00b6c8adb555bcde25d95c4"]) ==========
+ placeholder = PLACEHOLDER [1, 112, 112, 64]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 113)) && (i2 >= 1)) && (i2 < 113)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 64, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])
placeholder = PLACEHOLDER [1, 1, 1, 64]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 21 (workload key: ["8fcee68a4342c38248a827f1c6c69177"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 64]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 64, 256]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 56, 56, 256]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
-
- ========== Task 22 (workload key: ["8dd7d81db440763f622f03fdc99e6d46"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 64]
+ ========== Task 19 (workload key: ["63672689bf8f678a0abe0854828cbd3b"]) ==========
+ placeholder = PLACEHOLDER [1, 112, 112, 32]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 64, 64]
+ placeholder = PLACEHOLDER [1, 1, 32, 64]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 64]
T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 23 (workload key: ["ba2026d923536b75e9b4faed89287d5f"]) ==========
- placeholder = PLACEHOLDER [1, 112, 112, 64]
- pad_temp(ax0, ax1, ax2, ax3) = tir.if_then_else(((((ax1 >= 1) && (ax1 < 113)) && (ax2 >= 1)) && (ax2 < 113)), placeholder[ax0, (ax1 - 1), (ax2 - 1), ax3], -3.40282e+38f)
- tensor(ax0, ax1, ax2, ax3) max= pad_temp[ax0, ((ax1*2) + dh), ((ax2*2) + dw), ax3]
- placeholder = PLACEHOLDER [1, 1, 1, 64]
- T_add(ax0, ax1, ax2, ax3) = (tensor[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ ========== Task 20 (workload key: ["1ceacb63c63eaa3da881bff2858acdbf"]) ==========
+ placeholder = PLACEHOLDER [1, 112, 112, 32]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 113)) && (i2 >= 1)) && (i2 < 113)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 32, 1]
+ DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])
+ placeholder = PLACEHOLDER [1, 1, 1, 32]
+ T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 24 (workload key: ["a0eb8d6048282a4a0986cc2ccf14eaa2"]) ==========
+ ========== Task 21 (workload key: ["2c2147047fd6dafd3d66d75165843f67"]) ==========
placeholder = PLACEHOLDER [1, 224, 224, 3]
- PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 3) && (i1 < 227)) && (i2 >= 3)) && (i2 < 227)), placeholder[i0, (i1 - 3), (i2 - 3), i3], 0f)
- placeholder = PLACEHOLDER [7, 7, 3, 64]
+ PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 225)) && (i2 >= 1)) && (i2 < 225)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
+ placeholder = PLACEHOLDER [3, 3, 3, 32]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
- placeholder = PLACEHOLDER [1, 1, 1, 64]
- T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+ placeholder = PLACEHOLDER [1, 112, 1, 1]
+ T_multiply(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3]*placeholder[ax0, ax1, 0, 0])
+ placeholder = PLACEHOLDER [1, 112, 1, 1]
+ T_add(ax0, ax1, ax2, ax3) = (T_multiply[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, 0, 0])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
- ========== Task 25 (workload key: ["45b4de07687dee43ee1cbde9f516b2bf"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 64]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 64, 256]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
- ========== Task 26 (workload key: ["b2010aa63c95dedf1f58f3fe8bc78634"]) ==========
- placeholder = PLACEHOLDER [1, 56, 56, 256]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 256, 512]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
- ========== Task 27 (workload key: ["4d7e646d99bfa3cea8245bd7100369cb"]) ==========
- placeholder = PLACEHOLDER [1, 28, 28, 512]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 512, 1024]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
- ========== Task 28 (workload key: ["537c8642716948c33a6eaaabc86b159d"]) ==========
- placeholder = PLACEHOLDER [1, 14, 14, 1024]
- PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
- placeholder = PLACEHOLDER [1, 1, 1024, 2048]
- Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
+.. note:: How to get the hardware parameters from remote device
+
+ .. code-block:: python
+ from tvm.auto_scheduler.utils import request_remote
+ remote = request_remote(device_key, "0.0.0.0", 9190)
+ ctx = remote.cl()
+ max_shared_memory_per_block = ctx.max_shared_memory_per_block
+ # There is no explicit local memory limition
+ # so we can use INT32_MAX to disalbe the check on local_memory.
+ max_local_memory_per_block = 2147483647 # INT32_MAX
+ max_threads_per_block = ctx.max_threads_per_block
+ max_vthread_extent = int(ctx.warp_size / 4) if int(ctx.warp_size / 4) > 1 else ctx.warp_size
+ warp_size = ctx.warp_size
+ hardware_params = auto_scheduler.HardwareParams(-1, 16, 64,
+ max_shared_memory_per_block, max_local_memory_per_block,
+ max_threads_per_block, max_vthread_extent, warp_size)
+ Now you could pass it to search task and tune
+ .. code-block:: python
-Begin Tuning
-------------
-Now, we set some options for tuning and launch the search tasks
+ tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target, target_host, hardware_params)
+
+
+Tuning and Evaluate
+-------------------
+Now, we set some options for tuning, launch the search tasks and evaluate the end-to-end performance
* :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.
You can set it to a small number (e.g., 200) for a fast demonstrative run.
@@ -456,23 +451,60 @@ Now, we set some options for tuning and launch the search tasks
- def run_tuning():
+ def tune_and_evaluate():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=200, # change this to 20000 to achieve the best performance
- runner=auto_scheduler.LocalRunner(repeat=10, enable_cpu_cache_flush=True),
+ builder=auto_scheduler.LocalBuilder(build_func="ndk" if use_ndk else "default"),
+ runner=auto_scheduler.RPCRunner(
+ device_key, host="0.0.0.0", port=9190, repeat=3, timeout=50
+ ),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
+ # Compile the whole network
+ print("Compile...")
+ with auto_scheduler.ApplyHistoryBest(log_file):
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.backend.use_auto_scheduler": True}
+ ):
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
+
+ # Create graph runtime
+ print("=============== Request Remote ===============")
+ from tvm.auto_scheduler.utils import request_remote
+
+ remote = request_remote(device_key, "0.0.0.0", 9190)
+ ctx = remote.cl()
+ from tvm.contrib import utils, ndk
+
+ temp = utils.tempdir()
+ filename = "deploy_lib.so"
+ path_lib = temp.relpath(filename)
+ lib.export_library(path_lib, ndk.create_shared)
+ remote.upload(path_lib)
+ loaded_lib = remote.load_module(filename)
+ module = graph_runtime.GraphModule(loaded_lib["default"](ctx))
+ data = (np.random.uniform(size=input_shape)).astype(dtype)
+ data_tvm = tvm.nd.array(data)
+ module.set_input("data", data_tvm)
+
+ # Evaluate
+ print("Evaluate inference time cost...")
+ ftimer = module.module.time_evaluator("run", ctx, repeat=3, min_repeat_ms=500)
+ prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
+ print(
+ "Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), np.std(prof_res))
+ )
- # We do not run the tuning in our webpage server since it takes too long.
- # Uncomment the following line to run it by yourself.
- # run_tuning()
+ # We do not run the tuning in our webpage server since server doesn't have mali gpu.
+ # Uncomment the following line to run it by yourself.
+ # tune_and_evaluate()
@@ -546,51 +578,6 @@ Now, we set some options for tuning and launch the search tasks
you should be able to do the compilation (the secion below).
-Compile and Evaluate
---------------------
-After auto-tuning, we can compile the network with the best schedules we found.
-All measurement records are dumped into the log file during auto-tuning,
-so we can read the log file and load the best schedules.
-
-
-.. code-block:: default
-
-
- # Compile with the history best
- print("Compile...")
- with auto_scheduler.ApplyHistoryBest(log_file):
- with tvm.transform.PassContext(opt_level=3, config={"relay.backend.use_auto_scheduler": True}):
- lib = relay.build(mod, target=target, params=params)
-
- # Create graph runtime
- ctx = tvm.context(str(target), 0)
- module = graph_runtime.GraphModule(lib["default"](ctx))
- data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
- module.set_input("data", data_tvm)
-
- # Evaluate
- print("Evaluate inference time cost...")
- ftimer = module.module.time_evaluator("run", ctx, repeat=3, min_repeat_ms=500)
- prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
- print("Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), np.std(prof_res)))
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- Compile...
- Evaluate inference time cost...
- Mean inference time (std dev): 30.72 ms (0.09 ms)
-
-
-
Other Tips
----------
1. During the tuning, the auto-scheduler needs to compile many programs and
@@ -602,14 +589,14 @@ Other Tips
add a new argument :code:`load_log_file` when creating the task scheduler
in function :code:`run_tuning`. Say,
:code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
-4. If you have multiple target CPUs, you can use all of them for measurements to
- parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+4. If you have multiple target GPUs, you can use all of them for measurements to
+ parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
with :any:`auto_scheduler.RPCRunner`.
-.. _sphx_glr_download_tutorials_auto_scheduler_tune_network_x86.py:
+.. _sphx_glr_download_tutorials_auto_scheduler_tune_network_mali.py:
.. only :: html
@@ -621,13 +608,13 @@ Other Tips
.. container:: sphx-glr-download
- :download:`Download Python source code: tune_network_x86.py <tune_network_x86.py>`
+ :download:`Download Python source code: tune_network_mali.py <tune_network_mali.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: tune_network_x86.ipynb <tune_network_x86.ipynb>`
+ :download:`Download Jupyter notebook: tune_network_mali.ipynb <tune_network_mali.ipynb>`
.. only:: html
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
index 9965acf..38ef937 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
@@ -191,13 +191,12 @@ The task scheduler will just optimize this objective.
T_softmax_expsum(i0) += T_softmax_exp[i0, k]
T_softmax_norm(i0, i1) = (T_softmax_exp[i0, i1]/T_softmax_expsum[i0])
- ========== Task 1 (workload key: ["6129df1a3d5f6326c8393a8d17160199"]) ==========
+ ========== Task 1 (workload key: ["eca51cb8a8335304c6e670bdb115a9b7"]) ==========
placeholder = PLACEHOLDER [1, 2048]
placeholder = PLACEHOLDER [1000, 2048]
- compute(z, y, x) += (placeholder[z, ((k*16) + x)]*placeholder[y, ((k*16) + x)])
- compute(y, x) += compute[y, x, kk]
+ T_dense(i, j) += (placeholder[i, k]*placeholder[j, k])
placeholder = PLACEHOLDER [1000]
- T_add(ax0, ax1) = (compute[ax0, ax1] + placeholder[ax1])
+ T_add(ax0, ax1) = (T_dense[ax0, ax1] + placeholder[ax1])
========== Task 2 (workload key: ["36ee2798ed60bae3bcd1bb89a0285fe8"]) ==========
placeholder = PLACEHOLDER [1, 7, 7, 2048]
@@ -587,7 +586,7 @@ so we can read the log file and load the best schedules.
Compile...
Evaluate inference time cost...
- Mean inference time (std dev): 30.72 ms (0.09 ms)
+ Mean inference time (std dev): 32.65 ms (0.16 ms)
@@ -603,7 +602,7 @@ Other Tips
in function :code:`run_tuning`. Say,
:code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
4. If you have multiple target CPUs, you can use all of them for measurements to
- parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+ parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index e02f798..cf0a87b 100644
--- a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
Computation times
=================
-**00:59.256** total execution time for **tutorials_autotvm** files:
-
-- **00:30.101**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:28.464**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:00.203**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.173**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.158**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.157**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+**00:53.623** total execution time for **tutorials_autotvm** files:
+
+- **00:27.863**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:25.136**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.165**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.157**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.154**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.148**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index a2ea4e0..84f5c5c 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -26,7 +26,7 @@ To use autotvm package in tvm, we need to install some extra dependencies.
.. code-block:: bash
- pip3 install --user psutil xgboost tornado
+ pip3 install --user psutil xgboost tornado cloudpickle
To make TVM run faster in tuning, it is recommended to use cython
as FFI of tvm. In the root directory of tvm, execute
@@ -241,26 +241,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- No: 1 GFLOPS: 202.22/202.22 result: MeasureResult(costs=(0.0011447773775510204,), error_no=0, all_cost=1.6461100578308105, timestamp=1607225801.8082893) [('tile_f', [-1, 2, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4881186
- No: 2 GFLOPS: 0.00/202.22 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 3 GFLOPS: 180.05/202.22 result: MeasureResult(costs=(0.0012857949919354839,), error_no=0, all_cost=1.6061229705810547, timestamp=1607225803.2143183) [('tile_f', [-1, 4, 32, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3605182
- No: 4 GFLOPS: 0.00/202.22 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 5 GFLOPS: 0.00/202.22 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 6 GFLOPS: 0.00/202.22 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 7 GFLOPS: 0.00/202.22 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 8 GFLOPS: 1.76/202.22 result: MeasureResult(costs=(0.13166369325,), error_no=0, all_cost=3.334230899810791, timestamp=1607225806.5215275) [('tile_f', [-1, 2, 4, 64]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2716108
- No: 9 GFLOPS: 15.17/202.22 result: MeasureResult(costs=(0.015256713555555555,), error_no=0, all_cost=1.7692039012908936, timestamp=1607225809.5385563) [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1263092
- No: 10 GFLOPS: 228.21/228.21 result: MeasureResult(costs=(0.001014435909090909,), error_no=0, all_cost=1.5810611248016357, timestamp=1607225810.5847104) [('tile_f', [-1, 1, 32, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8921130
- No: 11 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 12 GFLOPS: 120.55/228.21 result: MeasureResult(costs=(0.0019204238301886794,), error_no=0, all_cost=1.3215758800506592, timestamp=1607225811.7186823) [('tile_f', [-1, 2, 32, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5036371
- No: 13 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 14 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 15 GFLOPS: 82.94/228.21 result: MeasureResult(costs=(0.0027913426315789476,), error_no=0, all_cost=1.4726567268371582, timestamp=1607225813.1304166) [('tile_f', [-1, 1, 1, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3582580
- No: 16 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 17 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 18 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 19 GFLOPS: 17.48/228.21 result: MeasureResult(costs=(0.013246279555555554,), error_no=0, all_cost=1.6513993740081787, timestamp=1607225816.3279276) [('tile_f', [-1, 8, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4107668
- No: 20 GFLOPS: 0.00/228.21 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc35dba3f31]\n [bt] (3) /workspace/build/libtvm.so(+0x6b1c67) [0x7fc35cf6ac67]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7fc35cf6768d]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 1 GFLOPS: 458.47/458.47 result: MeasureResult(costs=(0.0005049419962264151,), error_no=0, all_cost=1.5375077724456787, timestamp=1608956281.1430748) [('tile_f', [-1, 2, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4881186
+ No: 2 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 3 GFLOPS: 220.31/458.47 result: MeasureResult(costs=(0.0010508116209150325,), error_no=0, all_cost=1.6342782974243164, timestamp=1608956282.443572) [('tile_f', [-1, 4, 32, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3605182
+ No: 4 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 5 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 6 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 7 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 8 GFLOPS: 2.18/458.47 result: MeasureResult(costs=(0.10624710275,), error_no=0, all_cost=3.20185923576355, timestamp=1608956285.2293513) [('tile_f', [-1, 2, 4, 64]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2716108
+ No: 9 GFLOPS: 20.91/458.47 result: MeasureResult(costs=(0.0110708657,), error_no=0, all_cost=1.4918665885925293, timestamp=1608956286.1990628) [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1263092
+ No: 10 GFLOPS: 273.32/458.47 result: MeasureResult(costs=(0.0008469947421052631,), error_no=0, all_cost=1.6795475482940674, timestamp=1608956287.2305887) [('tile_f', [-1, 1, 32, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8921130
+ No: 11 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 12 GFLOPS: 148.81/458.47 result: MeasureResult(costs=(0.0015557267846153847,), error_no=0, all_cost=1.2524280548095703, timestamp=1608956288.236084) [('tile_f', [-1, 2, 32, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5036371
+ No: 13 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 14 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 15 GFLOPS: 108.29/458.47 result: MeasureResult(costs=(0.0021377897021276596,), error_no=0, all_cost=1.2944214344024658, timestamp=1608956289.4396245) [('tile_f', [-1, 1, 1, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3582580
+ No: 16 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 17 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 18 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 19 GFLOPS: 15.65/458.47 result: MeasureResult(costs=(0.014792380444444444,), error_no=0, all_cost=1.4618678092956543, timestamp=1608956292.6708024) [('tile_f', [-1, 8, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4107668
+ No: 20 GFLOPS: 0.00/458.47 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6bf2d7) [0x7f3501ef82d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3ed) [0x7f3501ef4cfd]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
@@ -312,8 +312,8 @@ and measure running time.
Best config:
- [('tile_f', [-1, 1, 32, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8921130
- Time cost of this operator: 0.001354
+ [('tile_f', [-1, 2, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4881186
+ Time cost of this operator: 0.000595
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
index 9ccc545..52b33b0 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
@@ -39,7 +39,7 @@ To use the autotvm package in tvm, we need to install some extra dependencies.
.. code-block:: bash
- pip3 install --user psutil xgboost tornado
+ pip3 install --user psutil xgboost tornado cloudpickle
To make TVM run faster during tuning, it is recommended to use cython
as FFI of TVM. In the root directory of TVM, execute
@@ -155,7 +155,7 @@ The expected output is
INFO:RPCTracker:bind to 0.0.0.0:9190
-Register devices to RPC Tracker
+Register Devices to RPC Tracker
-----------------------------------
Now we can register our devices to the tracker. The first step is to
build the TVM runtime for the ARM devices.
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
index 282e528..5ad864d 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
@@ -37,7 +37,7 @@ To use the autotvm package in tvm, we need to install some extra dependencies.
.. code-block:: bash
- pip3 install --user psutil xgboost tornado
+ pip3 install --user psutil xgboost tornado cloudpickle
To make TVM run faster during tuning, it is recommended to use cython
as FFI of tvm. In the root directory of tvm, execute:
@@ -342,10 +342,10 @@ As a reference baseline, the time cost of MXNet + TensorRT on resnet-18 is 1.30m
Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
+.. _tutorials-autotvm-scale-up-rpc-tracker:
+
Scale up measurement by using multiple devices
----------------------------------------------
-.. _tutorials-autotvm-rpc-tracker:
-
If you have multiple devices, you can use all of them for measurement.
TVM uses the RPC Tracker to manage distributed devices.
The RPC Tracker is a centralized controller node. We can register all devices to
@@ -366,8 +366,8 @@ The expected output is
INFO:RPCTracker:bind to 0.0.0.0:9190
-Then open another new terminal for the RPC server. We need to start one server
-for each dedicated device. We use a string key to distinguish the types of devices.
+Then open another new terminal for the RPC server. We need to start one dedicated server
+for each device. We use a string key to distinguish the types of devices.
You can pick a name you like.
(Note: For rocm backend, there are some internal errors with the compiler,
we need to add `--no-fork` to the argument list.)
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
index b82a4c7..bdfe404 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
@@ -37,7 +37,7 @@ To use the autotvm package in tvm, we need to install some extra dependencies.
.. code-block:: bash
- pip3 install --user psutil xgboost tornado
+ pip3 install --user psutil xgboost tornado cloudpickle
To make TVM run faster during tuning, it is recommended to use cython
as FFI of tvm. In the root directory of tvm, execute
@@ -129,6 +129,8 @@ We can also load models from MXNet, ONNX and TensorFlow.
+.. _tutorials-autotvm-start-rpc-tracker:
+
Start RPC Tracker
-----------------
TVM uses RPC session to communicate with ARM boards.
@@ -154,7 +156,7 @@ The expected output is
INFO:RPCTracker:bind to 0.0.0.0:9190
-Register devices to RPC Tracker
+Register Devices to RPC Tracker
-----------------------------------
Now we can register our devices to the tracker. The first step is to
build the TVM runtime for the ARM devices.
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index 5294a0a..9f786b7 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -31,7 +31,7 @@ This step (installing xgboost) can be skipped as it doesn't need XGBoost
.. code-block:: bash
- pip3 install --user psutil xgboost
+ pip3 install --user psutil xgboost cloudpickle
To make TVM run faster in tuning, it is recommended to use cython
as FFI of TVM. In the root directory of TVM, execute
@@ -369,16 +369,16 @@ used to get the best config later.
.. code-block:: none
Get devices for measurement successfully!
- No: 1 GFLOPS: 0.52/0.52 result: MeasureResult(costs=(0.5179643672,), error_no=0, all_cost=8.699557542800903, timestamp=1607225778.9184623) [('tile_y', [-1, 64]), ('tile_x', [-1, 1])],None,6
- No: 2 GFLOPS: 2.05/2.05 result: MeasureResult(costs=(0.1307110214,), error_no=0, all_cost=2.452157735824585, timestamp=1607225781.4836178) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
- No: 3 GFLOPS: 2.77/2.77 result: MeasureResult(costs=(0.0968108324,), error_no=0, all_cost=2.015434741973877, timestamp=1607225783.5040994) [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
- No: 4 GFLOPS: 7.71/7.71 result: MeasureResult(costs=(0.0348177938,), error_no=0, all_cost=0.9887301921844482, timestamp=1607225784.5313203) [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
- No: 5 GFLOPS: 13.46/13.46 result: MeasureResult(costs=(0.0199451586,), error_no=0, all_cost=0.7833263874053955, timestamp=1607225785.3334467) [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
- No: 6 GFLOPS: 11.91/13.46 result: MeasureResult(costs=(0.0225446656,), error_no=0, all_cost=0.7622959613800049, timestamp=1607225786.1802726) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
- No: 7 GFLOPS: 0.92/13.46 result: MeasureResult(costs=(0.2913359364,), error_no=0, all_cost=5.074311971664429, timestamp=1607225791.3119547) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
- No: 8 GFLOPS: 2.37/13.46 result: MeasureResult(costs=(0.1133100596,), error_no=0, all_cost=2.2167930603027344, timestamp=1607225793.595454) [('tile_y', [-1, 8]), ('tile_x', [-1, 4])],None,23
- No: 9 GFLOPS: 11.52/13.46 result: MeasureResult(costs=(0.0233022846,), error_no=0, all_cost=0.7279143333435059, timestamp=1607225795.1428313) [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
- No: 10 GFLOPS: 14.67/14.67 result: MeasureResult(costs=(0.0182990712,), error_no=0, all_cost=0.7626948356628418, timestamp=1607225795.9127738) [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
+ No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3502b3d7f1]\n [bt] (4) /workspace/build/libtvm.so(+0x1350b92) [0x7f3502b89b92]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x246) [0x7f3502b8c5c6]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::Call [...]
+ No: 2 GFLOPS: 5.20/5.20 result: MeasureResult(costs=(0.051649653799999994,), error_no=0, all_cost=1.605952262878418, timestamp=1608956261.9138966) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
+ No: 3 GFLOPS: 2.95/5.20 result: MeasureResult(costs=(0.0910745618,), error_no=0, all_cost=2.4689717292785645, timestamp=1608956263.7452652) [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
+ No: 4 GFLOPS: 16.65/16.65 result: MeasureResult(costs=(0.0161258324,), error_no=0, all_cost=0.7027585506439209, timestamp=1608956264.4698565) [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
+ No: 5 GFLOPS: 21.77/21.77 result: MeasureResult(costs=(0.012327853,), error_no=0, all_cost=0.9769332408905029, timestamp=1608956265.0378294) [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
+ No: 6 GFLOPS: 21.07/21.77 result: MeasureResult(costs=(0.0127378022,), error_no=0, all_cost=0.6480433940887451, timestamp=1608956265.6137564) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
+ No: 7 GFLOPS: 0.86/21.77 result: MeasureResult(costs=(0.3122983762,), error_no=0, all_cost=5.539120435714722, timestamp=1608956270.9430459) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
+ No: 8 GFLOPS: 1.52/21.77 result: MeasureResult(costs=(0.1765732038,), error_no=0, all_cost=3.407959222793579, timestamp=1608956274.1904879) [('tile_y', [-1, 8]), ('tile_x', [-1, 4])],None,23
+ No: 9 GFLOPS: 19.37/21.77 result: MeasureResult(costs=(0.0138569804,), error_no=0, all_cost=0.6956896781921387, timestamp=1608956274.776456) [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
+ No: 10 GFLOPS: 24.11/24.11 result: MeasureResult(costs=(0.0111336066,), error_no=0, all_cost=0.6651310920715332, timestamp=1608956275.326466) [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index 423392c..176cdd2 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:32.270** total execution time for **tutorials_dev** files:
+**00:26.149** total execution time for **tutorials_dev** files:
-- **00:31.677**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:00.396**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.198**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:25.646**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:00.332**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.171**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
index 754de4a..8bd3fbc 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -421,7 +421,7 @@ Execute on TVM
TVM prediction top-1: tiger cat
Evaluate inference time cost...
- Mean inference time (std dev): 5.42 ms (0.16 ms)
+ Mean inference time (std dev): 5.64 ms (0.06 ms)
diff --git a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
index d140675..6b57773 100644
--- a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
@@ -121,6 +121,9 @@ Load pre-trained maskrcnn from torchvision and do tracing
for s, s_orig in zip(new_size, original_size)
/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/roi_heads.py:372: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
return torch.tensor(M + 2 * padding).to(torch.float32) / torch.tensor(M).to(torch.float32)
+ /usr/local/lib/python3.6/dist-packages/torch/jit/_trace.py:966: TracerWarning: Output nr 2. of the traced function does not match the corresponding output of the Python function. Detailed error:
+ With rtol=1e-05 and atol=1e-05, found 1 element(s) (out of 2) whose difference(s) exceeded the margin of error (including 0 nan comparisons). The greatest difference was 2.456456422805786e-05 (0.11357344686985016 vs. 0.1135488823056221), which occurred at index 1.
+ _module_class,
@@ -247,7 +250,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 6.630 seconds)
+ **Total running time of the script:** ( 1 minutes 8.702 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
index 94b4250..e7c07f7 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -299,7 +299,7 @@ We should see identical labels printed.
.. code-block:: none
- PyTorch top3 labels: ['tiger cat', 'Egyptian cat', 'lynx, catamount']
+ PyTorch top3 labels: ['tiger cat', 'lynx, catamount', 'Egyptian cat']
TVM top3 labels: ['tiger cat', 'Egyptian cat', 'tabby, tabby cat']
@@ -323,7 +323,7 @@ output values are identical out of 1000 outputs from mobilenet v2.
.. code-block:: none
- 132 in 1000 raw floating outputs identical.
+ 156 in 1000 raw floating outputs identical.
@@ -350,7 +350,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 19.295305520000003
+ Elapsed average ms: 11.881062060000001
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index a05a4f0..9474092 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
@@ -368,7 +368,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 36.25781896
+ Elapsed average ms: 31.97628599
@@ -401,7 +401,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 35.942 seconds)
+ **Total running time of the script:** ( 1 minutes 59.235 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index 0c9638a..a61563a 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -195,7 +195,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 54.470 seconds)
+ **Total running time of the script:** ( 1 minutes 11.935 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
index a610cdd..1e03c39 100644
--- a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
@@ -138,6 +138,14 @@ now compile the graph
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('dense_small_batch.cuda', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+
Execute the portable graph on TVM
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index 9a20476..21ee522 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -77,7 +77,11 @@ we skip the pytorch model construction part, and download the saved onnx model
Load a test image
---------------------------------------------
-A single cat dominates the examples!
+A single cat dominates the examples! This model takes a single input image of size
+224x224 and outputs a scaled image that is 3x greater than the input along each
+axis, a 672x672 image. Re-scale the cat image to fit this input shape then
+convert to `YCbCr`. The super resolution model will then be applied to the
+luminance (`Y`) channel.
.. code-block:: default
@@ -107,6 +111,14 @@ A single cat dominates the examples!
Compile the model with relay
---------------------------------------------
+Typically ONNX models mix model input values with parameter values, with
+the input having the name `1`. This model dependent, and you should check
+with the documentation for your model to determine the full input and
+parameter name space.
+
+Passing in the shape dictionary to the `relay.frontend.from_onnx` method
+tells relay which ONNX parameters are inputs, and which are parameters, and
+provides a static definition of the input size.
.. code-block:: default
@@ -130,7 +142,7 @@ Compile the model with relay
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/onnx.py:2737: UserWarning: Mismatched attribute type in ' : kernel_shape'
+ /workspace/docs/../python/tvm/relay/frontend/onnx.py:3075: UserWarning: Mismatched attribute type in ' : kernel_shape'
==> Context: Bad node spec: input: "1" input: "2" output: "11" op_type: "Conv" attribute { name: "kernel_shape" ints: 5 ints: 5 } attribute { name: "strides" ints: 1 ints: 1 } attribute { name: "pads" ints: 2 ints: 2 ints: 2 ints: 2 } attribute { name: "dilations" ints: 1 ints: 1 } attribute { name: "group" i: 1 }
warnings.warn(str(e))
@@ -154,7 +166,9 @@ Execute on TVM
Display results
---------------------------------------------
-We put input and output image neck to neck
+We put input and output image neck to neck. The luminance channel, `Y` is the output
+from the model. The chroma channels `Cb` and `Cr` are resized to match with a simple
+bicubic algorithm. The image is then recombined and converted back to `RGB`.
.. code-block:: default
@@ -188,6 +202,11 @@ into a static shapes at compile time. If this fails, there may still be dynamic
operations in the model. Not all TVM kernels currently support dynamic shapes,
please file an issue on discuss.tvm.apache.org if you hit an error with dynamic kernels.
+This particular model was build using an older version of ONNX. During the import
+phase ONNX importer will run the ONNX verifier, which may throw a `Mismatched attribute type`
+warning. Because TVM supports a number of different ONNX versions, the Relay model
+will still be valid.
+
.. _sphx_glr_download_tutorials_frontend_from_onnx.py:
diff --git a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
index cad1d0a..808a4bf 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -155,7 +155,7 @@ Compile the graph to llvm target with given input specification.
.. code-block:: none
-
...47%, 0.01 MB, 39 KB/s, 0 seconds passed
...94%, 0.02 MB, 78 KB/s, 0 seconds passed
...100%, 0.02 MB, 117 KB/s, 0 seconds passed
+
...47%, 0.01 MB, 661 KB/s, 0 seconds passed
...94%, 0.02 MB, 1298 KB/s, 0 seconds passed
...100%, 0.02 MB, 1919 KB/s, 0 seconds passed
Cannot find config for target=llvm -keys=cpu -link-params=0, workload=('dense_nopack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index fac03e1..ad4ac5b 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -195,1971 +195,10 @@ Results:
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2894: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
+ /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2914: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
"will be used for operator %s." % node.name
/workspace/docs/../python/tvm/relay/frontend/tensorflow.py:745: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.expand_dims
- WARNING:root:Attribute T is ignored in relay.sym.expand_dims
- WARNING:root:Attribute Tdim is ignored in relay.sym.expand_dims
- WARNING:root:Attribute _node_name is ignored in relay.sym.expand_dims
- WARNING:root:Attribute _target_layout is ignored in relay.sym.expand_dims
- WARNING:root:Attribute half_pixel_centers is ignored in relay.sym.resize
- WARNING:root:Attribute T is ignored in relay.sym.resize
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.resize
- WARNING:root:Attribute _node_name is ignored in relay.sym.resize
- WARNING:root:Attribute _target_layout is ignored in relay.sym.resize
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
- WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
- WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.conv2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
- WARNING:root:Attribute T is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
- WARNING:root:Attribute T is ignored in relay.sym.copy
- WARNING:root:Attribute message is ignored in relay.sym.copy
- WARNING:root:Attribute _node_name is ignored in relay.sym.copy
- WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
- WARNING:root:Attribute T is ignored in relay.sym.relu
- WARNING:root:Attribute _node_name is ignored in relay.sym.relu
- WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
- WARNING:root:Attribute N is ignored in relay.sym.concatenate
- WARNING:root:Attribute T is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
- WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
- WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.reshape
- WARNING:root:Attribute Tshape is ignored in relay.sym.reshape
- WARNING:root:Attribute T is ignored in relay.sym.reshape
- WARNING:root:Attribute _node_name is ignored in relay.sym.reshape
- WARNING:root:Attribute _target_layout is ignored in relay.sym.reshape
- WARNING:root:Attribute transpose_a is ignored in relay.sym.dense
- WARNING:root:Attribute transpose_b is ignored in relay.sym.dense
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.dense
- WARNING:root:Attribute T is ignored in relay.sym.dense
- WARNING:root:Attribute _node_name is ignored in relay.sym.dense
- WARNING:root:Attribute _target_layout is ignored in relay.sym.dense
- WARNING:root:Attribute T is ignored in relay.sym.softmax
- WARNING:root:Attribute _output_shapes is ignored in relay.sym.softmax
- WARNING:root:Attribute _node_name is ignored in relay.sym.softmax
- WARNING:root:Attribute _target_layout is ignored in relay.sym.softmax
Tensorflow protobuf imported to relay frontend.
@@ -2184,6 +223,151 @@ Results:
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ Cannot find config for target=llvm -keys=cpu -link-params=0, workload=('dense_nopack.x86', ('TENSOR', (1, 2048), 'float32'), ('TENSOR', (1008, 2048), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+ conv2d NHWC layout is not optimized for x86 with autotvm.
+
Execute the portable graph on TVM
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index 8eae4e6..7edb568 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**10:34.564** total execution time for **tutorials_frontend** files:
+**07:19.891** total execution time for **tutorials_frontend** files:
-- **02:35.942**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **02:06.630**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **01:54.470**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:38.674**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:30.038**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:28.994**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:24.732**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:24.094**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:16.564**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:14.846**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:12.948**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:12.326**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:09.828**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:09.102**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:08.859**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:03.407**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.689**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
-- **00:01.228**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:00.194**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
+- **01:59.235**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:11.935**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:08.702**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **00:27.229**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:22.552**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:22.332**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:19.043**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:18.636**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:13.115**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:11.285**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:10.181**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:08.842**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:07.599**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:07.342**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:06.778**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:02.578**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.377**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:00.980**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:00.150**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
index f4add43..26cab3f 100644
--- a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.182e-07 secs/op
+ 1.944e-07 secs/op
diff --git a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
index 2965b65..a83cb40 100644
--- a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
@@ -224,7 +224,7 @@ in this example. Then the machine code will be generated as the module library.
.. code-block:: none
-
...1%, 0.01 MB, 39 KB/s, 0 seconds passed
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...35%, 0.16 MB, 774 KB/s, 0 seconds pa
ssed
...36%, 0.17 MB, 811 KB/s, 0 seconds passed
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...40%, 0.19 MB, 882 KB/s, 0 seconds passed
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...66%, 0.31 MB, 1425 KB/s, 0 seconds passed
...68%, 0.32 MB, 1460 KB/s, 0 seconds passed
...70%, 0.33 M
B, 1496 KB/s, 0 seconds passed
...71%, 0.34 MB, 1531 KB/s, 0 seconds passed
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...100%, 0.47 MB, 2118 KB/s, 0 seconds passed
+
...1%, 0.01 MB, 314 KB/s, 0 seconds passed
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...35%, 0.16 MB, 560
9 KB/s, 0 seconds passed
...36%, 0.17 MB, 5840 KB/s, 0 seconds passed
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passed
...70%, 0.33 MB, 10038 KB/s, 0 seconds passed
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...98%, 0.46 MB, 13171 KB/s, 0 seconds passed
...100%, 0.47 MB, 13318 KB/s, 0 seconds passed
Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=('dense_small_batch.cuda', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
index 7d657b5..d3a7e4e 100644
--- a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
Computation times
=================
-**00:16.931** total execution time for **tutorials_get_started** files:
+**00:13.165** total execution time for **tutorials_get_started** files:
-- **00:16.360**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.358**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.126**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.087**: :ref:`sphx_glr_tutorials_get_started_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:12.577**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.347**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.150**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.091**: :ref:`sphx_glr_tutorials_get_started_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
diff --git a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
index f86234d..8673a9a 100644
--- a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
@@ -325,7 +325,7 @@ The following code first performs the following steps:
.. code-block:: none
- ['myadd.tvm_meta.json', 'myadd.ptx', 'myadd.so', 'myadd.o']
+ ['myadd.tvm_meta.json', 'myadd.so', 'myadd.ptx', 'myadd.o']
diff --git a/docs/_sources/tutorials/index.rst.txt b/docs/_sources/tutorials/index.rst.txt
index 32a19ab..10baf1c 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -970,6 +970,26 @@ AutoScheduler : Template-free Auto Scheduling
.. only:: html
+ .. figure:: /tutorials/auto_scheduler/images/thumb/sphx_glr_tune_network_mali_thumb.png
+
+ :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_mali.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorials/auto_scheduler/tune_network_mali
+
+.. raw:: html
+
+ <div class="sphx-glr-thumbcontainer" tooltip="Auto-tuning for specific devices and workloads is critical for getting the best performance. Th...">
+
+.. only:: html
+
.. figure:: /tutorials/auto_scheduler/images/thumb/sphx_glr_tune_network_x86_thumb.png
:ref:`sphx_glr_tutorials_auto_scheduler_tune_network_x86.py`
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index ec5d465..877313c 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -449,13 +449,13 @@ of computation of `C`.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
- C: Buffer(C_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ B: Buffer(B_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
- B_2[(i*stride)] = ((float32*)A_2[(i*stride_2)] + 1f32)
- C_2[(i*stride_1)] = ((float32*)B_2[(i*stride)]*2f32)
+ B_2[(i*stride_1)] = ((float32*)A_2[(i*stride_2)] + 1f32)
+ C_2[(i*stride)] = ((float32*)B_2[(i*stride_1)]*2f32)
}
}
@@ -492,12 +492,12 @@ tensor is required.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
- B: Buffer(B_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ C: Buffer(C_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
- C_2[(i*stride)] = (((float32*)A_2[(i*stride_2)] + 1f32)*2f32)
+ C_2[(i*stride_1)] = (((float32*)A_2[(i*stride_2)] + 1f32)*2f32)
}
}
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index e5f878a..beb90e1 100644
--- a/docs/_sources/tutorials/language/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**00:04.449** total execution time for **tutorials_language** files:
+**00:03.582** total execution time for **tutorials_language** files:
-- **00:01.582**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.773**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.581**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.542**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.319**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.245**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.210**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
-- **00:00.198**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:01.247**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.669**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.457**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.455**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.238**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.181**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:00.173**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.162**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/tutorials/language/tensorize.rst.txt b/docs/_sources/tutorials/language/tensorize.rst.txt
index 1f1b6ab..4d05246 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -308,12 +308,12 @@ The importing needs to happen before the tensorized GEMV being executed.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
- B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpinr5hwkd/input0.cc'
- source_filename = "/tmp/tmpinr5hwkd/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp8hwfzwof/input0.cc'
+ source_filename = "/tmp/tmp8hwfzwof/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 9ddc391..dce05da 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -64,15 +64,15 @@ together in the next schedule procedure.
primfn(A0_1: handle, A1_1: handle, B.v0_1: handle, B.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B.v1: Buffer(B.v1_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- B.v0: Buffer(B.v0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+ buffers = {B.v0: Buffer(B.v0_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ B.v1: Buffer(B.v1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, B.v0_1: B.v0, B.v1_1: B.v1} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B.v0_2[((i*stride_2) + (j*stride_3))] = ((float32*)A0_2[((i*stride_6) + (j*stride_7))] + 2f32)
- B.v1_2[((i*stride) + (j*stride_1))] = ((float32*)A1_2[((i*stride_4) + (j*stride_5))]*3f32)
+ B.v0_2[((i*stride) + (j*stride_1))] = ((float32*)A0_2[((i*stride_6) + (j*stride_7))] + 2f32)
+ B.v1_2[((i*stride_4) + (j*stride_5))] = ((float32*)A1_2[((i*stride_2) + (j*stride_3))]*3f32)
}
}
}
@@ -135,17 +135,17 @@ with :py:func:`te.comm_reducer` as below:
primfn(idx_1: handle, val_1: handle, T.v0_1: handle, T.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {T.v1: Buffer(T.v1_2: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
+ buffers = {T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
val: Buffer(val_2: Pointer(int32), int32, [m, n: int32], [stride_1: int32, stride_2: int32], type="auto"),
- T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m], [stride_3: int32], type="auto"),
+ T.v1: Buffer(T.v1_2: Pointer(int32), int32, [m], [stride_3: int32], type="auto"),
idx: Buffer(idx_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {idx_1: idx, val_1: val, T.v0_1: T.v0, T.v1_1: T.v1} {
for (i: int32, 0, m) {
- T.v0_2[(i*stride_3)] = -1
- T.v1_2[(i*stride)] = -2147483648
+ T.v0_2[(i*stride)] = -1
+ T.v1_2[(i*stride_3)] = -2147483648
for (k: int32, 0, n) {
- T.v0_2[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v0_2[(i*stride_3)], (int32*)idx_2[((i*stride_4) + (k*stride_5))], dtype=int32)
- T.v1_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v1_2[(i*stride)], (int32*)val_2[((i*stride_1) + (k*stride_2))], dtype=int32)
+ T.v0_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride_3)]), (int32*)T.v0_2[(i*stride)], (int32*)idx_2[((i*stride_4) + (k*stride_5))], dtype=int32)
+ T.v1_2[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride_3)]), (int32*)T.v1_2[(i*stride_3)], (int32*)val_2[((i*stride_1) + (k*stride_2))], dtype=int32)
}
}
}
diff --git a/docs/_sources/tutorials/micro/micro_tflite.rst.txt b/docs/_sources/tutorials/micro/micro_tflite.rst.txt
index bb9567b..122dd85 100644
--- a/docs/_sources/tutorials/micro/micro_tflite.rst.txt
+++ b/docs/_sources/tutorials/micro/micro_tflite.rst.txt
@@ -231,6 +231,9 @@ file.
c_mod,
lib_opts=opts["bin_opts"],
bin_opts=opts["bin_opts"],
+ # Use the microTVM memory manager. If, in your main.cc, you change TVMPlatformMemoryAllocate and
+ # TVMPlatformMemoryFree to use e.g. malloc() and free(), you can omit this extra library.
+ extra_libs=[os.path.join(tvm.micro.build.CRT_ROOT_DIR, "memory")],
)
diff --git a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
index c04245a..d2fc72f 100644
--- a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:06.048** total execution time for **tutorials_micro** files:
+**00:06.152** total execution time for **tutorials_micro** files:
-- **00:05.849**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.199**: :ref:`sphx_glr_tutorials_micro_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:05.998**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.154**: :ref:`sphx_glr_tutorials_micro_micro_reference_vm.py` (``micro_reference_vm.py``)
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index 85d20dc..140945d 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -296,7 +296,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.197723 ms
+ Convolution: 19.711014 ms
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index 17e2062..c73eb43 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -624,7 +624,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 8.329637 ms
+ conv2d with tensor core: 6.665305 ms
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index a213af0..452fea6 100644
--- a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.006963
- Baseline: 3.516655
+ Numpy running time: 0.006749
+ Baseline: 5.843904
@@ -206,7 +206,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.284967
+ Opt1: 0.107367
@@ -300,7 +300,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.321595
+ Opt2: 0.112012
@@ -389,7 +389,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.111657
+ Opt3: 0.060845
@@ -413,8 +413,8 @@ Here is the generated IR after loop permutation.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x.outer: int32, 0, 32) {
@@ -499,7 +499,7 @@ the corresponding value from the packed array.
.. code-block:: none
- Opt4: 0.105409
+ Opt4: 0.062676
@@ -609,7 +609,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.098048
+ Opt5: 0.059428
@@ -725,7 +725,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.032347
+ Opt6: 0.016327
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index eefe5a5..6e5acde 100644
--- a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
Computation times
=================
-**00:27.648** total execution time for **tutorials_optimize** files:
+**00:26.965** total execution time for **tutorials_optimize** files:
-- **00:25.018**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.324**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.095**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.212**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:24.637**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.085**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.080**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.163**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index 445cb92..e9938a5 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -231,7 +231,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x190de3e10)), stage(b, placeholder(b, 0x1a4304890)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range( [...]
+ [stage(a, placeholder(a, 0x179c79ba0)), stage(b, placeholder(b, 0x178d329e0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range( [...]
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index a5d1531..a3de2c6 100644
--- a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:00.640** total execution time for **tutorials_topi** files:
+**00:00.576** total execution time for **tutorials_topi** files:
-- **00:00.640**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.576**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 7623620..1de3a8a 100644
--- a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:07.840** total execution time for **vta_tutorials_autotvm** files:
+**00:06.050** total execution time for **vta_tutorials_autotvm** files:
-- **00:07.840**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:06.050**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
index 5735b17..f9de15d 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -30,7 +30,7 @@ To use the autotvm package in tvm, we need to install some extra dependencies.
.. code-block:: bash
- pip3 install --user psutil xgboost tornado mxnet requests "Pillow<7"
+ pip3 install --user psutil xgboost tornado mxnet requests "Pillow<7" cloudpickle
To make TVM run faster during tuning, it is recommended to use cython
as FFI of TVM. In the root directory of TVM, execute
@@ -375,18 +375,6 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
def tune_and_evaluate(tuning_opt):
- if env.TARGET != "sim":
- # Get remote from fleet node
- remote = autotvm.measure.request_remote(
- env.TARGET, tracker_host, tracker_port, timeout=10000
- )
- # Reconfigure the JIT runtime and FPGA.
- vta.reconfig_runtime(remote)
- vta.program_fpga(remote, bitstream=None)
- else:
- # In simulation mode, host the RPC server locally.
- remote = rpc.LocalSession()
-
# Register VTA tuning tasks
register_vta_tuning_tasks()
@@ -442,6 +430,19 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
print("Tuning...")
tune_tasks(tasks, **tuning_opt)
+ # evaluate with tuning history
+ if env.TARGET != "sim":
+ # Get remote from fleet node
+ remote = autotvm.measure.request_remote(
+ env.TARGET, tracker_host, tracker_port, timeout=10000
+ )
+ # Reconfigure the JIT runtime and FPGA.
+ vta.reconfig_runtime(remote)
+ vta.program_fpga(remote, bitstream=None)
+ else:
+ # In simulation mode, host the RPC server locally.
+ remote = rpc.LocalSession()
+
# compile kernels with history best records
with autotvm.tophub.context(target, extra_files=[log_file]):
# Compile network
@@ -460,9 +461,9 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
# Export library
print("Upload...")
temp = utils.tempdir()
- lib.save(temp.relpath("graphlib.o"))
- remote.upload(temp.relpath("graphlib.o"))
- lib = remote.load_module("graphlib.o")
+ lib.export_library(temp.relpath("graphlib.tar"))
+ remote.upload(temp.relpath("graphlib.tar"))
+ lib = remote.load_module("graphlib.tar")
# Generate the graph runtime
ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
@@ -497,7 +498,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
.. code-block:: none
Extract tasks...
-
...1%, 0.01 MB, 22 KB/s, 0 seconds passed
...2%, 0.02 MB, 45 KB/s, 0 seconds passed
...3%, 0.02 MB, 68 KB/s, 0 seconds passed
...4%, 0.03 MB, 91 KB/s, 0 seconds passed
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ds passed
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...86%, 0.62 MB, 1699 KB/s, 0 seconds passed
...87%, 0.63 MB, 1720 KB/s, 0 seconds passed
...89%, 0.64 MB, 1741 KB/s, 0 seconds passed
...90%, 0.65 MB, 1762 KB
/s, 0 seconds passed
...91%, 0.66 MB, 1782 KB/s, 0 seconds passed
...92%, 0.66 MB, 1803 KB/s, 0 seconds passed
...93%, 0.67 MB, 1824 KB/s, 0 seconds passed
...94%, 0.68 MB, 1845 KB/s, 0 seconds passed
...95%, 0.69 MB, 1866 KB/s, 0 seconds passed
...96%, 0.70 MB, 1887 KB/s, 0 seconds passed
...97%, 0.70 MB, 1908 KB/s, 0 seconds passed
...98%, 0.71 MB, 1929 KB/s, 0 seconds passed
...99%, 0.72 MB, 1949 KB/s, 0 seconds passed
...100%, 0.73 MB, 1970 KB/s, 0 seconds passed
+
...1%, 0.01 MB, 32 KB/s, 0 seconds passed
...2%, 0.02 MB, 64 KB/s, 0 seconds passed
...3%, 0.02 MB, 97 KB/s, 0 seconds passed
...4%, 0.03 MB, 129 KB/s, 0 seconds passed
...5%, 0.04 MB, 161 KB/s, 0 seconds passed
...6%, 0.05 MB, 194 KB/s, 0 seconds passed
...7%, 0.05 MB, 226 KB/s, 0 seconds passed
...8%, 0.06 MB, 258 KB/s, 0 seconds passed
...9%, 0.07 MB, 290 KB/s, 0 seconds passed
...10%, 0.08 MB, 322 KB/s, 0 seconds passed
...11%, 0.09 MB, 354 KB/s, 0 seconds passed
...13%, 0.09 MB, 386 KB/s, 0 seconds passed
...14%, 0.10 MB, 418 KB/s, 0 seconds passed
...15%, 0.11 MB, 449 KB/s, 0 seconds passed
...16%, 0.12 MB, 481 KB/s, 0 seconds passed
...17%, 0.12 MB, 513 KB/s, 0 seconds passed
...18%, 0.13 MB, 544 KB/s, 0 seconds passed
...19%, 0.14 MB, 576 KB/s, 0 seconds passed
...20%, 0.15 MB, 607 KB/s, 0 seconds passed
...21%, 0.16 MB, 639 KB/s, 0 seconds passed
...22%, 0.16 MB, 670 KB/s, 0 seconds passed
...23%, 0.17 MB, 702 KB/s, 0 seconds passed
...24%, 0.18 MB, 733 KB/s, 0 seconds passed
...26%, 0.19 MB, 765 KB/s, 0 seconds passed
...27%, 0.20 MB, 796 KB/s, 0 seconds passed
...28%, 0.20 MB, 827 KB/s, 0 seconds passed
...29%, 0.21 MB, 859 KB/s, 0 seconds passed
...30%, 0.22 MB, 890 KB/s, 0 seconds passed
...31%, 0.23 MB, 921 KB/s, 0 seconds passed
...32%, 0.23 MB, 953 KB/s, 0 seconds passed
...33%, 0.24 MB, 983 KB/s, 0 seconds passed
...34%, 0.25 MB, 1014 KB/s, 0 seconds passed
...35%, 0.26 MB, 1045 KB/s, 0 seconds passed
...36%, 0.27 MB, 1076 KB/s, 0 seconds passed
...38%, 0.27 MB, 1105 KB/s, 0 seconds passed
...39%, 0.28 MB, 1136 KB/s, 0 seconds passed
...40%, 0.29 MB, 1167 KB/s, 0 seconds passed
...41%, 0.30 MB, 1198 KB/s, 0 seconds passed
...42%, 0.30 MB, 1229 KB/s, 0 seconds passed
...43%, 0.31 MB, 1260 KB/s, 0 seconds passed
...44%, 0.32 MB, 1290 KB/s, 0 seconds passed
...45%, 0.33 MB, 1321 K
B/s, 0 seconds passed
...46%, 0.34 MB, 1351 KB/s, 0 seconds passed
...47%, 0.34 MB, 1382 KB/s, 0 seconds passed
...48%, 0.35 MB, 1413 KB/s, 0 seconds passed
...49%, 0.36 MB, 1443 KB/s, 0 seconds passed
...51%, 0.37 MB, 1474 KB/s, 0 seconds passed
...52%, 0.38 MB, 1505 KB/s, 0 seconds passed
...53%, 0.38 MB, 1535 KB/s, 0 seconds passed
...54%, 0.39 MB, 1566 KB/s, 0 seconds passed
...55%, 0.40 MB, 1597 KB/s, 0 seconds passed
...56%, 0.41 MB, 1627 KB/s, 0 seconds passed
...57%, 0.41 MB, 1658 KB/s, 0 seconds passed
...58%, 0.42 MB, 1687 KB/s, 0 seconds passed
...59%, 0.43 MB, 1717 KB/s, 0 seconds passed
...60%, 0.44 MB, 1748 KB/s, 0 seconds passed
...61%, 0.45 MB, 1779 KB/s, 0 seconds passed
...63%, 0.45 MB, 1809 KB/s, 0 seconds passed
...64%, 0.46 MB, 1840 KB/s, 0 seconds passed
...65%, 0.47 MB, 1870 KB/s, 0 seconds passed
...66%, 0.48 MB, 1901 KB/s, 0 seconds passed
...67%, 0.48 MB, 1931 KB/s, 0 seconds pa
ssed
...68%, 0.49 MB, 1962 KB/s, 0 seconds passed
...69%, 0.50 MB, 1992 KB/s, 0 seconds passed
...70%, 0.51 MB, 2022 KB/s, 0 seconds passed
...71%, 0.52 MB, 2052 KB/s, 0 seconds passed
...72%, 0.52 MB, 2083 KB/s, 0 seconds passed
...73%, 0.53 MB, 2110 KB/s, 0 seconds passed
...74%, 0.54 MB, 2140 KB/s, 0 seconds passed
...76%, 0.55 MB, 2170 KB/s, 0 seconds passed
...77%, 0.55 MB, 2201 KB/s, 0 seconds passed
...78%, 0.56 MB, 2230 KB/s, 0 seconds passed
...79%, 0.57 MB, 2261 KB/s, 0 seconds passed
...80%, 0.58 MB, 2291 KB/s, 0 seconds passed
...81%, 0.59 MB, 2321 KB/s, 0 seconds passed
...82%, 0.59 MB, 2351 KB/s, 0 seconds passed
...83%, 0.60 MB, 2381 KB/s, 0 seconds passed
...84%, 0.61 MB, 2407 KB/s, 0 seconds passed
...85%, 0.62 MB, 2438 KB/s, 0 seconds passed
...86%, 0.62 MB, 2468 KB/s, 0 seconds passed
...87%, 0.63 MB, 2498 KB/s, 0 seconds passed
...89%, 0.64 MB, 2528 KB/s, 0 seconds passed
...90%,
0.65 MB, 2558 KB/s, 0 seconds passed
...91%, 0.66 MB, 2588 KB/s, 0 seconds passed
...92%, 0.66 MB, 2618 KB/s, 0 seconds passed
...93%, 0.67 MB, 2648 KB/s, 0 seconds passed
...94%, 0.68 MB, 2677 KB/s, 0 seconds passed
...95%, 0.69 MB, 2707 KB/s, 0 seconds passed
...96%, 0.70 MB, 2737 KB/s, 0 seconds passed
...97%, 0.70 MB, 2767 KB/s, 0 seconds passed
...98%, 0.71 MB, 2796 KB/s, 0 seconds passed
...99%, 0.72 MB, 2826 KB/s, 0 seconds passed
...100%, 0.73 MB, 2855 KB/s, 0 seconds passed
Extracted 10 conv2d tasks:
(1, 14, 14, 256, 512, 1, 1, 0, 0, 2, 2)
(1, 28, 28, 128, 256, 1, 1, 0, 0, 2, 2)
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index 602c00f..24a47fd 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -243,8 +243,8 @@ The compilation steps are:
.. code-block:: none
-
...12%, 0.01 MB, 42 KB/s, 0 seconds passed
...25%, 0.02 MB, 85 KB/s, 0 seconds passed
...38%, 0.02 MB, 127 KB/s, 0 seconds passed
...51%, 0.03 MB, 169 KB/s, 0 seconds passed
...64%, 0.04 MB, 206 KB/s, 0 seconds passed
...77%, 0.05 MB, 247 KB/s, 0 seconds passed
...89%, 0.05 MB, 288 KB/s, 0 seconds passed
...100%, 0.06 MB, 329 KB/s, 0 seconds passed
- resnet18_v1 inference graph built in 8.53s!
+
...12%, 0.01 MB, 42 KB/s, 0 seconds passed
...25%, 0.02 MB, 85 KB/s, 0 seconds passed
...38%, 0.02 MB, 128 KB/s, 0 seconds passed
...51%, 0.03 MB, 170 KB/s, 0 seconds passed
...64%, 0.04 MB, 213 KB/s, 0 seconds passed
...77%, 0.05 MB, 255 KB/s, 0 seconds passed
...89%, 0.05 MB, 297 KB/s, 0 seconds passed
...100%, 0.06 MB, 339 KB/s, 0 seconds passed
+ resnet18_v1 inference graph built in 6.50s!
diff --git a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
index 08db91b..a91794b 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:29.592** total execution time for **vta_tutorials_frontend** files:
+**00:24.644** total execution time for **vta_tutorials_frontend** files:
-- **00:29.592**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:24.644**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
index 4331499..aa5bdcc 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -535,8 +535,8 @@ by the VTA runtime JIT compiler.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
- C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [C_buf: Pointer(int32)] "storage_scope" = "local.acc_buffer";
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index 95b98e9..72105b5 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -219,14 +219,6 @@ manageable chunks.
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- <class 'int'>
-
Scheduling the Computation
@@ -456,8 +448,8 @@ below.
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
- kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], []),
+ buffers = {kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
attr [data_buf: Pointer(int8)] "storage_scope" = "global";
diff --git a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index dd5dcfc..5b75d4d 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -156,14 +156,6 @@ manageable chunks.
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- <class 'int'>
-
Scheduling the Computation
@@ -197,8 +189,8 @@ Those include:
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
- weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
+ buffers = {weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
attr [data_buf: Pointer(int8)] "storage_scope" = "global";
@@ -502,8 +494,8 @@ and mapping the shift, and clipping computation to the vector ALU.
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
- res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
attr [res_gem: Pointer(int32)] "storage_scope" = "local.acc_buffer";
diff --git a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
index c37a09d..bfad1bf 100644
--- a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:03.753** total execution time for **vta_tutorials_optimize** files:
+**00:02.971** total execution time for **vta_tutorials_optimize** files:
-- **00:03.215**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.537**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.539**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.432**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
index 1e2b328..612ccf3 100644
--- a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.975** total execution time for **vta_tutorials** files:
+**00:00.817** total execution time for **vta_tutorials** files:
-- **00:00.496**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.479**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.424**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.394**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/api/doxygen/annotated.html b/docs/api/doxygen/annotated.html
index 7654691..05f54d9 100644
--- a/docs/api/doxygen/annotated.html
+++ b/docs/api/doxygen/annotated.html
@@ -176,40 +176,42 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr id="row_1_1_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html" target="_self">ProgramMeasurerNode</a></td><td class="desc">Measurer that measures the time costs of tvm programs This class combines <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html" title="Managed refere [...]
<tr id="row_1_1_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html" target="_self">ProgramRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunnerNode.html" title="ProgramRunner that runs the built programs and measure the time cost. ">Prog [...]
<tr id="row_1_1_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunnerNode.html" target="_self">ProgramRunnerNode</a></td><td class="desc"><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html" title="Managed reference to ProgramRunnerNode. ">ProgramRunner</a> that runs the built programs and [...]
-<tr id="row_1_1_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModel.html" target="_self">PythonBasedModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" title="A wrapper for cost model defined by python code This class will cal [...]
-<tr id="row_1_1_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" target="_self">PythonBasedModelNode</a></td><td class="desc">A wrapper for cost model defined by python code This class will call functions defined in the python </td></tr>
-<tr id="row_1_1_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html" target="_self">RandomModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" title="The cost model returning random value for all predictions. ">RandomModelNode</a> < [...]
-<tr id="row_1_1_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" target="_self">RandomModelNode</a></td><td class="desc">The cost model returning random value for all predictions </td></tr>
-<tr id="row_1_1_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html" target="_self">RecordReader</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" title="Log reader to load step logs from a file. ">RecordReaderNode</a> </td></tr>
-<tr id="row_1_1_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" target="_self">RecordReaderNode</a></td><td class="desc">Log reader to load step logs from a file </td></tr>
-<tr id="row_1_1_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html" target="_self">RecordToFile</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" title="Callback for logging the input and results of measurements to file. ">RecordToF [...]
-<tr id="row_1_1_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" target="_self">RecordToFileNode</a></td><td class="desc">Callback for logging the input and results of measurements to file </td></tr>
-<tr id="row_1_1_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStep.html" target="_self">ReorderStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" title="Reorder step that corresponds to te::Stage::reorder. ">ReorderStepNode</a> </td></tr>
-<tr id="row_1_1_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" target="_self">ReorderStepNode</a></td><td class="desc">Reorder step that corresponds to <a class="el" href="classtvm_1_1te_1_1Stage.html#ad96cd240a92df9cafae89cdf2a7e302e" title="Reorder the iteration. ">te::Stage::reorder</a> </td></tr>
-<tr id="row_1_1_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html" target="_self">RfactorStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" title="Reduction factor step that corresponds to te::Schedule::rfactor. ">RfactorStepNode [...]
-<tr id="row_1_1_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" target="_self">RfactorStepNode</a></td><td class="desc">Reduction factor step that corresponds to <a class="el" href="classtvm_1_1te_1_1Schedule.html#a34ae85add41bbed0140726d024d08862" title="Factor a reduction axis in tensor's [...]
-<tr id="row_1_1_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" target="_self">RPCRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" title="RPCRunner that uses RPC call to measures the time cost of programs on remote devices. Or [...]
-<tr id="row_1_1_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" target="_self">RPCRunnerNode</a></td><td class="desc"><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" title="Managed reference to RPCRunnerNode. ">RPCRunner</a> that uses RPC call to measures the time cost of progr [...]
-<tr id="row_1_1_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallback.html" target="_self">SearchCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallbackNode.html" title="Callback function to be called by the search process. This interface allo [...]
-<tr id="row_1_1_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallbackNode.html" target="_self">SearchCallbackNode</a></td><td class="desc">Callback function to be called by the search process. This interface allows to do extra initializations before schedule search or extra check during/after the schedule s [...]
-<tr id="row_1_1_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" target="_self">SearchPolicy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" title="The base class of search policies. ">SearchPolicyNode</a> </td></tr>
-<tr id="row_1_1_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1auto__scheduler_1_1SearchPolicyKey.html" target="_self">SearchPolicyKey</a></td><td class="desc">Attribute keys of ops used for <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" title="Managed reference to SearchPolicyNode. ">SearchPolicy</a> </td></tr>
-<tr id="row_1_1_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" target="_self">SearchPolicyNode</a></td><td class="desc">The base class of search policies </td></tr>
-<tr id="row_1_1_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTask.html" target="_self">SearchTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" title="The computation information and hardware parameters for a specific schedule search ta [...]
-<tr id="row_1_1_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" target="_self">SearchTaskNode</a></td><td class="desc">The computation information and hardware parameters for a specific schedule search task </td></tr>
-<tr id="row_1_1_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html" target="_self">SplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" title="Split step that corresponds to te::Stage::split with additional support of multiple-leve [...]
-<tr id="row_1_1_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" target="_self">SplitStepNode</a></td><td class="desc">Split step that corresponds to <a class="el" href="classtvm_1_1te_1_1Stage.html#a5a7cd562be59b68a187ad97085a3425d" title="Split the parent by factor, generate. ">te::Stage::split< [...]
-<tr id="row_1_1_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html" target="_self">Stage</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" title="A op stage in the compute declaration. Similar to te::Stage in include/tvm/te/schedule.h. ">StageNod [...]
-<tr id="row_1_1_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1auto__scheduler_1_1StageAttributes.html" target="_self">StageAttributes</a></td><td class="desc">Stage-level attributes </td></tr>
-<tr id="row_1_1_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" target="_self">StageNode</a></td><td class="desc">A op stage in the compute declaration. Similar to <a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">te::Stage</a> in <code> [...]
-<tr id="row_1_1_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1State.html" target="_self">State</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" title="A state in the search process. It consists of the current loop structure and a list of transformatio [...]
-<tr id="row_1_1_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" target="_self">StateNode</a></td><td class="desc">A state in the search process. It consists of the current loop structure and a list of transformation steps used to construct it. Each <a class="el" href="classtvm_1_1auto__scheduler_1_1S [...]
-<tr id="row_1_1_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Step.html" target="_self">Step</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" title="The base class of transformation steps. Each step has its corresponding tvm.te schedule primitives..."> [...]
-<tr id="row_1_1_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" target="_self">StepNode</a></td><td class="desc">The base class of transformation steps. Each step has its corresponding <a class="el" href="namespacetvm_1_1te.html" title="Tensor expression language DSL. ">tvm.te</a> schedule primitives [...]
-<tr id="row_1_1_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html" target="_self">StorageAlignStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" title="Storage align step that corresponds to te::Stage::storage_align. "> [...]
-<tr id="row_1_1_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" target="_self">StorageAlignStepNode</a></td><td class="desc">Storage align step that corresponds to <a class="el" href="classtvm_1_1te_1_1Stage.html#aa73e3a269d84c3b4f0a1994371d67bab" title="Set alignment requirement for speci [...]
-<tr id="row_1_1_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html" target="_self">TuningOptions</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" title="Tuning and measurement options. ">TuningOptionsNode</a> </td></tr>
-<tr id="row_1_1_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" target="_self">TuningOptionsNode</a></td><td class="desc">Tuning and measurement options </td></tr>
+<tr id="row_1_1_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html" target="_self">PythonBasedMeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallbackNode.html" title="A wrapper for measure callback define [...]
+<tr id="row_1_1_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallbackNode.html" target="_self">PythonBasedMeasureCallbackNode</a></td><td class="desc">A wrapper for measure callback defined by python code This class will call functions defined in the python </td></tr>
+<tr id="row_1_1_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModel.html" target="_self">PythonBasedModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" title="A wrapper for cost model defined by python code This class will cal [...]
+<tr id="row_1_1_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" target="_self">PythonBasedModelNode</a></td><td class="desc">A wrapper for cost model defined by python code This class will call functions defined in the python </td></tr>
+<tr id="row_1_1_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html" target="_self">RandomModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" title="The cost model returning random value for all predictions. ">RandomModelNode</a> < [...]
+<tr id="row_1_1_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" target="_self">RandomModelNode</a></td><td class="desc">The cost model returning random value for all predictions </td></tr>
+<tr id="row_1_1_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html" target="_self">RecordReader</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" title="Log reader to load step logs from a file. ">RecordReaderNode</a> </td></tr>
+<tr id="row_1_1_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" target="_self">RecordReaderNode</a></td><td class="desc">Log reader to load step logs from a file </td></tr>
+<tr id="row_1_1_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html" target="_self">RecordToFile</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" title="Callback for logging the input and results of measurements to file. ">RecordToF [...]
+<tr id="row_1_1_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" target="_self">RecordToFileNode</a></td><td class="desc">Callback for logging the input and results of measurements to file </td></tr>
+<tr id="row_1_1_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStep.html" target="_self">ReorderStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" title="Reorder step that corresponds to te::Stage::reorder. ">ReorderStepNode</a> </td></tr>
+<tr id="row_1_1_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" target="_self">ReorderStepNode</a></td><td class="desc">Reorder step that corresponds to <a class="el" href="classtvm_1_1te_1_1Stage.html#ad96cd240a92df9cafae89cdf2a7e302e" title="Reorder the iteration. ">te::Stage::reorder</a> </td></tr>
+<tr id="row_1_1_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html" target="_self">RfactorStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" title="Reduction factor step that corresponds to te::Schedule::rfactor. ">RfactorStepNode [...]
+<tr id="row_1_1_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" target="_self">RfactorStepNode</a></td><td class="desc">Reduction factor step that corresponds to <a class="el" href="classtvm_1_1te_1_1Schedule.html#a34ae85add41bbed0140726d024d08862" title="Factor a reduction axis in tensor's [...]
+<tr id="row_1_1_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" target="_self">RPCRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" title="RPCRunner that uses RPC call to measures the time cost of programs on remote devices. Or [...]
+<tr id="row_1_1_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" target="_self">RPCRunnerNode</a></td><td class="desc"><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" title="Managed reference to RPCRunnerNode. ">RPCRunner</a> that uses RPC call to measures the time cost of progr [...]
+<tr id="row_1_1_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallback.html" target="_self">SearchCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallbackNode.html" title="Callback function to be called by the search process. This interface allo [...]
+<tr id="row_1_1_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallbackNode.html" target="_self">SearchCallbackNode</a></td><td class="desc">Callback function to be called by the search process. This interface allows to do extra initializations before schedule search or extra check during/after the schedule s [...]
+<tr id="row_1_1_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" target="_self">SearchPolicy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" title="The base class of search policies. ">SearchPolicyNode</a> </td></tr>
+<tr id="row_1_1_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1auto__scheduler_1_1SearchPolicyKey.html" target="_self">SearchPolicyKey</a></td><td class="desc">Attribute keys of ops used for <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" title="Managed reference to SearchPolicyNode. ">SearchPolicy</a> </td></tr>
+<tr id="row_1_1_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" target="_self">SearchPolicyNode</a></td><td class="desc">The base class of search policies </td></tr>
+<tr id="row_1_1_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTask.html" target="_self">SearchTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" title="The computation information and hardware parameters for a specific schedule search ta [...]
+<tr id="row_1_1_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" target="_self">SearchTaskNode</a></td><td class="desc">The computation information and hardware parameters for a specific schedule search task </td></tr>
+<tr id="row_1_1_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html" target="_self">SplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" title="Split step that corresponds to te::Stage::split with additional support of multiple-leve [...]
+<tr id="row_1_1_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" target="_self">SplitStepNode</a></td><td class="desc">Split step that corresponds to <a class="el" href="classtvm_1_1te_1_1Stage.html#a5a7cd562be59b68a187ad97085a3425d" title="Split the parent by factor, generate. ">te::Stage::split< [...]
+<tr id="row_1_1_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html" target="_self">Stage</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" title="A op stage in the compute declaration. Similar to te::Stage in include/tvm/te/schedule.h. ">StageNod [...]
+<tr id="row_1_1_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1auto__scheduler_1_1StageAttributes.html" target="_self">StageAttributes</a></td><td class="desc">Stage-level attributes </td></tr>
+<tr id="row_1_1_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" target="_self">StageNode</a></td><td class="desc">A op stage in the compute declaration. Similar to <a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">te::Stage</a> in <code> [...]
+<tr id="row_1_1_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1State.html" target="_self">State</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" title="A state in the search process. It consists of the current loop structure and a list of transformatio [...]
+<tr id="row_1_1_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" target="_self">StateNode</a></td><td class="desc">A state in the search process. It consists of the current loop structure and a list of transformation steps used to construct it. Each <a class="el" href="classtvm_1_1auto__scheduler_1_1S [...]
+<tr id="row_1_1_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Step.html" target="_self">Step</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" title="The base class of transformation steps. Each step has its corresponding tvm.te schedule primitives..."> [...]
+<tr id="row_1_1_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" target="_self">StepNode</a></td><td class="desc">The base class of transformation steps. Each step has its corresponding <a class="el" href="namespacetvm_1_1te.html" title="Tensor expression language DSL. ">tvm.te</a> schedule primitives [...]
+<tr id="row_1_1_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html" target="_self">StorageAlignStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" title="Storage align step that corresponds to te::Stage::storage_align. "> [...]
+<tr id="row_1_1_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" target="_self">StorageAlignStepNode</a></td><td class="desc">Storage align step that corresponds to <a class="el" href="classtvm_1_1te_1_1Stage.html#aa73e3a269d84c3b4f0a1994371d67bab" title="Set alignment requirement for speci [...]
+<tr id="row_1_1_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html" target="_self">TuningOptions</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" title="Tuning and measurement options. ">TuningOptionsNode</a> </td></tr>
+<tr id="row_1_1_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" target="_self">TuningOptionsNode</a></td><td class="desc">Tuning and measurement options </td></tr>
<tr id="row_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_2_" class="arrow" onclick="toggleFolder('1_2_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_1_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html" target="_self">AttrDocEntry</a></td><td class="desc"></td></tr>
<tr id="row_1_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html" target="_self">AttrDocVisitor</a></td><td class="desc"></td></tr>
@@ -271,199 +273,202 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr id="row_1_4_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html" target="_self">AvgPool1DAttrs</a></td><td class="desc">Attributes for 1D avg pool operator </td></tr>
<tr id="row_1_4_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html" target="_self">AvgPool2DAttrs</a></td><td class="desc">Attributes for avg pool operator </td></tr>
<tr id="row_1_4_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html" target="_self">AvgPool3DAttrs</a></td><td class="desc">Attributes for 3D avg pool operator </td></tr>
-<tr id="row_1_4_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html" target="_self">BatchNormAttrs</a></td><td class="desc">Attributes used in batch_norm operator </td></tr>
-<tr id="row_1_4_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html" target="_self">BatchToSpaceNDAttrs</a></td><td class="desc">Attributes used in BatchToSpaceND operator </td></tr>
-<tr id="row_1_4_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html" target="_self">BiasAddAttrs</a></td><td class="desc">Add a 1D Tensor to an axis of a data </td></tr>
-<tr id="row_1_4_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html" target="_self">BinaryConv2DAttrs</a></td><td class="desc">Attribues used in bitserial convolution operators </td></tr>
-<tr id="row_1_4_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html" target="_self">BinaryDenseAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html" target="_self">BitPackAttrs</a></td><td class="desc">Attributes used in bitpack operators </td></tr>
-<tr id="row_1_4_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">Call</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> container </td></tr>
-<tr id="row_1_4_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">CallPattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html" target="_self">CallPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> container </td></tr>
-<tr id="row_1_4_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html" target="_self">CastAttrs</a></td><td class="desc">Data type cast </td></tr>
-<tr id="row_1_4_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html" target="_self">CastHintAttrs</a></td><td class="desc">Annotate an expression to be cast into specific data type </td></tr>
-<tr id="row_1_4_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">Clause</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html" target="_self">ClauseNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> container node </td></tr>
-<tr id="row_1_4_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html" target="_self">ClipAttrs</a></td><td class="desc">Attributes for Clip operator </td></tr>
-<tr id="row_1_4_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html" target="_self">CompilerAttrs</a></td><td class="desc">Options for the operators used to annotate a compiler </td></tr>
-<tr id="row_1_4_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html" target="_self">ConcatenateAttrs</a></td><td class="desc">Attributes used in concatenate operators </td></tr>
-<tr id="row_1_4_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">Constant</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html" target="_self">ConstantNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> tensor type </td></tr>
-<tr id="row_1_4_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">ConstantPattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html" target="_self">ConstantPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> </td></tr>
-<tr id="row_1_4_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">ConstructorValue</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html" target="_self">ConstructorValueObj</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html" target="_self">Conv1DAttrs</a></td><td class="desc">Attributes used in 1D convolution operators </td></tr>
-<tr id="row_1_4_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html" target="_self">Conv1DTransposeAttrs</a></td><td class="desc">Attributes used in 1D transposed convolution operator </td></tr>
-<tr id="row_1_4_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html" target="_self">Conv2DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
-<tr id="row_1_4_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html" target="_self">Conv2DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
-<tr id="row_1_4_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html" target="_self">Conv2DWinogradAttrs</a></td><td class="desc">Attributes used in convolution operators with winograd algorithm </td></tr>
-<tr id="row_1_4_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html" target="_self">Conv2DWinogradNNPACKWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
-<tr id="row_1_4_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html" target="_self">Conv3DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
-<tr id="row_1_4_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html" target="_self">Conv3DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
-<tr id="row_1_4_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html" target="_self">Conv3DWinogradAttrs</a></td><td class="desc">Attributes used in 3d winograd convolution operators </td></tr>
-<tr id="row_1_4_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html" target="_self">ConvGemmWeightTransformAttrs</a></td><td class="desc">Attributes used in gemm weight transformation operators </td></tr>
-<tr id="row_1_4_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html" target="_self">ConvWinogradWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
-<tr id="row_1_4_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html" target="_self">CorrelationAttrs</a></td><td class="desc">Attributes used in correlation operators </td></tr>
-<tr id="row_1_4_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html" target="_self">CropAndResizeAttrs</a></td><td class="desc">Attributes used in image crop_and_resize operator </td></tr>
-<tr id="row_1_4_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_1_4_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html" target="_self">DataTypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
-<tr id="row_1_4_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html" target="_self">DebugAttrs</a></td><td class="desc">Options for the debug operators </td></tr>
-<tr id="row_1_4_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html" target="_self">DeformableConv2DAttrs</a></td><td class="desc">Attributes for DeformableConv2D operator </td></tr>
-<tr id="row_1_4_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html" target="_self">DenseAttrs</a></td><td class="desc">Attributes for dense operator </td></tr>
-<tr id="row_1_4_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html" target="_self">DeviceCopyAttrs</a></td><td class="desc">Options for the device copy operators </td></tr>
-<tr id="row_1_4_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
-<tr id="row_1_4_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
-<tr id="row_1_4_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html" target="_self">DFPatternCallbackNode</a></td><td class="desc">Base type of all dataflow pattern callbacks </td></tr>
-<tr id="row_1_4_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" target="_self">DFPatternFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first <a class="el" href="classtvm_1_1relay_1_1DFPattern.html" title="Managed reference to dataflow patterns. ">DFPattern</a> argument </ [...]
-<tr id="row_1_4_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">DFPatternFunctor< R(const DFPattern &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_4_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html" target="_self">DFPatternNode</a></td><td class="desc">Base type of all dataflow patterns </td></tr>
-<tr id="row_1_4_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html" target="_self">DFPatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" title="A dynamical functor that dispatches on in the first DFPattern argument. ">DFPatt [...]
-<tr id="row_1_4_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html" target="_self">DilateAttrs</a></td><td class="desc">Attributes used in dilate operator </td></tr>
-<tr id="row_1_4_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html" target="_self">Dilation2DAttrs</a></td><td class="desc">Attributes used in dilation operators </td></tr>
-<tr id="row_1_4_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
-<tr id="row_1_4_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html" target="_self">DominatorPatternNode</a></td><td class="desc">Dominated Graph <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> <a class="el" href="cla [...]
-<tr id="row_1_4_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html" target="_self">DropoutAttrs</a></td><td class="desc">Attributes used in dropout operator </td></tr>
-<tr id="row_1_4_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html" target="_self">ExpandDimsAttrs</a></td><td class="desc">Attributes used in expand_dims operators </td></tr>
-<tr id="row_1_4_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit <a class="el" href="cl [...]
-<tr id="row_1_4_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor< R(const Expr &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_4_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">Expr [...]
-<tr id="row_1_4_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
-<tr id="row_1_4_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html" target="_self">ExprPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Relay Expression </td></tr>
-<tr id="row_1_4_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html" target="_self">ExprRewriter</a></td><td class="desc">A non-iterating Expression Rewriter </td></tr>
-<tr id="row_1_4_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more p [...]
-<tr id="row_1_4_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html" target="_self">FeatureSet</a></td><td class="desc">A finite set of Feature </td></tr>
-<tr id="row_1_4_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html" target="_self">FIFOBufferAttrs</a></td><td class="desc">Attributes for FIFO buffer operator </td></tr>
-<tr id="row_1_4_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html" target="_self">FixedPointMultiplyAttrs</a></td><td class="desc">Attributes for FixedPointMultiply operator </td></tr>
-<tr id="row_1_4_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
-<tr id="row_1_4_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" target="_self">FunctionNode</a></td><td class="desc">Relay <a class="el" href="classtvm_1_1relay_1_1Function.html" title="Managed reference to FunctionNode. ">Function</a> container </td></tr>
-<tr id="row_1_4_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html" target="_self">GatherAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html" target="_self">GetValidCountsAttrs</a></td><td class="desc">Attributes used in get_valid_counts operator </td></tr>
-<tr id="row_1_4_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html" target="_self">GlobalPool2DAttrs</a></td><td class="desc">Attributes for global pool operator </td></tr>
-<tr id="row_1_4_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html" target="_self">GridSampleAttrs</a></td><td class="desc">Attributes used in image grid_sample operator </td></tr>
-<tr id="row_1_4_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html" target="_self">GroupNormAttrs</a></td><td class="desc">Attributes used in group_norm operator </td></tr>
-<tr id="row_1_4_88_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">Id</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_89_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IdNode.html" target="_self">IdNode</a></td><td class="desc">The unique identifier of variables </td></tr>
-<tr id="row_1_4_90_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">If</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_91_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfNode.html" target="_self">IfNode</a></td><td class="desc">Container of <a class="el" href="classtvm_1_1relay_1_1If.html">If</a> </td></tr>
-<tr id="row_1_4_92_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html" target="_self">InitOpAttrs</a></td><td class="desc">Attributes that specify a tensor </td></tr>
-<tr id="row_1_4_93_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html" target="_self">InstanceNormAttrs</a></td><td class="desc">Attributes used in instance_norm operator </td></tr>
-<tr id="row_1_4_94_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">InterpreterClosure</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_95_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html" target="_self">InterpreterClosureObj</a></td><td class="desc">The container type of Closures used by the interpreter </td></tr>
-<tr id="row_1_4_96_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html" target="_self">L2NormalizeAttrs</a></td><td class="desc">Attributes for L2Normalize operator </td></tr>
-<tr id="row_1_4_97_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html" target="_self">LayerNormAttrs</a></td><td class="desc">Attributes used in layer_norm operator </td></tr>
-<tr id="row_1_4_98_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html" target="_self">LayoutTransformAttrs</a></td><td class="desc">Attributes for LayoutTransform operator </td></tr>
-<tr id="row_1_4_99_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html" target="_self">LeakyReluAttrs</a></td><td class="desc">Attributes for leaky relu operator </td></tr>
-<tr id="row_1_4_100_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">Let</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_101_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc">A binding of a sub-network </td></tr>
-<tr id="row_1_4_102_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html" target="_self">LRNAttrs</a></td><td class="desc">Attributes for LRN operator </td></tr>
-<tr id="row_1_4_103_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">Match</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_104_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MatchNode.html" target="_self">MatchNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> container node </td></tr>
-<tr id="row_1_4_105_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html" target="_self">MatrixSetDiagAttrs</a></td><td class="desc">Attributes used in matrix_set_diag operator </td></tr>
-<tr id="row_1_4_106_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html" target="_self">MaxPool1DAttrs</a></td><td class="desc">Attributes for 1D max pool operator </td></tr>
-<tr id="row_1_4_107_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html" target="_self">MaxPool2DAttrs</a></td><td class="desc">Attributes for max pool operator </td></tr>
-<tr id="row_1_4_108_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html" target="_self">MaxPool3DAttrs</a></td><td class="desc">Attributes for 3D max pool operator </td></tr>
-<tr id="row_1_4_109_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html" target="_self">MeshgridAttrs</a></td><td class="desc">Attributes used in meshgrid operators </td></tr>
-<tr id="row_1_4_110_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html" target="_self">MirrorPadAttrs</a></td><td class="desc">Attributes used for the MirrorPadding operator </td></tr>
-<tr id="row_1_4_111_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html" target="_self">MixedModeMutator</a></td><td class="desc">Non-recursive DFS Graph Traversal for Custom Rewriting Passes </td></tr>
-<tr id="row_1_4_112_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html" target="_self">MixedModeVisitor</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" title="A simple visitor wrapper around ExprFunctor. Recursively visit the content. ">ExprVisitor</a> which [...]
-<tr id="row_1_4_113_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html" target="_self">MultiBoxPriorAttrs</a></td><td class="desc">Attributes used in multibox_prior operators </td></tr>
-<tr id="row_1_4_114_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html" target="_self">MultiBoxTransformLocAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_115_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html" target="_self">NdarraySizeAttrs</a></td><td class="desc">Attributes for ndarray_size operator </td></tr>
-<tr id="row_1_4_116_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html" target="_self">NonMaximumSuppressionAttrs</a></td><td class="desc">Attributes used in non_maximum_suppression operator </td></tr>
-<tr id="row_1_4_117_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html" target="_self">OnDeviceAttrs</a></td><td class="desc">Options for the device annotation operators </td></tr>
-<tr id="row_1_4_118_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html" target="_self">OneHotAttrs</a></td><td class="desc">Attributes used in one-hot operator </td></tr>
-<tr id="row_1_4_119_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
-<tr id="row_1_4_120_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html" target="_self">OpImplementationNode</a></td><td class="desc">Operator implementation that includes compute and schedule function </td></tr>
-<tr id="row_1_4_121_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
-<tr id="row_1_4_122_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html" target="_self">OpSpecializationNode</a></td><td class="desc">Specialized implementations for operators under certain conditions </td></tr>
-<tr id="row_1_4_123_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
-<tr id="row_1_4_124_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html" target="_self">OpStrategyNode</a></td><td class="desc">Operator strategy to choose implementation </td></tr>
-<tr id="row_1_4_125_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html" target="_self">PadAttrs</a></td><td class="desc">Attributes used for the padding operator </td></tr>
-<tr id="row_1_4_126_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> is the base type for an ADT match pattern in Relay </td></tr>
-<tr id="row_1_4_127_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">PatternConstructor</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_128_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html" target="_self">PatternConstructorNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_1_4_129_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" target="_self">PatternFunctor</a></td><td class="desc">A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a more powerful visitor, since it allows you to define the types of further arguments to Vi [...]
-<tr id="row_1_4_130_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">PatternFunctor< R(const Pattern &n, Args...)></a></td><td class="desc"></td></tr>
-<tr id="row_1_4_131_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html" target="_self">PatternMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu.. [...]
-<tr id="row_1_4_132_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternNode.html" target="_self">PatternNode</a></td><td class="desc">Base type for declaring relay pattern </td></tr>
-<tr id="row_1_4_133_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">PatternTuple</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_134_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html" target="_self">PatternTupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_1_4_135_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">PatternVar</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_136_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html" target="_self">PatternVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_1_4_137_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can us [...]
-<tr id="row_1_4_138_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">PatternWildcard</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_139_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html" target="_self">PatternWildcardNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> container node </td></tr>
-<tr id="row_1_4_140_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html" target="_self">PReluAttrs</a></td><td class="desc">Attributes for prelu operator </td></tr>
-<tr id="row_1_4_141_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ProposalAttrs.html" target="_self">ProposalAttrs</a></td><td class="desc">Attributes used in proposal operators </td></tr>
-<tr id="row_1_4_142_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">RecClosure</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_143_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html" target="_self">RecClosureObj</a></td><td class="desc">The container type of <a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> </td></tr>
-<tr id="row_1_4_144_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReduceAttrs.html" target="_self">ReduceAttrs</a></td><td class="desc">Attributes for Reduce operators </td></tr>
-<tr id="row_1_4_145_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">RefCreate</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_146_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreateNode.html" target="_self">RefCreateNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_147_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">RefRead</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_148_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html" target="_self">RefReadNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_149_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">RefValue</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_150_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RefValueObj.html" target="_self">RefValueObj</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_151_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">RefWrite</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_152_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html" target="_self">RefWriteNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_153_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RelayNode.html" target="_self">RelayNode</a></td><td class="desc">This is the base node container of all relay structures </td></tr>
-<tr id="row_1_4_154_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RepeatAttrs.html" target="_self">RepeatAttrs</a></td><td class="desc">Attributes used in repeat operators </td></tr>
-<tr id="row_1_4_155_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeAttrs.html" target="_self">ReshapeAttrs</a></td><td class="desc">Attributes used in reshape operators </td></tr>
-<tr id="row_1_4_156_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeLikeAttrs.html" target="_self">ReshapeLikeAttrs</a></td><td class="desc">Attributes used in MXNet-style reshape_like operators </td></tr>
-<tr id="row_1_4_157_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeTensorAttrs.html" target="_self">ReshapeTensorAttrs</a></td><td class="desc">Attributes for VM reshape_tensor operator </td></tr>
-<tr id="row_1_4_158_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Resize3dAttrs.html" target="_self">Resize3dAttrs</a></td><td class="desc">Attributes used in image resize3d operator </td></tr>
-<tr id="row_1_4_159_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html" target="_self">ResizeAttrs</a></td><td class="desc">Attributes used in image resize operator </td></tr>
-<tr id="row_1_4_160_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html" target="_self">ReverseAttrs</a></td><td class="desc">Attributes used in reverse operators </td></tr>
-<tr id="row_1_4_161_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseSequenceAttrs.html" target="_self">ReverseSequenceAttrs</a></td><td class="desc">Attributes used in reverse_sequence operators </td></tr>
-<tr id="row_1_4_162_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html" target="_self">ROIAlignAttrs</a></td><td class="desc">Attributes used in roi_align operators </td></tr>
-<tr id="row_1_4_163_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIPoolAttrs.html" target="_self">ROIPoolAttrs</a></td><td class="desc">Attributes used in roi_pool operators </td></tr>
-<tr id="row_1_4_164_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAddAttrs.html" target="_self">ScatterAddAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_165_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html" target="_self">ScatterAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_166_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterNDAttrs.html" target="_self">ScatterNDAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_167_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SequenceMaskAttrs.html" target="_self">SequenceMaskAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_168_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html" target="_self">ShapeFuncAttrs</a></td><td class="desc">Options for the shape function operator </td></tr>
-<tr id="row_1_4_169_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html" target="_self">ShapeOfAttrs</a></td><td class="desc">Attributes for ShapeOf operator </td></tr>
-<tr id="row_1_4_170_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_1_4_171_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html" target="_self">ShapePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Shapes </td></tr>
-<tr id="row_1_4_172_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html" target="_self">SliceLikeAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_173_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html" target="_self">SoftmaxAttrs</a></td><td class="desc">Attributes used in softmax operators </td></tr>
-<tr id="row_1_4_174_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SpaceToBatchNDAttrs.html" target="_self">SpaceToBatchNDAttrs</a></td><td class="desc">Attributes used in SpaceToBatchND operator </td></tr>
-<tr id="row_1_4_175_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseDenseAttrs.html" target="_self">SparseDenseAttrs</a></td><td class="desc">Attributes for sparse_dense operator </td></tr>
-<tr id="row_1_4_176_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseToDenseAttrs.html" target="_self">SparseToDenseAttrs</a></td><td class="desc">Attributes used in sparse_to_dense operator </td></tr>
-<tr id="row_1_4_177_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseTransposeAttrs.html" target="_self">SparseTransposeAttrs</a></td><td class="desc">Attributes for sparse_transpose operator </td></tr>
-<tr id="row_1_4_178_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SplitAttrs.html" target="_self">SplitAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_179_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SqueezeAttrs.html" target="_self">SqueezeAttrs</a></td><td class="desc">Attributes used in squeeze operators </td></tr>
-<tr id="row_1_4_180_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StackAttrs.html" target="_self">StackAttrs</a></td><td class="desc">Attributes used in stack operators </td></tr>
-<tr id="row_1_4_181_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html" target="_self">StridedSliceAttrs</a></td><td class="desc">Attributes for StridedSlice operator </td></tr>
-<tr id="row_1_4_182_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html" target="_self">SubPixelAttrs</a></td><td class="desc">Attributes used in subpixel operators </td></tr>
-<tr id="row_1_4_183_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html" target="_self">TakeAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_184_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">TempExpr</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_185_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExprNode.html" target="_self">TempExprNode</a></td><td class="desc">Base class of the temporary expression </td></tr>
-<tr id="row_1_4_186_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TileAttrs.html" target="_self">TileAttrs</a></td><td class="desc">Attributes used in tile operators </td></tr>
-<tr id="row_1_4_187_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TopKAttrs.html" target="_self">TopKAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_188_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html" target="_self">TransposeAttrs</a></td><td class="desc">Attributes used in transpose operators </td></tr>
-<tr id="row_1_4_189_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">Tuple</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_190_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">TupleGetItem</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_191_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">TupleGetItemNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_192_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">TupleGetItemPattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_193_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html" target="_self">TupleGetItemPatternNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_194_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
-<tr id="row_1_4_195_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">TuplePattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_196_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePatternNode.html" target="_self">TuplePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
-<tr id="row_1_4_197_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_1_4_198_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePatternNode.html" target="_self">TypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
-<tr id="row_1_4_199_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSampling3DAttrs.html" target="_self">UpSampling3DAttrs</a></td><td class="desc">Attributes for upsampling3d operator </td></tr>
-<tr id="row_1_4_200_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html" target="_self">UpSamplingAttrs</a></td><td class="desc">Attributes for upsampling operator </td></tr>
-<tr id="row_1_4_201_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">Var</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_202_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1VarianceAttrs.html" target="_self">VarianceAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_203_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_1_4_204_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">VarPattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_205_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPatternNode.html" target="_self">VarPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_1_4_206_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
-<tr id="row_1_4_207_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPatternNode.html" target="_self">WildcardPatternNode</a></td><td class="desc">Wildcard <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> </td></tr>
-<tr id="row_1_4_208_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1YoloReorgAttrs.html" target="_self">YoloReorgAttrs</a></td><td class="desc">Attributes used in yolo reorg operators </td></tr>
+<tr id="row_1_4_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BatchMatmulAttrs.html" target="_self">BatchMatmulAttrs</a></td><td class="desc">Attributes for batch matmul operator </td></tr>
+<tr id="row_1_4_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html" target="_self">BatchNormAttrs</a></td><td class="desc">Attributes used in batch_norm operator </td></tr>
+<tr id="row_1_4_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html" target="_self">BatchToSpaceNDAttrs</a></td><td class="desc">Attributes used in BatchToSpaceND operator </td></tr>
+<tr id="row_1_4_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html" target="_self">BiasAddAttrs</a></td><td class="desc">Add a 1D Tensor to an axis of a data </td></tr>
+<tr id="row_1_4_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html" target="_self">BinaryConv2DAttrs</a></td><td class="desc">Attribues used in bitserial convolution operators </td></tr>
+<tr id="row_1_4_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html" target="_self">BinaryDenseAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html" target="_self">BitPackAttrs</a></td><td class="desc">Attributes used in bitpack operators </td></tr>
+<tr id="row_1_4_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">Call</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> container </td></tr>
+<tr id="row_1_4_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">CallPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html" target="_self">CallPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> container </td></tr>
+<tr id="row_1_4_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html" target="_self">CastAttrs</a></td><td class="desc">Data type cast </td></tr>
+<tr id="row_1_4_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html" target="_self">CastHintAttrs</a></td><td class="desc">Annotate an expression to be cast into specific data type </td></tr>
+<tr id="row_1_4_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">Clause</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html" target="_self">ClauseNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> container node </td></tr>
+<tr id="row_1_4_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html" target="_self">ClipAttrs</a></td><td class="desc">Attributes for Clip operator </td></tr>
+<tr id="row_1_4_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html" target="_self">CompilerAttrs</a></td><td class="desc">Options for the operators used to annotate a compiler </td></tr>
+<tr id="row_1_4_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html" target="_self">ConcatenateAttrs</a></td><td class="desc">Attributes used in concatenate operators </td></tr>
+<tr id="row_1_4_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">Constant</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html" target="_self">ConstantNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> tensor type </td></tr>
+<tr id="row_1_4_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">ConstantPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html" target="_self">ConstantPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> </td></tr>
+<tr id="row_1_4_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">ConstructorValue</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html" target="_self">ConstructorValueObj</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html" target="_self">Conv1DAttrs</a></td><td class="desc">Attributes used in 1D convolution operators </td></tr>
+<tr id="row_1_4_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html" target="_self">Conv1DTransposeAttrs</a></td><td class="desc">Attributes used in 1D transposed convolution operator </td></tr>
+<tr id="row_1_4_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html" target="_self">Conv2DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
+<tr id="row_1_4_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html" target="_self">Conv2DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
+<tr id="row_1_4_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html" target="_self">Conv2DWinogradAttrs</a></td><td class="desc">Attributes used in convolution operators with winograd algorithm </td></tr>
+<tr id="row_1_4_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html" target="_self">Conv2DWinogradNNPACKWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
+<tr id="row_1_4_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html" target="_self">Conv3DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
+<tr id="row_1_4_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html" target="_self">Conv3DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
+<tr id="row_1_4_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html" target="_self">Conv3DWinogradAttrs</a></td><td class="desc">Attributes used in 3d winograd convolution operators </td></tr>
+<tr id="row_1_4_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html" target="_self">ConvGemmWeightTransformAttrs</a></td><td class="desc">Attributes used in gemm weight transformation operators </td></tr>
+<tr id="row_1_4_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html" target="_self">ConvWinogradWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
+<tr id="row_1_4_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html" target="_self">CorrelationAttrs</a></td><td class="desc">Attributes used in correlation operators </td></tr>
+<tr id="row_1_4_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html" target="_self">CropAndResizeAttrs</a></td><td class="desc">Attributes used in image crop_and_resize operator </td></tr>
+<tr id="row_1_4_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_1_4_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html" target="_self">DataTypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
+<tr id="row_1_4_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html" target="_self">DebugAttrs</a></td><td class="desc">Options for the debug operators </td></tr>
+<tr id="row_1_4_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html" target="_self">DeformableConv2DAttrs</a></td><td class="desc">Attributes for DeformableConv2D operator </td></tr>
+<tr id="row_1_4_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html" target="_self">DenseAttrs</a></td><td class="desc">Attributes for dense operator </td></tr>
+<tr id="row_1_4_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html" target="_self">DeviceCopyAttrs</a></td><td class="desc">Options for the device copy operators </td></tr>
+<tr id="row_1_4_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
+<tr id="row_1_4_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
+<tr id="row_1_4_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html" target="_self">DFPatternCallbackNode</a></td><td class="desc">Base type of all dataflow pattern callbacks </td></tr>
+<tr id="row_1_4_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" target="_self">DFPatternFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first <a class="el" href="classtvm_1_1relay_1_1DFPattern.html" title="Managed reference to dataflow patterns. ">DFPattern</a> argument </ [...]
+<tr id="row_1_4_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">DFPatternFunctor< R(const DFPattern &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_4_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html" target="_self">DFPatternNode</a></td><td class="desc">Base type of all dataflow patterns </td></tr>
+<tr id="row_1_4_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html" target="_self">DFPatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" title="A dynamical functor that dispatches on in the first DFPattern argument. ">DFPatt [...]
+<tr id="row_1_4_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html" target="_self">DilateAttrs</a></td><td class="desc">Attributes used in dilate operator </td></tr>
+<tr id="row_1_4_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html" target="_self">Dilation2DAttrs</a></td><td class="desc">Attributes used in dilation operators </td></tr>
+<tr id="row_1_4_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
+<tr id="row_1_4_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html" target="_self">DominatorPatternNode</a></td><td class="desc">Dominated Graph <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> <a class="el" href="cla [...]
+<tr id="row_1_4_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html" target="_self">DropoutAttrs</a></td><td class="desc">Attributes used in dropout operator </td></tr>
+<tr id="row_1_4_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html" target="_self">ExpandDimsAttrs</a></td><td class="desc">Attributes used in expand_dims operators </td></tr>
+<tr id="row_1_4_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit <a class="el" href="cl [...]
+<tr id="row_1_4_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor< R(const Expr &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_4_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">Expr [...]
+<tr id="row_1_4_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
+<tr id="row_1_4_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html" target="_self">ExprPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Relay Expression </td></tr>
+<tr id="row_1_4_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html" target="_self">ExprRewriter</a></td><td class="desc">A non-iterating Expression Rewriter </td></tr>
+<tr id="row_1_4_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more p [...]
+<tr id="row_1_4_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html" target="_self">FeatureSet</a></td><td class="desc">A finite set of Feature </td></tr>
+<tr id="row_1_4_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html" target="_self">FIFOBufferAttrs</a></td><td class="desc">Attributes for FIFO buffer operator </td></tr>
+<tr id="row_1_4_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html" target="_self">FixedPointMultiplyAttrs</a></td><td class="desc">Attributes for FixedPointMultiply operator </td></tr>
+<tr id="row_1_4_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_1_4_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" target="_self">FunctionNode</a></td><td class="desc">Relay <a class="el" href="classtvm_1_1relay_1_1Function.html" title="Managed reference to FunctionNode. ">Function</a> container </td></tr>
+<tr id="row_1_4_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html" target="_self">FunctionPattern</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_1_4_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionPatternNode.html" target="_self">FunctionPatternNode</a></td><td class="desc">Relay <a class="el" href="classtvm_1_1relay_1_1Function.html" title="Managed reference to FunctionNode. ">Function</a> container </td></tr>
+<tr id="row_1_4_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html" target="_self">GatherAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html" target="_self">GetValidCountsAttrs</a></td><td class="desc">Attributes used in get_valid_counts operator </td></tr>
+<tr id="row_1_4_88_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html" target="_self">GlobalPool2DAttrs</a></td><td class="desc">Attributes for global pool operator </td></tr>
+<tr id="row_1_4_89_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html" target="_self">GridSampleAttrs</a></td><td class="desc">Attributes used in image grid_sample operator </td></tr>
+<tr id="row_1_4_90_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html" target="_self">GroupNormAttrs</a></td><td class="desc">Attributes used in group_norm operator </td></tr>
+<tr id="row_1_4_91_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">Id</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_92_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IdNode.html" target="_self">IdNode</a></td><td class="desc">The unique identifier of variables </td></tr>
+<tr id="row_1_4_93_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">If</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_94_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfNode.html" target="_self">IfNode</a></td><td class="desc">Container of <a class="el" href="classtvm_1_1relay_1_1If.html">If</a> </td></tr>
+<tr id="row_1_4_95_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html" target="_self">InitOpAttrs</a></td><td class="desc">Attributes that specify a tensor </td></tr>
+<tr id="row_1_4_96_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html" target="_self">InstanceNormAttrs</a></td><td class="desc">Attributes used in instance_norm operator </td></tr>
+<tr id="row_1_4_97_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">InterpreterClosure</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_98_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html" target="_self">InterpreterClosureObj</a></td><td class="desc">The container type of Closures used by the interpreter </td></tr>
+<tr id="row_1_4_99_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html" target="_self">L2NormalizeAttrs</a></td><td class="desc">Attributes for L2Normalize operator </td></tr>
+<tr id="row_1_4_100_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html" target="_self">LayerNormAttrs</a></td><td class="desc">Attributes used in layer_norm operator </td></tr>
+<tr id="row_1_4_101_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html" target="_self">LayoutTransformAttrs</a></td><td class="desc">Attributes for LayoutTransform operator </td></tr>
+<tr id="row_1_4_102_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html" target="_self">LeakyReluAttrs</a></td><td class="desc">Attributes for leaky relu operator </td></tr>
+<tr id="row_1_4_103_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">Let</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_104_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc">A binding of a sub-network </td></tr>
+<tr id="row_1_4_105_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html" target="_self">LRNAttrs</a></td><td class="desc">Attributes for LRN operator </td></tr>
+<tr id="row_1_4_106_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">Match</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_107_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MatchNode.html" target="_self">MatchNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> container node </td></tr>
+<tr id="row_1_4_108_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html" target="_self">MatrixSetDiagAttrs</a></td><td class="desc">Attributes used in matrix_set_diag operator </td></tr>
+<tr id="row_1_4_109_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html" target="_self">MaxPool1DAttrs</a></td><td class="desc">Attributes for 1D max pool operator </td></tr>
+<tr id="row_1_4_110_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html" target="_self">MaxPool2DAttrs</a></td><td class="desc">Attributes for max pool operator </td></tr>
+<tr id="row_1_4_111_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html" target="_self">MaxPool3DAttrs</a></td><td class="desc">Attributes for 3D max pool operator </td></tr>
+<tr id="row_1_4_112_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html" target="_self">MeshgridAttrs</a></td><td class="desc">Attributes used in meshgrid operators </td></tr>
+<tr id="row_1_4_113_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html" target="_self">MirrorPadAttrs</a></td><td class="desc">Attributes used for the MirrorPadding operator </td></tr>
+<tr id="row_1_4_114_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html" target="_self">MixedModeMutator</a></td><td class="desc">Non-recursive DFS Graph Traversal for Custom Rewriting Passes </td></tr>
+<tr id="row_1_4_115_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html" target="_self">MixedModeVisitor</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" title="A simple visitor wrapper around ExprFunctor. Recursively visit the content. ">ExprVisitor</a> which [...]
+<tr id="row_1_4_116_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html" target="_self">MultiBoxPriorAttrs</a></td><td class="desc">Attributes used in multibox_prior operators </td></tr>
+<tr id="row_1_4_117_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html" target="_self">MultiBoxTransformLocAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_118_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html" target="_self">NdarraySizeAttrs</a></td><td class="desc">Attributes for ndarray_size operator </td></tr>
+<tr id="row_1_4_119_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html" target="_self">NonMaximumSuppressionAttrs</a></td><td class="desc">Attributes used in non_maximum_suppression operator </td></tr>
+<tr id="row_1_4_120_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html" target="_self">OnDeviceAttrs</a></td><td class="desc">Options for the device annotation operators </td></tr>
+<tr id="row_1_4_121_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html" target="_self">OneHotAttrs</a></td><td class="desc">Attributes used in one-hot operator </td></tr>
+<tr id="row_1_4_122_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
+<tr id="row_1_4_123_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html" target="_self">OpImplementationNode</a></td><td class="desc">Operator implementation that includes compute and schedule function </td></tr>
+<tr id="row_1_4_124_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
+<tr id="row_1_4_125_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html" target="_self">OpSpecializationNode</a></td><td class="desc">Specialized implementations for operators under certain conditions </td></tr>
+<tr id="row_1_4_126_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
+<tr id="row_1_4_127_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html" target="_self">OpStrategyNode</a></td><td class="desc">Operator strategy to choose implementation </td></tr>
+<tr id="row_1_4_128_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html" target="_self">PadAttrs</a></td><td class="desc">Attributes used for the padding operator </td></tr>
+<tr id="row_1_4_129_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> is the base type for an ADT match pattern in Relay </td></tr>
+<tr id="row_1_4_130_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">PatternConstructor</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_131_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html" target="_self">PatternConstructorNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_1_4_132_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" target="_self">PatternFunctor</a></td><td class="desc">A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a more powerful visitor, since it allows you to define the types of further arguments to Vi [...]
+<tr id="row_1_4_133_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">PatternFunctor< R(const Pattern &n, Args...)></a></td><td class="desc"></td></tr>
+<tr id="row_1_4_134_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html" target="_self">PatternMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu.. [...]
+<tr id="row_1_4_135_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternNode.html" target="_self">PatternNode</a></td><td class="desc">Base type for declaring relay pattern </td></tr>
+<tr id="row_1_4_136_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">PatternTuple</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_137_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html" target="_self">PatternTupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_1_4_138_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">PatternVar</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_139_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html" target="_self">PatternVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_1_4_140_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can us [...]
+<tr id="row_1_4_141_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">PatternWildcard</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_142_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html" target="_self">PatternWildcardNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> container node </td></tr>
+<tr id="row_1_4_143_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html" target="_self">PReluAttrs</a></td><td class="desc">Attributes for prelu operator </td></tr>
+<tr id="row_1_4_144_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ProposalAttrs.html" target="_self">ProposalAttrs</a></td><td class="desc">Attributes used in proposal operators </td></tr>
+<tr id="row_1_4_145_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">RecClosure</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_146_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html" target="_self">RecClosureObj</a></td><td class="desc">The container type of <a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> </td></tr>
+<tr id="row_1_4_147_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReduceAttrs.html" target="_self">ReduceAttrs</a></td><td class="desc">Attributes for Reduce operators </td></tr>
+<tr id="row_1_4_148_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">RefCreate</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_149_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreateNode.html" target="_self">RefCreateNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_150_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">RefRead</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_151_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html" target="_self">RefReadNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_152_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">RefValue</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_153_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RefValueObj.html" target="_self">RefValueObj</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_154_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">RefWrite</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_155_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html" target="_self">RefWriteNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_156_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RelayNode.html" target="_self">RelayNode</a></td><td class="desc">This is the base node container of all relay structures </td></tr>
+<tr id="row_1_4_157_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RepeatAttrs.html" target="_self">RepeatAttrs</a></td><td class="desc">Attributes used in repeat operators </td></tr>
+<tr id="row_1_4_158_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeAttrs.html" target="_self">ReshapeAttrs</a></td><td class="desc">Attributes used in reshape operators </td></tr>
+<tr id="row_1_4_159_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeLikeAttrs.html" target="_self">ReshapeLikeAttrs</a></td><td class="desc">Attributes used in MXNet-style reshape_like operators </td></tr>
+<tr id="row_1_4_160_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeTensorAttrs.html" target="_self">ReshapeTensorAttrs</a></td><td class="desc">Attributes for VM reshape_tensor operator </td></tr>
+<tr id="row_1_4_161_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Resize3dAttrs.html" target="_self">Resize3dAttrs</a></td><td class="desc">Attributes used in image resize3d operator </td></tr>
+<tr id="row_1_4_162_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html" target="_self">ResizeAttrs</a></td><td class="desc">Attributes used in image resize operator </td></tr>
+<tr id="row_1_4_163_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html" target="_self">ReverseAttrs</a></td><td class="desc">Attributes used in reverse operators </td></tr>
+<tr id="row_1_4_164_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseSequenceAttrs.html" target="_self">ReverseSequenceAttrs</a></td><td class="desc">Attributes used in reverse_sequence operators </td></tr>
+<tr id="row_1_4_165_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html" target="_self">ROIAlignAttrs</a></td><td class="desc">Attributes used in roi_align operators </td></tr>
+<tr id="row_1_4_166_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIPoolAttrs.html" target="_self">ROIPoolAttrs</a></td><td class="desc">Attributes used in roi_pool operators </td></tr>
+<tr id="row_1_4_167_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAddAttrs.html" target="_self">ScatterAddAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_168_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html" target="_self">ScatterAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_169_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterNDAttrs.html" target="_self">ScatterNDAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_170_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SequenceMaskAttrs.html" target="_self">SequenceMaskAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_171_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html" target="_self">ShapeFuncAttrs</a></td><td class="desc">Options for the shape function operator </td></tr>
+<tr id="row_1_4_172_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html" target="_self">ShapeOfAttrs</a></td><td class="desc">Attributes for ShapeOf operator </td></tr>
+<tr id="row_1_4_173_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_1_4_174_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html" target="_self">ShapePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Shapes </td></tr>
+<tr id="row_1_4_175_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html" target="_self">SliceLikeAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_176_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html" target="_self">SoftmaxAttrs</a></td><td class="desc">Attributes used in softmax operators </td></tr>
+<tr id="row_1_4_177_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SpaceToBatchNDAttrs.html" target="_self">SpaceToBatchNDAttrs</a></td><td class="desc">Attributes used in SpaceToBatchND operator </td></tr>
+<tr id="row_1_4_178_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseDenseAttrs.html" target="_self">SparseDenseAttrs</a></td><td class="desc">Attributes for sparse_dense operator </td></tr>
+<tr id="row_1_4_179_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseToDenseAttrs.html" target="_self">SparseToDenseAttrs</a></td><td class="desc">Attributes used in sparse_to_dense operator </td></tr>
+<tr id="row_1_4_180_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseTransposeAttrs.html" target="_self">SparseTransposeAttrs</a></td><td class="desc">Attributes for sparse_transpose operator </td></tr>
+<tr id="row_1_4_181_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SplitAttrs.html" target="_self">SplitAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_182_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SqueezeAttrs.html" target="_self">SqueezeAttrs</a></td><td class="desc">Attributes used in squeeze operators </td></tr>
+<tr id="row_1_4_183_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StackAttrs.html" target="_self">StackAttrs</a></td><td class="desc">Attributes used in stack operators </td></tr>
+<tr id="row_1_4_184_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html" target="_self">StridedSliceAttrs</a></td><td class="desc">Attributes for StridedSlice operator </td></tr>
+<tr id="row_1_4_185_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html" target="_self">SubPixelAttrs</a></td><td class="desc">Attributes used in subpixel operators </td></tr>
+<tr id="row_1_4_186_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html" target="_self">TakeAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_187_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">TempExpr</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_188_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExprNode.html" target="_self">TempExprNode</a></td><td class="desc">Base class of the temporary expression </td></tr>
+<tr id="row_1_4_189_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TileAttrs.html" target="_self">TileAttrs</a></td><td class="desc">Attributes used in tile operators </td></tr>
+<tr id="row_1_4_190_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TopKAttrs.html" target="_self">TopKAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_191_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html" target="_self">TransposeAttrs</a></td><td class="desc">Attributes used in transpose operators </td></tr>
+<tr id="row_1_4_192_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">Tuple</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_193_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">TupleGetItem</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_194_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">TupleGetItemNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_195_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">TupleGetItemPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_196_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html" target="_self">TupleGetItemPatternNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_197_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
+<tr id="row_1_4_198_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">TuplePattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_199_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePatternNode.html" target="_self">TuplePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
+<tr id="row_1_4_200_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_1_4_201_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePatternNode.html" target="_self">TypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
+<tr id="row_1_4_202_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSampling3DAttrs.html" target="_self">UpSampling3DAttrs</a></td><td class="desc">Attributes for upsampling3d operator </td></tr>
+<tr id="row_1_4_203_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html" target="_self">UpSamplingAttrs</a></td><td class="desc">Attributes for upsampling operator </td></tr>
+<tr id="row_1_4_204_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">Var</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_205_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1VarianceAttrs.html" target="_self">VarianceAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_206_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
+<tr id="row_1_4_207_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">VarPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_208_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPatternNode.html" target="_self">VarPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
+<tr id="row_1_4_209_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
+<tr id="row_1_4_210_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPatternNode.html" target="_self">WildcardPatternNode</a></td><td class="desc">Wildcard <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> </td></tr>
+<tr id="row_1_4_211_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1YoloReorgAttrs.html" target="_self">YoloReorgAttrs</a></td><td class="desc">Attributes used in yolo reorg operators </td></tr>
<tr id="row_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_5_" class="arrow" onclick="toggleFolder('1_5_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime.html" target="_self">runtime</a></td><td class="desc"></td></tr>
<tr id="row_1_5_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_5_0_" class="arrow" onclick="toggleFolder('1_5_0_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html" target="_self">micro_rpc</a></td><td class="desc"></td></tr>
<tr id="row_1_5_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html" target="_self">FrameBuffer</a></td><td class="desc"></td></tr>
@@ -853,16 +858,17 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr id="row_1_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> parameter in functions </td></tr>
<tr id="row_1_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
<tr id="row_1_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
-<tr id="row_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
-<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
-<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
-<tr id="row_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphRuntimeGraphAttr.html" target="_self">TVMGraphRuntimeGraphAttr</a></td><td class="desc"></td></tr>
-<tr id="row_6_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
-<tr id="row_7_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name. ">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
-<tr id="row_8_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
-<tr id="row_9_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
-<tr id="row_10_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
-<tr id="row_11_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
+<tr id="row_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structMemoryManagerInterface.html" target="_self">MemoryManagerInterface</a></td><td class="desc"></td></tr>
+<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
+<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
+<tr id="row_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
+<tr id="row_6_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphRuntimeGraphAttr.html" target="_self">TVMGraphRuntimeGraphAttr</a></td><td class="desc"></td></tr>
+<tr id="row_7_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
+<tr id="row_8_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name. ">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
+<tr id="row_9_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
+<tr id="row_10_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
+<tr id="row_11_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
+<tr id="row_12_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
</table>
</div><!-- directory -->
</div><!-- contents -->
diff --git a/docs/api/doxygen/auto__schedule_8h_source.html b/docs/api/doxygen/auto__schedule_8h_source.html
index 7438027..a71b867 100644
--- a/docs/api/doxygen/auto__schedule_8h_source.html
+++ b/docs/api/doxygen/auto__schedule_8h_source.html
@@ -97,7 +97,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html_a60515c17b530f5c8b806930469bdd22c"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html#a60515c17b530f5c8b806930469bdd22c">tvm::auto_scheduler::TuningOptionsNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> auto_schedule.h:54</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html">tvm::auto_scheduler::TuningOptionsNode</a></div><div class="ttdoc">Tuning and measurement options. </div><div class="ttdef"><b>Definition:</b> auto_schedule.h:37</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html_a64abc6eeb90309a01a0a9d415aaf26f4"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html#a64abc6eeb90309a01a0a9d415aaf26f4">tvm::auto_scheduler::TuningOptionsNode::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(TuningOptionsNode, Object)</div></div>
-<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ProgramRunner_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html">tvm::auto_scheduler::ProgramRunner</a></div><div class="ttdoc">Managed reference to ProgramRunnerNode. </div><div class="ttdef"><b>Definition:</b> measure.h:302</div></div>
+<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ProgramRunner_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html">tvm::auto_scheduler::ProgramRunner</a></div><div class="ttdoc">Managed reference to ProgramRunnerNode. </div><div class="ttdef"><b>Definition:</b> measure.h:331</div></div>
<div class="ttc" id="classtvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class for to get the attributesof a AST/IR node. The content is going to be called for each f...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html_ade4f4a9a5dda968e8a81a43c8d8b597e"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html#ade4f4a9a5dda968e8a81a43c8d8b597e">tvm::auto_scheduler::TuningOptionsNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> auto_schedule.h:64</div></div>
<div class="ttc" id="measure_8h_html"><div class="ttname"><a href="measure_8h.html">measure.h</a></div><div class="ttdoc">Distributed measurement infrastructure to measure the runtime costs of tensor programs. These functions are responsible for building the tvm module, uploading it to remote devices, recording the running time costs, and checking the correctness of the output. </div></div>
@@ -105,7 +105,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1SearchPolicy_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html">tvm::auto_scheduler::SearchPolicy</a></div><div class="ttdoc">Managed reference to SearchPolicyNode. </div><div class="ttdef"><b>Definition:</b> search_policy.h:198</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html_af6f3c49598d8377e02df4cd4d43ce732"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html#af6f3c49598d8377e02df4cd4d43ce732">tvm::auto_scheduler::TuningOptionsNode::num_measure_trials</a></div><div class="ttdeci">int num_measure_trials</div><div class="ttdoc">The number of total measurement trials. </div><div class="ttdef"><b>Definition:</b> auto_schedule.h:40</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html_a355d86b2c38f0827ae1b158753d1daa2"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html#a355d86b2c38f0827ae1b158753d1daa2">tvm::auto_scheduler::TuningOptionsNode::runner</a></div><div class="ttdeci">ProgramRunner runner</div><div class="ttdoc">ProgramRunner which runs the program and measures time costs. </div><div class="ttdef"><b>Definition:</b> auto_schedule.h:50</div></div>
-<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ProgramBuilder_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html">tvm::auto_scheduler::ProgramBuilder</a></div><div class="ttdoc">Managed reference to ProgramBuilderNode. </div><div class="ttdef"><b>Definition:</b> measure.h:262</div></div>
+<div class="ttc" id="classtvm_1_1auto__scheduler_1_1ProgramBuilder_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html">tvm::auto_scheduler::ProgramBuilder</a></div><div class="ttdoc">Managed reference to ProgramBuilderNode. </div><div class="ttdef"><b>Definition:</b> measure.h:291</div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptionsNode_html_a3ba0915aabeb33a6adbcdceb4e6a43b9"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html#a3ba0915aabeb33a6adbcdceb4e6a43b9">tvm::auto_scheduler::TuningOptionsNode::measure_callbacks</a></div><div class="ttdeci">Optional< Array< MeasureCallback > > measure_callbacks</div><div class="ttdoc">MeasureCallback functions to be called after each measure batch. </div><div class="ttd [...]
<div class="ttc" id="namespacetvm_1_1auto__scheduler_html_a1d6d14e885abf0c3b91e6b3cf542c2de"><div class="ttname"><a href="namespacetvm_1_1auto__scheduler.html#a1d6d14e885abf0c3b91e6b3cf542c2de">tvm::auto_scheduler::AutoSchedule</a></div><div class="ttdeci">std::pair< te::Schedule, Array< te::Tensor > > AutoSchedule(SearchPolicy search_policy, TuningOptions tuning_options)</div><div class="ttdoc">Run schedule search for a given compute declaration. </div></div>
<div class="ttc" id="classtvm_1_1auto__scheduler_1_1TuningOptions_html"><div class="ttname"><a href="classtvm_1_1auto__scheduler_1_1TuningOptions.html">tvm::auto_scheduler::TuningOptions</a></div><div class="ttdoc">Managed reference to TuningOptionsNode. </div><div class="ttdef"><b>Definition:</b> auto_schedule.h:72</div></div>
diff --git a/docs/api/doxygen/builtin_8h.html b/docs/api/doxygen/builtin_8h.html
index 0c520c2..2f9354c 100644
--- a/docs/api/doxygen/builtin_8h.html
+++ b/docs/api/doxygen/builtin_8h.html
@@ -303,6 +303,9 @@ Functions</h2></td></tr>
<tr class="memitem:a30dff65bc2c142b57fae7f60e378ff43"><td class="memItemLeft" align="right" valign="top">const Op & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43">tvm::tir::builtin::vectorcombine</a> ()</td></tr>
<tr class="memdesc:a30dff65bc2c142b57fae7f60e378ff43"><td class="mdescLeft"> </td><td class="mdescRight">Concat two vectors. <a href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43">More...</a><br /></td></tr>
<tr class="separator:a30dff65bc2c142b57fae7f60e378ff43"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab4a648f6e7451af295688f243a215cd7"><td class="memItemLeft" align="right" valign="top">const Op & </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7">tvm::tir::builtin::atomic_add</a> ()</td></tr>
+<tr class="memdesc:ab4a648f6e7451af295688f243a215cd7"><td class="mdescLeft"> </td><td class="mdescRight">atomic add instruction, corresponding e.g. to atomicAdd in CUDA <a href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7">More...</a><br /></td></tr>
+<tr class="separator:ab4a648f6e7451af295688f243a215cd7"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>TIR builtin intrinsics. </p>
diff --git a/docs/api/doxygen/builtin_8h_source.html b/docs/api/doxygen/builtin_8h_source.html
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--- a/docs/api/doxygen/builtin_8h_source.html
+++ b/docs/api/doxygen/builtin_8h_source.html
@@ -89,44 +89,45 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="title">builtin.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="builtin_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * or more con [...]
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a">tvm::tir::builtin::kArrNDim</a></div><div class="ttdef"><b>Definition:</b> builtin.h:559</div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54">tvm::tir::builtin::kArrAddr</a></div><div class="ttdef"><b>Definition:</b> builtin.h:555</div></div>
+<a href="builtin_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * or more con [...]
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cad28cfc7b69fd8745e12a4f0024d6942a">tvm::tir::builtin::kArrNDim</a></div><div class="ttdef"><b>Definition:</b> builtin.h:564</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0b8af30aa268164148d5bfe1d8c2ba54">tvm::tir::builtin::kArrAddr</a></div><div class="ttdef"><b>Definition:</b> builtin.h:560</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a925a45e5bb05e0cbf2daf2ffdbdcf53a"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a925a45e5bb05e0cbf2daf2ffdbdcf53a">tvm::tir::builtin::tvm_storage_sync</a></div><div class="ttdeci">const Op & tvm_storage_sync()</div><div class="ttdoc">See pseudo code. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a15c5e0e0478e0ebff91690f60992cf3f"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a15c5e0e0478e0ebff91690f60992cf3f">tvm::tir::builtin::tvm_stack_alloca</a></div><div class="ttdeci">const Op & tvm_stack_alloca()</div><div class="ttdoc">See pesudo code. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_aa1d19e758595200998a4e1ea39767b6b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#aa1d19e758595200998a4e1ea39767b6b">tvm::tir::builtin::tvm_thread_allreduce</a></div><div class="ttdeci">const Op & tvm_thread_allreduce()</div><div class="ttdoc">See pesudo code. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_aca44a85c87273dfab1731421f4edd2bf"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#aca44a85c87273dfab1731421f4edd2bf">tvm::tir::builtin::tvm_warp_shuffle</a></div><div class="ttdeci">const Op & tvm_warp_shuffle()</div><div class="ttdoc">See pseudo code. </div></div>
<div class="ttc" id="namespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a83892dca19e44a96752625c65c38d645"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a83892dca19e44a96752625c65c38d645">tvm::tir::builtin::call_llvm_intrin</a></div><div class="ttdeci">const Op & call_llvm_intrin()</div><div class="ttdoc">Call an LLVM intrinsic with a given intrinsic id and signature from the types of args in the runtime ...</div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d">tvm::tir::builtin::kArrDeviceType</a></div><div class="ttdef"><b>Definition:</b> builtin.h:565</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cabf798b873c868b7d77ced30c9907037d">tvm::tir::builtin::kArrDeviceType</a></div><div class="ttdef"><b>Definition:</b> builtin.h:570</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a1e15b04fe89f7899e09e528946aa5bb4"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a1e15b04fe89f7899e09e528946aa5bb4">tvm::tir::builtin::fma</a></div><div class="ttdeci">const Op & fma()</div><div class="ttdoc">Fused multiply add. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a8e3504415c78f3f8fd719a21e5280b38"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a8e3504415c78f3f8fd719a21e5280b38">tvm::tir::builtin::call_llvm_pure_intrin</a></div><div class="ttdeci">const Op & call_llvm_pure_intrin()</div><div class="ttdoc">Call an LLVM pure intrinsic with a given intrinsic id and signature from the types of args in the run...</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ab4a648f6e7451af295688f243a215cd7"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ab4a648f6e7451af295688f243a215cd7">tvm::tir::builtin::atomic_add</a></div><div class="ttdeci">const Op & atomic_add()</div><div class="ttdoc">atomic add instruction, corresponding e.g. to atomicAdd in CUDA </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ac8e7bc86b8fa81453291ae5299062001"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ac8e7bc86b8fa81453291ae5299062001">tvm::tir::builtin::tvm_global_barrier_kinit</a></div><div class="ttdeci">const Op & tvm_global_barrier_kinit()</div><div class="ttdoc">Initialize the global barrier. Call this at beginning of kernel that need global barrier. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a30dff65bc2c142b57fae7f60e378ff43"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a30dff65bc2c142b57fae7f60e378ff43">tvm::tir::builtin::vectorcombine</a></div><div class="ttdeci">const Op & vectorcombine()</div><div class="ttdoc">Concat two vectors. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ae0470bd69bb03047aae4cb52e1e6e337"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ae0470bd69bb03047aae4cb52e1e6e337">tvm::tir::builtin::tvm_warp_shuffle_up</a></div><div class="ttdeci">const Op & tvm_warp_shuffle_up()</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ac54288cc9f1fee8c26db9bd87ac320ee"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ac54288cc9f1fee8c26db9bd87ac320ee">tvm::tir::builtin::tvm_call_trace_packed</a></div><div class="ttdeci">const Op & tvm_call_trace_packed()</div><div class="ttdoc">See pesudo code. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_afc81da8cbcd7f34ec5e1e80d837ca265"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#afc81da8cbcd7f34ec5e1e80d837ca265">tvm::tir::builtin::tvm_store_matrix_sync</a></div><div class="ttdeci">const Op & tvm_store_matrix_sync()</div><div class="ttdoc">tvm intrinsic for tensor core store operators. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c">tvm::tir::builtin::kArrStrides</a></div><div class="ttdef"><b>Definition:</b> builtin.h:558</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca96e7b6492b5b174219cf60e19af0857c">tvm::tir::builtin::kArrStrides</a></div><div class="ttdef"><b>Definition:</b> builtin.h:563</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ac4887bd93ad67619ad290a33e2bdd340"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ac4887bd93ad67619ad290a33e2bdd340">tvm::tir::builtin::call_spirv_pure_glsl450</a></div><div class="ttdeci">const Op & call_spirv_pure_glsl450()</div><div class="ttdoc">Call an SPIRV pure GLSL450 intrinsic. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a6be181be34fba13d129aadc6c9a23f73"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a6be181be34fba13d129aadc6c9a23f73">tvm::tir::builtin::tvm_thread_context</a></div><div class="ttdeci">const Op & tvm_thread_context()</div><div class="ttdoc">See pesudo code Mark the content as thread local context, can get optimized by only call the call onc...</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a700b7018f2c1f1fba8b4e28f264d8bbb"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a700b7018f2c1f1fba8b4e28f264d8bbb">tvm::tir::builtin::address_of</a></div><div class="ttdeci">const Op & address_of()</div><div class="ttdoc">See pesudo code. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a091ef99dc63f6945588dbb81c968ca15"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a091ef99dc63f6945588dbb81c968ca15">tvm::tir::builtin::bitwise_not</a></div><div class="ttdeci">const Op & bitwise_not()</div><div class="ttdoc">Bitwise not operator. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb">tvm::tir::builtin::kArrByteOffset</a></div><div class="ttdef"><b>Definition:</b> builtin.h:563</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1cafdb925cdf50f17a2b96c7ac4faefa1fb">tvm::tir::builtin::kArrByteOffset</a></div><div class="ttdef"><b>Definition:</b> builtin.h:568</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a0c2ebdcec34d7c79dc8480e5dab8547a"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a0c2ebdcec34d7c79dc8480e5dab8547a">tvm::tir::builtin::q_multiply_shift</a></div><div class="ttdeci">const Op & q_multiply_shift()</div><div class="ttdoc">Execute a multiplication between two Q-numbers x and y followed by a right shift s The default roundi...</div></div>
<div class="ttc" id="ir_2op_8h_html"><div class="ttname"><a href="ir_2op_8h.html">op.h</a></div><div class="ttdoc">Primitive operators(builtin intrinsics) and registry for them. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a7ed64a9fb0a7f575fc63e1e0395e96a6"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a7ed64a9fb0a7f575fc63e1e0395e96a6">tvm::tir::builtin::vectorlow</a></div><div class="ttdeci">const Op & vectorlow()</div><div class="ttdoc">Get the low-level half of the vector. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a0cbd267877168afd5bbea35f0e5d70fe"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a0cbd267877168afd5bbea35f0e5d70fe">tvm::tir::builtin::tvm_mma_sync</a></div><div class="ttdeci">const Op & tvm_mma_sync()</div><div class="ttdoc">tvm intrinsic for tensor core mma_sync operators. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e">tvm::tir::builtin::kArrTypeBits</a></div><div class="ttdef"><b>Definition:</b> builtin.h:561</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca344dc1f419339b81024d4d3628083a1e">tvm::tir::builtin::kArrTypeBits</a></div><div class="ttdef"><b>Definition:</b> builtin.h:566</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a7b555bc5cca2f5e7b26c1037bc0001ce"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a7b555bc5cca2f5e7b26c1037bc0001ce">tvm::tir::builtin::reinterpret</a></div><div class="ttdeci">const Op & reinterpret()</div><div class="ttdoc">Reinterpret the value using the target type. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ae2add6e324d391782d367360a68ccf51"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ae2add6e324d391782d367360a68ccf51">tvm::tir::builtin::call_pure_extern</a></div><div class="ttdeci">const Op & call_pure_extern()</div><div class="ttdoc">Call an pure extern C function with given name and signature from the types of args in the runtime en...</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a2172690dd21d7fd50a4fd4d696ea7bb2"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a2172690dd21d7fd50a4fd4d696ea7bb2">tvm::tir::builtin::popcount</a></div><div class="ttdeci">const Op & popcount()</div><div class="ttdoc">Popcount. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00">tvm::tir::builtin::kArrTypeLanes</a></div><div class="ttdef"><b>Definition:</b> builtin.h:562</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca0c960782c20a4f16cfe203c516760b00">tvm::tir::builtin::kArrTypeLanes</a></div><div class="ttdef"><b>Definition:</b> builtin.h:567</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a2c13c6e4b2f92e17f357665f9f11736c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a2c13c6e4b2f92e17f357665f9f11736c">tvm::tir::builtin::tvm_call_packed</a></div><div class="ttdeci">const Op & tvm_call_packed()</div><div class="ttdoc">See pesudo code. </div></div>
<div class="ttc" id="tir_2expr_8h_html"><div class="ttname"><a href="tir_2expr_8h.html">expr.h</a></div><div class="ttdoc">TIR expressions. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92">tvm::tir::builtin::kArrKindBound_</a></div><div class="ttdef"><b>Definition:</b> builtin.h:566</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca4d4a5d54434514fd8b0ce57160059c92">tvm::tir::builtin::kArrKindBound_</a></div><div class="ttdef"><b>Definition:</b> builtin.h:571</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a21d1f0395dca5af4a90cdb42c1d1d154"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a21d1f0395dca5af4a90cdb42c1d1d154">tvm::tir::builtin::likely</a></div><div class="ttdeci">const Op & likely()</div><div class="ttdoc">Marks a condition is likely going to happen. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a23003bd9331efaa58d8420529ea96c0b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a23003bd9331efaa58d8420529ea96c0b">tvm::tir::builtin::tvm_struct_get</a></div><div class="ttdeci">const Op & tvm_struct_get()</div><div class="ttdoc">See pesudo code. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304">tvm::tir::builtin::kTVMValueContent</a></div><div class="ttdef"><b>Definition:</b> builtin.h:568</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca779c07403e11f671e936ec2813ce2304">tvm::tir::builtin::kTVMValueContent</a></div><div class="ttdef"><b>Definition:</b> builtin.h:573</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a28f99e6dd767482765b854ee9fc71f2c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a28f99e6dd767482765b854ee9fc71f2c">tvm::tir::builtin::tvm_stack_make_array</a></div><div class="ttdeci">const Op & tvm_stack_make_array()</div><div class="ttdoc">Allocate a NDArray(DLTensor) on stack, return the handle. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a543f1fc334d2bc830add972895a03f17"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a543f1fc334d2bc830add972895a03f17">tvm::tir::builtin::prefetch</a></div><div class="ttdeci">const Op & prefetch()</div><div class="ttdoc">Prefetch a cacheline. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a92624d2aa5c435cd7a0ea8efb698a115"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a92624d2aa5c435cd7a0ea8efb698a115">tvm::tir::builtin::tvm_throw_last_error</a></div><div class="ttdeci">const Op & tvm_throw_last_error()</div><div class="ttdoc">See pesudo code. </div></div>
@@ -134,9 +135,9 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a3e84c73dbbcf7f97008ac84c169feae9"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a3e84c73dbbcf7f97008ac84c169feae9">tvm::tir::builtin::tvm_access_ptr</a></div><div class="ttdeci">const Op & tvm_access_ptr()</div><div class="ttdoc">Get head access address with memory access pattern info. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_abd540cb73407771ecfb4f78722ce5a1b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#abd540cb73407771ecfb4f78722ce5a1b">tvm::tir::builtin::tvm_stack_make_shape</a></div><div class="ttdeci">const Op & tvm_stack_make_shape()</div><div class="ttdoc">Allocate a shape tuple on stack, return the handle. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ae741e67259cd4b844a8934f2e2704adc"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ae741e67259cd4b844a8934f2e2704adc">tvm::tir::builtin::if_then_else</a></div><div class="ttdeci">const Op & if_then_else()</div><div class="ttdoc">Same as select, used for unsafe memory access. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858">tvm::tir::builtin::kTVMValueKindBound_</a></div><div class="ttdef"><b>Definition:</b> builtin.h:569</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1caa73457ed97931251f1762cb319adc858">tvm::tir::builtin::kTVMValueKindBound_</a></div><div class="ttdef"><b>Definition:</b> builtin.h:574</div></div>
<div class="ttc" id="classtvm_1_1Op_html"><div class="ttname"><a href="classtvm_1_1Op.html">tvm::Op</a></div><div class="ttdoc">Managed reference class to OpNode. </div><div class="ttdef"><b>Definition:</b> op.h:165</div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da">tvm::tir::builtin::kArrShape</a></div><div class="ttdef"><b>Definition:</b> builtin.h:557</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca57f69fd3d141caaa7e2e72fda7d6a1da">tvm::tir::builtin::kArrShape</a></div><div class="ttdef"><b>Definition:</b> builtin.h:562</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_afc4086a245ded9076de226ae802ced32"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#afc4086a245ded9076de226ae802ced32">tvm::tir::builtin::tvm_warp_activemask</a></div><div class="ttdeci">const Op & tvm_warp_activemask()</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a668eaad07b6c46238f2bf758e61b58a5"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a668eaad07b6c46238f2bf758e61b58a5">tvm::tir::builtin::call_extern</a></div><div class="ttdeci">const Op & call_extern()</div><div class="ttdoc">Call an extern C function with given name and signature from the types of args in the runtime environ...</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_af103ae0715d4ebcbaccd49d2b6a12afe"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#af103ae0715d4ebcbaccd49d2b6a12afe">tvm::tir::builtin::shift_right</a></div><div class="ttdeci">const Op & shift_right()</div><div class="ttdoc">Right shift. </div></div>
@@ -145,7 +146,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a8d5e173f1a16740172a9ad6f2aa85a08"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a8d5e173f1a16740172a9ad6f2aa85a08">tvm::tir::builtin::tvm_bmma_sync</a></div><div class="ttdeci">const Op & tvm_bmma_sync()</div><div class="ttdoc">tvm intrinsic for tensor core bmma_sync operators. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a0cd2ac37b80c498ded412572146ecc67"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a0cd2ac37b80c498ded412572146ecc67">tvm::tir::builtin::bitwise_xor</a></div><div class="ttdeci">const Op & bitwise_xor()</div><div class="ttdoc">Bitwise xor operator. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a6aeb24a28d19cdc60e4e1fa7b420d7fd"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a6aeb24a28d19cdc60e4e1fa7b420d7fd">tvm::tir::builtin::tvm_static_handle</a></div><div class="ttdeci">const Op & tvm_static_handle()</div><div class="ttdoc">Create a function local static handle that iniitalizes to nullptr. can be used to cache function loca...</div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c">tvm::tir::builtin::TVMStructFieldKind</a></div><div class="ttdeci">TVMStructFieldKind</div><div class="ttdoc">The kind of structure field info used in intrinsic. </div><div class="ttdef"><b>Definition:</b> builtin.h:553</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1c">tvm::tir::builtin::TVMStructFieldKind</a></div><div class="ttdeci">TVMStructFieldKind</div><div class="ttdoc">The kind of structure field info used in intrinsic. </div><div class="ttdef"><b>Definition:</b> builtin.h:558</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a0e633f53c50e14d7e2fc07636a223309"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a0e633f53c50e14d7e2fc07636a223309">tvm::tir::builtin::bitwise_and</a></div><div class="ttdeci">const Op & bitwise_and()</div><div class="ttdoc">Bitwise and operator. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a6f53be295396c301082696ca0c113501"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a6f53be295396c301082696ca0c113501">tvm::tir::builtin::isnan</a></div><div class="ttdeci">const Op & isnan()</div><div class="ttdoc">Check if value is nan. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_af6d1c48570e10287683d58f22e4de98f"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#af6d1c48570e10287683d58f22e4de98f">tvm::tir::builtin::tvm_warp_shuffle_down</a></div><div class="ttdeci">const Op & tvm_warp_shuffle_down()</div></div>
@@ -155,9 +156,9 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a0117a4a76af962576a6a3bbf32f97b36"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a0117a4a76af962576a6a3bbf32f97b36">tvm::tir::builtin::tvm_call_packed_lowered</a></div><div class="ttdeci">const Op & tvm_call_packed_lowered()</div><div class="ttdoc">Lowered version of call packed, the space of value and type codes are explicitly allocated. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a322ae63444ed4e5fcf7247aa93f8bb7c"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a322ae63444ed4e5fcf7247aa93f8bb7c">tvm::tir::builtin::large_uint_imm</a></div><div class="ttdeci">const Op & large_uint_imm()</div><div class="ttdoc">See pesudo code. </div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a21c2ad8b095dcbefa786394981ea0b71"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a21c2ad8b095dcbefa786394981ea0b71">tvm::tir::builtin::tvm_context_id</a></div><div class="ttdeci">const Op & tvm_context_id()</div><div class="ttdoc">Return a unique context id, used for hint of workspace separation. Different context id ganrantees no...</div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844">tvm::tir::builtin::kArrData</a></div><div class="ttdef"><b>Definition:</b> builtin.h:556</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca9076fb1a58386bac2e0f1fdae9cab844">tvm::tir::builtin::kArrData</a></div><div class="ttdef"><b>Definition:</b> builtin.h:561</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_a26472adf05d821f1929cfbc02bc3c231"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#a26472adf05d821f1929cfbc02bc3c231">tvm::tir::builtin::shift_left</a></div><div class="ttdeci">const Op & shift_left()</div><div class="ttdoc">Left shift. </div></div>
-<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b">tvm::tir::builtin::kArrTypeCode</a></div><div class="ttdef"><b>Definition:</b> builtin.h:560</div></div>
+<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#ad3b90c881b67ebe8e8fe68f14143bb1ca5ce842cabb26975681dd561c5132af1b">tvm::tir::builtin::kArrTypeCode</a></div><div class="ttdef"><b>Definition:</b> builtin.h:565</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_aa6e23eac98abb8378b9837011a5c04b5"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#aa6e23eac98abb8378b9837011a5c04b5">tvm::tir::builtin::tvm_call_trace_packed_lowered</a></div><div class="ttdeci">const Op & tvm_call_trace_packed_lowered()</div><div class="ttdoc">Lowered version of trace intrinsic, the space of value and type codes are explicitly allocated...</div></div>
<div class="ttc" id="namespacetvm_1_1tir_1_1builtin_html_aa5b0e90771b35d78b6c07c0054abe023"><div class="ttname"><a href="namespacetvm_1_1tir_1_1builtin.html#aa5b0e90771b35d78b6c07c0054abe023">tvm::tir::builtin::isnullptr</a></div><div class="ttdeci">const Op & isnullptr()</div><div class="ttdoc">See pesudo code. </div></div>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1681,-504.374 1684.5,-514.374 1688,-504.374 1681,-504.374"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1599.85,-517.279 1610.29,-515.499 1601.02,-510.377 1599.85,-517.279"/>
+<!-- Node34->Node22 -->
+<g id="edge57" class="edge"><title>Node34->Node22</title>
+<path fill="none" stroke="midnightblue" d="M1476.76,-508.975C1457.04,-496.649 1432.55,-481.345 1417.61,-472.007"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1475.1,-512.064 1485.43,-514.396 1478.81,-506.128 1475.1,-512.064"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1541.91,-505.41 1536.26,-514.374 1546.27,-510.889 1541.91,-505.41"/>
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-<g id="edge63" class="edge"><title>Node36->Node24</title>
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+<g id="edge59" class="edge"><title>Node35->Node7</title>
+<path fill="none" stroke="midnightblue" d="M1640,-509.498C1607.7,-491.902 1557.44,-465.708 1512,-447 1469.86,-429.654 1419.42,-414.152 1387.79,-405.035"/>
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+</g>
+<!-- Node35->Node22 -->
+<g id="edge60" class="edge"><title>Node35->Node22</title>
+<path fill="none" stroke="midnightblue" d="M1605.75,-512.069C1552.8,-499.323 1481.55,-482.169 1439.43,-472.029"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1605.2,-515.538 1615.75,-514.476 1606.84,-508.733 1605.2,-515.538"/>
+</g>
+<!-- Node36->Node14 -->
+<g id="edge86" class="edge"><title>Node36->Node14</title>
+<path fill="none" stroke="midnightblue" d="M368.77,-708.036C359.917,-699.235 349.769,-688.825 341,-679 287.375,-618.916 114,-477.034 114,-396.5 114,-396.5 114,-396.5 114,-126.5 114,-65.8226 546.262,-28.1034 716.38,-15.6083"/>
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+<!-- Node36->Node22 -->
+<g id="edge62" class="edge"><title>Node36->Node22</title>
+<path fill="none" stroke="midnightblue" d="M457.634,-727.718C636.793,-721.807 1124.19,-698.025 1256,-612 1298.77,-584.088 1279.38,-549.596 1316,-514 1335.45,-495.093 1363.35,-480.659 1382.49,-472.158"/>
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<!-- Node36->Node37 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="810.018,-709.483 801.281,-715.476 811.848,-716.24 810.018,-709.483"/>
+<g id="edge63" class="edge"><title>Node36->Node37</title>
+<path fill="none" stroke="midnightblue" d="M457.629,-714.251C506.445,-703.078 571.996,-688.075 618.309,-677.475"/>
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+<path fill="none" stroke="midnightblue" d="M400.765,-705.991C404.581,-696.804 408.829,-686.578 412.153,-678.577"/>
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+<g id="edge64" class="edge"><title>Node37->Node3</title>
+<path fill="none" stroke="midnightblue" d="M741.612,-647.645C799.453,-630.35 874,-595.459 874,-530.5 874,-530.5 874,-530.5 874,-461.5 874,-414.638 840.938,-366.949 822.34,-343.83"/>
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+<!-- Node37->Node22 -->
+<g id="edge73" class="edge"><title>Node37->Node22</title>
+<path fill="none" stroke="midnightblue" d="M741.546,-647.577C783.652,-637.923 839.163,-624.809 888,-612 1075.55,-562.808 1298.97,-495.52 1376.16,-472.016"/>
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<!-- Node38 -->
<g id="node38" class="node"><title>Node38</title>
<g id="a_node38"><a xlink:href="node_2container_8h.html" target="_top" xlink:title="Array/Map container in the DSL graph. ">
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</a>
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<!-- Node37->Node38 -->
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+<g id="edge65" class="edge"><title>Node37->Node38</title>
+<path fill="none" stroke="midnightblue" d="M741.873,-653.719C837.099,-641.214 1010.65,-618.425 1104.89,-606.049"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="741.242,-650.271 731.783,-655.044 742.153,-657.212 741.242,-650.271"/>
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<!-- Node37->Node39 -->
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+<g id="edge74" class="edge"><title>Node37->Node39</title>
+<path fill="none" stroke="midnightblue" d="M607.84,-654.007C505.769,-641.099 313.389,-616.771 217.834,-604.687"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="607.669,-657.513 618.029,-655.296 608.547,-650.568 607.669,-657.513"/>
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<!-- Node37->Node40 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="979.47,-638.736 983.822,-648.396 986.444,-638.131 979.47,-638.736"/>
+<g id="edge75" class="edge"><title>Node37->Node40</title>
+<path fill="none" stroke="midnightblue" d="M614.848,-645.58C579.975,-635.022 537.107,-621.592 506.476,-611.611"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="613.871,-648.941 624.455,-648.476 615.891,-642.239 613.871,-648.941"/>
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<!-- Node41 -->
<g id="node41" class="node"><title>Node41</title>
<g id="a_node41"><a xlink:href="executable_8h.html" target="_top" xlink:title="The Relay virtual machine executable. ">
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<!-- Node37->Node41 -->
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<g id="node42" class="node"><title>Node42</title>
<g id="a_node42"><a xlink:href="runtime_2vm_2vm_8h.html" target="_top" xlink:title="The Relay virtual machine runtime. ">
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<!-- Node37->Node42 -->
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+<path fill="none" stroke="midnightblue" d="M635.397,-643.681C600.402,-624.488 552.053,-590.998 533,-545 527.727,-532.271 526.898,-526.353 533,-514 540.796,-498.219 556.151,-486.01 570.089,-477.552"/>
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<!-- Node38->Node7 -->
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+<g id="edge69" class="edge"><title>Node38->Node7</title>
+<path fill="none" stroke="midnightblue" d="M1168.5,-576.538C1167.13,-545.45 1169.1,-483.463 1202,-447 1224.18,-422.414 1258.46,-409.753 1289.15,-403.252"/>
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<!-- Node38->Node9 -->
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+<g id="edge66" class="edge"><title>Node38->Node9</title>
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<!-- Node38->Node10 -->
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+<g id="edge67" class="edge"><title>Node38->Node10</title>
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+<!-- Node38->Node17 -->
+<g id="edge68" class="edge"><title>Node38->Node17</title>
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<!-- Node38->Node34 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="1740.38,-582.894 1750.25,-586.734 1744.93,-577.574 1740.38,-582.894"/>
+<g id="edge70" class="edge"><title>Node38->Node34</title>
+<path fill="none" stroke="midnightblue" d="M1224.45,-585.029C1281.56,-574.047 1371.62,-556.726 1434.89,-544.559"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1223.69,-581.61 1214.54,-586.936 1225.02,-588.484 1223.69,-581.61"/>
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<!-- Node38->Node35 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="1718.05,-587.722 1728.61,-586.936 1719.86,-580.961 1718.05,-587.722"/>
+<g id="edge71" class="edge"><title>Node38->Node35</title>
+<path fill="none" stroke="midnightblue" d="M1257.92,-586.341C1343.22,-577.128 1476.23,-561.897 1591,-545 1593.89,-544.574 1596.84,-544.126 1599.82,-543.662"/>
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<!-- Node40->Node37 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="999.528,-621.236 995.175,-611.577 992.555,-621.842 999.528,-621.236"/>
+<g id="edge76" class="edge"><title>Node40->Node37</title>
+<path fill="none" stroke="midnightblue" d="M527.322,-614.472C562.172,-625.024 604.962,-638.43 635.546,-648.396"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="528.304,-611.112 517.72,-611.577 526.284,-617.814 528.304,-611.112"/>
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<!-- Node40->Node41 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="942.022,-581.094 952.613,-581.396 944.522,-574.555 942.022,-581.094"/>
+<g id="edge77" class="edge"><title>Node40->Node41</title>
+<path fill="none" stroke="midnightblue" d="M504.867,-576.853C525.738,-566.576 551.226,-554.025 570.414,-544.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="503.067,-573.838 495.642,-581.396 506.16,-580.118 503.067,-573.838"/>
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<!-- Node40->Node42 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="972.047,-574.861 980.404,-581.374 977.932,-571.071 972.047,-574.861"/>
+<g id="edge79" class="edge"><title>Node40->Node42</title>
+<path fill="none" stroke="midnightblue" d="M471.965,-571.385C476.65,-553.724 485.223,-530.122 500,-514 514.993,-497.643 536.494,-485.791 555.63,-477.697"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="468.469,-570.952 469.53,-581.493 475.275,-572.591 468.469,-570.952"/>
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<!-- Node41->Node42 -->
-<g id="edge81" class="edge"><title>Node41->Node42</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="843.761,-505.541 837.944,-514.396 848.013,-511.102 843.761,-505.541"/>
+<g id="edge78" class="edge"><title>Node41->Node42</title>
+<path fill="none" stroke="midnightblue" d="M599,-504.108C599,-495.154 599,-485.323 599,-477.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="595.5,-504.396 599,-514.396 602.5,-504.396 595.5,-504.396"/>
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<!-- Node43->Node36 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="701.068,-681.665 690.933,-678.577 696.93,-687.311 701.068,-681.665"/>
+<g id="edge83" class="edge"><title>Node43->Node36</title>
+<path fill="none" stroke="midnightblue" d="M285.578,-682.426C311.192,-692.823 341.626,-705.728 363.263,-715.396"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="286.609,-679.068 276.025,-678.577 283.992,-685.561 286.609,-679.068"/>
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<!-- Node44->Node42 -->
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-<path fill="none" stroke="midnightblue" d="M495.892,-642.1C475.273,-625.362 454.02,-601.216 469.5,-581 515.513,-520.909 740.493,-484.528 847.604,-470.346"/>
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+<g id="edge85" class="edge"><title>Node44->Node42</title>
+<path fill="none" stroke="midnightblue" d="M405.718,-639.262C398.591,-622.476 392.537,-599.583 401,-581 426.68,-524.616 493.848,-493.435 542.851,-477.607"/>
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<!-- Node45->Node41 -->
-<g id="edge92" class="edge"><title>Node45->Node41</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="765.316,-572.236 759.991,-581.396 769.865,-577.556 765.316,-572.236"/>
+<g id="edge89" class="edge"><title>Node45->Node41</title>
+<path fill="none" stroke="midnightblue" d="M708.689,-577.407C684.423,-567.029 654.411,-554.195 631.917,-544.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="707.447,-580.682 718.018,-581.396 710.2,-574.246 707.447,-580.682"/>
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<!-- Node45->Node42 -->
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+<g id="edge90" class="edge"><title>Node45->Node42</title>
+<path fill="none" stroke="midnightblue" d="M740.031,-572.101C730.801,-554.387 716.204,-530.388 698,-514 680.506,-498.251 657.201,-486.043 637.598,-477.579"/>
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<!-- Node46->Node7 -->
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+<g id="edge106" class="edge"><title>Node46->Node7</title>
+<path fill="none" stroke="midnightblue" d="M1153.35,-846.604C1207.81,-828.277 1276,-793.042 1276,-731.5 1276,-731.5 1276,-731.5 1276,-662.5 1276,-565.004 1276.45,-535.663 1317,-447 1324.54,-430.523 1338.55,-414.677 1347.83,-405.261"/>
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<!-- Node46->Node9 -->
-<g id="edge100" class="edge"><title>Node46->Node9</title>
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+<g id="edge97" class="edge"><title>Node46->Node9</title>
+<path fill="none" stroke="midnightblue" d="M1074.5,-840.13C1070.4,-831.749 1066.1,-822.116 1063,-813 1011.89,-662.575 988,-622.37 988,-463.5 988,-463.5 988,-463.5 988,-327.5 988,-206.581 1158.22,-155.267 1244.33,-137.045"/>
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<!-- Node46->Node10 -->
-<g id="edge101" class="edge"><title>Node46->Node10</title>
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+<g id="edge98" class="edge"><title>Node46->Node10</title>
+<path fill="none" stroke="midnightblue" d="M1153.81,-862.796C1315.54,-860.502 1724.35,-851.006 1855,-813 1927.48,-791.916 2004,-806.981 2004,-731.5 2004,-731.5 2004,-731.5 2004,-528.5 2004,-367.033 1811.38,-237.644 1756.46,-204.031"/>
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-<!-- Node46->Node14 -->
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+<!-- Node46->Node13 -->
+<g id="edge128" class="edge"><title>Node46->Node13</title>
+<path fill="none" stroke="midnightblue" d="M1020.03,-863.152C856.408,-861.664 440.078,-853.852 308,-813 161.32,-767.632 38,-751.036 38,-597.5 38,-597.5 38,-597.5 38,-126.5 38,-73.8728 202.031,-35.677 286.289,-19.5285"/>
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+<!-- Node46->Node16 -->
+<g id="edge99" class="edge"><title>Node46->Node16</title>
+<path fill="none" stroke="midnightblue" d="M1153.67,-861.978C1361.25,-856.967 1984.18,-839.759 2018,-813 2047.61,-789.572 2042,-769.26 2042,-731.5 2042,-731.5 2042,-731.5 2042,-461.5 2042,-413.697 2020.83,-359.147 2011.9,-338.261"/>
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<!-- Node46->Node17 -->
-<g id="edge102" class="edge"><title>Node46->Node17</title>
-<path fill="none" stroke="midnightblue" d="M1505.27,-863.32C1683.76,-862.266 2162.26,-855.467 2218.5,-813 2249.18,-789.831 2247.5,-769.947 2247.5,-731.5 2247.5,-731.5 2247.5,-731.5 2247.5,-461.5 2247.5,-414.946 2250.64,-359.222 2251.95,-338.144"/>
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+<g id="edge100" class="edge"><title>Node46->Node17</title>
+<path fill="none" stroke="midnightblue" d="M1154.01,-864.323C1308.91,-865.343 1687.38,-862.517 1803,-813 1893.09,-774.417 1966,-762.501 1966,-664.5 1966,-664.5 1966,-664.5 1966,-528.5 1966,-394.957 1796.99,-299.149 1740.87,-271.018"/>
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-<!-- Node46->Node18 -->
-<g id="edge103" class="edge"><title>Node46->Node18</title>
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+<!-- Node46->Node20 -->
+<g id="edge109" class="edge"><title>Node46->Node20</title>
+<path fill="none" stroke="midnightblue" d="M1020.35,-864.403C908.786,-864.471 677.853,-858.425 489,-813 339.884,-777.133 255.077,-807.269 171,-679 163.447,-667.477 164.448,-660.12 171,-648 184.894,-622.3 209.122,-635.846 226,-612 247.547,-581.557 246,-567.797 246,-530.5 246,-530.5 246,-530.5 246,-327.5 246,-240.845 527.848,-209.994 670.423,-199.931"/>
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<!-- Node46->Node22 -->
-<g id="edge112" class="edge"><title>Node46->Node22</title>
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+<g id="edge107" class="edge"><title>Node46->Node22</title>
+<path fill="none" stroke="midnightblue" d="M1153.63,-853.331C1194.28,-845.585 1246.6,-832.808 1290,-813 1347.32,-786.84 1406,-794.508 1406,-731.5 1406,-731.5 1406,-731.5 1406,-595.5 1406,-548.974 1404.74,-493.234 1404.22,-472.148"/>
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<!-- Node46->Node24 -->
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+<g id="edge108" class="edge"><title>Node46->Node24</title>
+<path fill="none" stroke="midnightblue" d="M1154.04,-855.31C1232.48,-845.633 1366.26,-828.84 1481,-813 1494.54,-811.13 1509.13,-809.023 1522.8,-807.007"/>
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<!-- Node46->Node36 -->
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+<g id="edge123" class="edge"><title>Node46->Node36</title>
+<path fill="none" stroke="midnightblue" d="M1020.27,-863.572C921.557,-862.193 731.523,-853.829 576,-813 516.085,-797.271 450.848,-764.49 416.108,-745.647"/>
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<!-- Node46->Node37 -->
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+<g id="edge124" class="edge"><title>Node46->Node37</title>
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<!-- Node46->Node38 -->
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diff --git a/docs/api/doxygen/classes.html b/docs/api/doxygen/classes.html
index faff824..d7edf4d 100644
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<table class="classindex">
<tr><td rowspan="2" valign="bottom"><a name="letter_A"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  A  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html">CostModel</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html">PlaceholderOpNode</a> (<a class="el" href="names [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html">IterAdapter</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerType.html">PointerType</a> (<a clas [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html">AccessAnalyzer</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html">CropAndResizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator.htm [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html">CostModel</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModule.html">IRModule</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html">PlaceholderOp</a> (<a class="el" href="namespacetvm_1_1te.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html">PlaceholderOpNode</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html">AccessAnalyzer</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html">CropAndResizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html">It [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html">AccessAnalyzerNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_D"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  D  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html">IteratorNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.html">PragmaStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1Sta [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1auto__scheduler_1_1AttachMapNode_1_1IterKeyHash.html">AttachMapNode::IterKeyHash</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1au [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducer.html">DataProducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html">IterMapExpr</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Add.html">Add</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html">DataProducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.html">IterMapExprNode</a> (<a class="el" href="namespacetvm_1_1arith.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AddNode.html">AddNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DataType.html">DataType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMark.html">IterMark</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arit [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADT.html">ADT</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html">DataTypePattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMarkNode.html">IterMarkNode</a> (<a class="el" href="namespacetvm_1_1a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html">ADTObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html">DataTypePatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html">IterSplitExpr</a> (<a class="el" href="n [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AffineGridAttrs.html">AffineGridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html">DebugAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html">IterSplitExprNode</a> (<a class="el" hre [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Allocate.html">Allocate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html">DeformableConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html">IterSumExpr</a> (<a class="el" href="namespac [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AllocateNode.html">AllocateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html">DenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSumExprNode.html">IterSumExprNode</a> (<a class="el" href="namespacetvm_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Allocator.html">Allocator</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DenseMapNode.html">DenseMapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVar.html">IterVar</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::t [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocStorageAttrs.html">AllocStorageAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1qnn_1_1DequantizeAttrs.html">DequantizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay_1_1qnn.html">tvm::relay::qnn</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html">IterVarAttr</a> [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocTensorAttrs.html">AllocTensorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html">DeviceAPI</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttrNode.html">IterVarAttrNode</a> (<a class="el" href= [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPattern.html">AltPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html">DeviceCopyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVarNode.html">IterVarNode</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPatternNode.html">AltPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPattern.html">DFPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html">IterVarRelation</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html">DFPatternCallback</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelationNode.html">IterVarRelationNode</a> (<a class="el" href [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1And.html">And</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html">DFPatternCallbackNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_L"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah"> [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html">ProducerRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1StorageObj.html">StorageObj</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AndNode.html">AndNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html">DFPatternFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ProducerStore.html">ProducerStore</a> (<a class="el" href="namespacetvm_1_1tir [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStep.html">AnnotationStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html">DFPatternFunctor< R(const DFPattern &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html">AnnotationStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html">DFPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html">Lay [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Any.html">Any</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html">DFPatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Layout.html">Layout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AnyNode.html">AnyNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Diagnostic.html">Diagnostic</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html">LayoutAxis</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td> [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArangeAttrs.html">ArangeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticBuilder.html">DiagnosticBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html">LayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::ti [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArgsortAttrs.html">ArgsortAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticContext.html">DiagnosticContext</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html">LayoutTransformAttrs</a> (<a class="el" href="namespac [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticContextNode.html">DiagnosticContextNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LE.html">LE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html">SimpleObjAllocator::ArrayHandler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticNode.html">DiagnosticNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html">LeakyReluAttrs</a> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html">ArrayNode</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticRenderer.html">DiagnosticRenderer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1LENode.html">LENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html">AssertStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticRendererNode.html">DiagnosticRendererNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) & [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html">AssertStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrs.html">DictAttrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   < [...]
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html">SubPixelAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html">AttachMap</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrsNode.html">DictAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetNode.html">LetNode</a> (<a class="el" href="namespacetvm_1_1tir.htm [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator.html">Iterator</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerTypeNode.html">PointerTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1auto__scheduler_1_1StageAttributes.html">StageAttributes</a> (<a class="e [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html">IteratorNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.htm [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducer.html">DataProducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1auto__scheduler_1_1AttachMapNode_1_1IterKeyHash.html">AttachMapNode [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Add.html">Add</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html">DataProducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html">IterMapExpr</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::ar [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AddNode.html">AddNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DataType.html">DataType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.html">IterMapExprNode</a> (<a class="el" href="namespacetvm_1_1arith.h [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADT.html">ADT</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html">DataTypePattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMark.html">IterMark</a> (<a class="el" href="namespacetvm_1_1arith.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html">ADTObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html">DataTypePatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMarkNode.html">IterMarkNode</a> (<a class="el" href="nam [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AffineGridAttrs.html">AffineGridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html">DebugAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html">IterSplitExpr</a> (<a class="el" href="names [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Allocate.html">Allocate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html">DeformableConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html">IterSplitExprNode</a> (<a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AllocateNode.html">AllocateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html">DenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html">IterSumExpr</a> (<a class="el" href="namespacetvm_1_1arith. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Allocator.html">Allocator</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DenseMapNode.html">DenseMapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterSumExprNode.html">IterSumExprNode</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocStorageAttrs.html">AllocStorageAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1qnn_1_1DequantizeAttrs.html">DequantizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay_1_1qnn.html">tvm::relay::qnn</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVar.html">IterVar</a> (<a cla [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocTensorAttrs.html">AllocTensorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html">DeviceAPI</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html">IterVarAttr</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPattern.html">AltPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html">DeviceCopyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttrNode.html">IterVarAttrNode</a> (<a class="el" href="names [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPatternNode.html">AltPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPattern.html">DFPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVarNode.html">IterVarNode</a> (<a class="el" href="namespacetvm_1_1t [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html">DFPatternCallback</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html">IterVarRelation</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1And.html">And</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html">DFPatternCallbackNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelationNode.html">IterVarRelationNode</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AndNode.html">AndNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html">DFPatternFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_L"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html">ProducerRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html">StorageAlignStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStep.html">AnnotationStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html">DFPatternFunctor< R(const DFPattern &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html">AnnotationStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html">DFPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html">L [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Any.html">Any</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html">DFPatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html">LayerNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AnyNode.html">AnyNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Diagnostic.html">Diagnostic</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Layout.html">Layout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td vali [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArangeAttrs.html">ArangeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticBuilder.html">DiagnosticBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html">LayoutAxis</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::ti [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArgsortAttrs.html">ArgsortAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticContext.html">DiagnosticContext</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html">LayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm:: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticContextNode.html">DiagnosticContextNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html">LayoutTransformAttrs</a> (<a class="el" href="namespace [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html">SimpleObjAllocator::ArrayHandler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticNode.html">DiagnosticNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LE.html">LE</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html">ArrayNode</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticRenderer.html">DiagnosticRenderer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html">LeakyReluAttrs</a> (<a class="el" href="namespacetvm_1_1rel [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html">AssertStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DiagnosticRendererNode.html">DiagnosticRendererNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1LENode.html">LENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html">AssertStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrs.html">DictAttrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   < [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html">AttachMap</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrsNode.html">DictAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm:: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html">AttachMapNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html">DilateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetNode.html">LetNode</a> (<a class="el" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html">AttrDocEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html">Dilation2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1LetNode.html">LetNode</a> (<a class="el" href="namespacetv [...]
+</td><td rowspan="2" valign="bottom"><a name="letter_T"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  T  </div></td></tr></table>
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html">AttachMapNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html">DilateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1LetNode.html">LetNode</a> (<a class="e [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html">AttrDocEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html">Dilation2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmt.html">LetStmt</a> (<a class="el" href="namespacetvm_ [...]
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html">TakeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html">AttrDocVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Div.html">Div</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html">LetStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::t [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1AttrError.html">AttrError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DivNode.html">DivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LinkedParam.html">LinkedParam</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrExistVisitor.html">AttrExistVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html">DominatorPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LinkedParamNode.html">LinkedParamNode</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfo.html">AttrFieldInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html">DominatorPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Load.html">Load</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfoNode.html">AttrFieldInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html">DropoutAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LoadNode.html">LoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html">AttrDocVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Div.html">Div</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmt.html">LetStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)& [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1AttrError.html">AttrError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DivNode.html">DivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html">LetStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrExistVisitor.html">AttrExistVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html">DominatorPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LinkedParam.html">LinkedParam</a> (<a class="el" hr [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1Target.html">Target</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfo.html">AttrFieldInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html">DominatorPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LinkedParamNode.html">LinkedParamNode</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfoNode.html">AttrFieldInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html">DropoutAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Load.html">Load</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html">AttrInitEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_E"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  E  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilder.html">LocalBuilder</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Range.html">Range</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TargetNode.html">TargetNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html">AttrInitVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilderNode.html">LocalBuilderNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1RangeNode.html">RangeNode</a> ( [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNonDefaultVisitor.html">AttrNonDefaultVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFunc.html">EnvFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunner.html">LocalRunner</a> (<a class="el" href="namespacetvm_1_1a [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrNopEntry.html">AttrNopEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFuncNode.html">EnvFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html">LocalRunnerNode</a> (<a class="el" href="namespacetvm_1_1au [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNormalVisitor.html">AttrNormalVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQ.html">EQ</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html">LRNAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html">AttrPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQNode.html">EQNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LT.html">LT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  &# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPatternNode.html">AttrPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Error.html">Error</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LTNode.html">LTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   < [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMap.html">AttrRegistryMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1ErrorBuilder.html">ErrorBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_M"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  M  </div></td></tr [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html">RecordReaderNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorComputeOp.html">TensorComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ErrorReporter.html">ErrorReporter</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html">RecordToFile</a> (<a class="el" href="namespacetvm_1_1aut [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Evaluate.html">Evaluate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Map.html">Map</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="clas [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html">EvaluateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MapNode.html">MapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html">Executable</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> (<a class="el [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSHashVisitor.html">AttrsSHashVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MatchNode.html">MatchNode</a> (<a class="el" hre [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html">AttrStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html">MatrixSetDiagAttrs</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Max.html">Max</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MaxNode.html">MaxNode</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool1DA [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AutoSchedulerLayoutTransformAttrs.html">AutoSchedulerLayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </ [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html">MaxPool3DAttrs</a> (<a class="el" href="namespace [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html">MeasureCallback</a> (<a class="el [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html">ExprPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallbackNode.html">MeasureCallbackNode</a> (<a c [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LoadNode.html">LoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1RampNode.html">RampNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TargetKindNode.html">TargetKindNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html">AttrInitVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilder.html">LocalBuilder</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html">Ra [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNonDefaultVisitor.html">AttrNonDefaultVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFunc.html">EnvFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilderNode.html">LocalBuilderNode</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrNopEntry.html">AttrNopEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFuncNode.html">EnvFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunner.html">LocalRunner</a> (<a class="el" href="namespacetvm_1_1auto__sche [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNormalVisitor.html">AttrNormalVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQ.html">EQ</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html">LocalRunnerNode</a> (<a class="el" href="namespac [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html">AttrPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQNode.html">EQNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html">LRNAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPatternNode.html">AttrPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Error.html">Error</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LT.html">LT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMap.html">AttrRegistryMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1ErrorBuilder.html">ErrorBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LTNode.html">LTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td va [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ErrorReporter.html">ErrorReporter</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_M"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  M&# [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html">RecClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Tensor.html">Tensor</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Evaluate.html">Evaluate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html">RecordReader</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_schedu [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html">EvaluateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Map.html">Map</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html">Executable</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MapNode.html">MapNode</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSHashVisitor.html">AttrsSHashVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> (<a class="el" href="names [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html">AttrStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MatchNode.html">MatchNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::r [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html">MatrixSetDiagAttrs</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Max.html">Max</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MaxNode.html" [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AutoSchedulerLayoutTransformAttrs.html">AutoSchedulerLayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html">MaxPool2DAttrs</a> (<a class="el" href="namespace [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html">MaxPool3DAttrs</a> (<a class="el" href="nam [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html">ExprPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html">MeasureCallback</a> (<a class="el [...]
<tr><td rowspan="2" valign="bottom"><a name="letter_B"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  B  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html">ExprPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html">MeasureInput</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefWrite.html">RefWrite</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html">ExprRewriter</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html">MeasureInputNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html">RefWriteNode [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseAttrsNode.html">BaseAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResult.html">MeasureResult</a> (<a class="el" href="namespacetvm_1_1auto__schedule [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html">BaseComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResultNode.html">MeasureResultNode</a> (<a class="el [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExpr.html">BaseExpr</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOp.html">ExternOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfo.html">MemoryInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a clas [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExprNode.html">BaseExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html">ExternOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfoNode.html">MemoryInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td> [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html">ExprPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallbackNode.html">MeasureCallbackNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html"> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html">ExprRewriter</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html">MeasureInput</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefValue.html">RefValue</a> (<a class=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseAttrsNode.html">BaseAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html">MeasureInputNode</a> (<a class="el" href="namespacetvm_1_1au [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html">BaseComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResult.html">MeasureResult</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExpr.html">BaseExpr</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOp.html">ExternOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResultNode.html">MeasureResultNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExprNode.html">BaseExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html">ExternOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfo.html">MemoryInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td vali [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFunc.html">BaseFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_F"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  F  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1MemoryManager.html">MemoryManager</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RelayNode.html">RelayNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html">TupleGetItemPattern [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFuncNode.html">BaseFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html">MeshgridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1RelayRefType.html">RelayRefType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorType.html">BaseTensorType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html">FeatureSet</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Min.html">Min</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)    [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorTypeNode.html">BaseTensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html">FIFOBufferAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MinNode.html">MinNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueEqual.html">BaseValueEqual</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html">FixedPointMultiplyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html">MirrorPadAttrs</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueHash.html">BaseValueHash</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html">SeqStmt::Flattener</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html">MixedModeMutator</a> (<a class="el" href="namespacetvm_1_1re [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html">BatchNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html">MixedModeVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html">BatchToSpaceNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImmNode.html">FloatImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mod.html">Mod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&# [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html">BiasAddAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html">FloorDiv</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ModNode.html">ModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html">BijectiveLayout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html">FloorDivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> (<a class="el" href="namespacetvm_1_1arith [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html">BijectiveLayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorMod.html">FloorMod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> (<a class="el" href="names [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html">BinaryConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> (<a class="el" href="na [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html">BinaryDenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html">FollowFusedSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html">BinaryOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html">FollowFusedSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html">Mo [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html">BitPackAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html">FollowSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" h [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Bool.html">Bool</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html">FollowSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</a> (<a class="el" href="namespacetvm_1_1tir.h [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Broadcast.html">Broadcast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">MultiBoxPriorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::r [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html">BroadcastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBoxTransformLocAttrs</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html">FrameBuffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_N"></a><table border="0" [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html">RfactorStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structTVMParallelGroupEnv.html">TVMParallelGroupEnv</a>   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Framer.html">Framer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html">ROIAlignAttrs</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html">BufferLoad</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html">StringObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> (<a class="el" href="namespace [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Function.html">Function</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html">NdarraySizeAttrs</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html">FunctionNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NE.html">NE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncType.html">FuncType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NENode.html">NENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td> [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1TypeCall.html">TypeCall</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncTypeNode.html">FuncTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor.html">NodeFunctor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Fuse.html">Fuse</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html">NodeFunctor< R(const ObjectRef & [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html">NonMaximumSuppressionAttrs</a> (<a class="el" href= [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html">BuildResult</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html">FuseStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Not.html">Not</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html">BuildResultNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html">FuseStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NotNod [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfoNode.html">MemoryInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Registry.html">Registry</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html">TupleGetItemNode</a> (<a class="el" href="namespacetvm_1_1relay.htm [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorType.html">BaseTensorType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html">FeatureSet</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structMemoryManagerInterface.html">MemoryManagerInterface</a>   </td><td valign="top"><a class="el" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorTypeNode.html">BaseTensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html">FIFOBufferAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html">MeshgridAttrs</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueEqual.html">BaseValueEqual</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html">FixedPointMultiplyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Min.html">Min</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueHash.html">BaseValueHash</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html">SeqStmt::Flattener</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MinNode.html">MinNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a> [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchMatmulAttrs.html">BatchMatmulAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html">MirrorPadAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tv [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html">BatchNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImmNode.html">FloatImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html">MixedModeMutator</a> (<a class="el" href="namespacetvm_1_1relay.h [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html">BatchToSpaceNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html">FloorDiv</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html">MixedModeVisitor</a> (<a class="el" href="na [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html">BiasAddAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html">FloorDivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mod.html">Mod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html">BijectiveLayout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorMod.html">FloorMod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ModNode.html">ModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html">BijectiveLayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> (<a class="el" href="namespacetvm_ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html">BinaryConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html">FollowFusedSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetAn [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html">BinaryDenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html">FollowFusedSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1Modula [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html">BinaryOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html">FollowSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">Module</a> (<a class="el [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html">BitPackAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html">FollowSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html">Modul [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Bool.html">Bool</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Broadcast.html">Broadcast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html">BroadcastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html">FrameBuffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">Mul [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Framer.html">Framer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAtt [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html">StringObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_N"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class= [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html">RfactorStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structTVMParallelGroupEnv.html">TVMParallelGroupEnv</a>   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html">BufferLoad</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Function.html">Function</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html">RfactorStepNode</a> (<a class="el" href="namespacetvm_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html">FunctionNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> (<a class="el" href="namespacetvm_1_1runti [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html">FunctionPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html">NdarraySizeAttrs</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionPatternNode.html">FunctionPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NE.html">NE</a> (<a class="el" href="namespacetvm_1_1tir.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncType.html">FuncType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NENode.html">NENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncTypeNode.html">FuncTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor.html">NodeFunctor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td r [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1TypeCallNode.html">TypeCallNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Fuse.html">Fuse</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html">NodeFunctor< R(const ObjectR [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html">BuildResult</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html">NonMaximumSuppressionAtt [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html">BuildResultNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html">FuseStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Not.html">Not< [...]
<tr><td rowspan="2" valign="bottom"><a name="letter_C"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  C  </div></td></tr></table>
-</td><td rowspan="2" valign="bottom"><a name="letter_G"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  G  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterNDAttrs.html">ScatterNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypedEnvFunc.html">TypedEnvFunc</a> (<a class="el" href="namespace [...]
-<tr><td rowspan="2" valign="bottom"><a name="letter_O"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  O  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html">TypedEnvFunc< R(Args...)></a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html">CacheReadStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ScheduleNode.html">ScheduleNode</a> (<a c [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html">CacheReadStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html">CacheWriteStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html">CacheWriteStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespacetvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html">GlobalPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class="el" href="namespa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVar.html">GlobalTypeVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::ru [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> (<a class="el" href="namespacetvm_1_1runtime.html">tv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html">ObjectTypeChecker</a> (<a class="el" href="namespacetvm_1_1runtime [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVarNode.html">GlobalVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker< Ar [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Cast.html">Cast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html">ObjectTypeChecker< Map [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namespac [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GT.html">GT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::re [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CastNode.html">CastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Op.html">Op</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a cl [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_H"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  H  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectVisitAttrs_3_01T_00_01TraitName_00_01false_01_4.html">SelectVisitAttrs< T, TraitName, false ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1TypePattern.html">Ty [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html">ClauseNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Operation.html">Operation</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html">SeqStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html">ClipAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html">SimpleObjAllocator::Handler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1OperationNode.html">OperationNode</a> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Closure.html">Closure</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html">SEqualReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html">OpImplementation</a> (<a class="el" href="namespacet [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ClosureObj.html">ClosureObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html">OpImplementationNode</a> (<a class="el" href [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLContext_01_4.html">Handler< DLContext ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a class="el" hre [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDataType_01_4.html">Handler< DLDataType ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEntry</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html">CommReducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html">HardwareParams</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html">OpSpecializa [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html">HardwareParamsNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.htm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html">ComputeAtStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOp.html">HybridOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html">OpStrategy</a> (<a class="el" href="n [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html">FuseStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NotNode.html">NotNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterAddAttrs.html">ScatterAddAttrs</a> (<a class=" [...]
+<tr><td rowspan="2" valign="bottom"><a name="letter_G"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  G  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html">ScatterAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypedEnvFunc.html">TypedEnvFunc</a> (<a class="el" href="namespacetvm. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html">CacheReadStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_O"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  O  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterNDAttrs.html">ScatterNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html">TypedEnvFunc< R(Args...)></a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html">CacheReadStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html">CacheWriteStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html">CacheWriteStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>) [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html">GlobalPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class="el" href="n [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVar.html">GlobalTypeVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">t [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> (<a class="el" href="namespacetvm_1_1runtime. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html">ObjectTypeChecker</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Cast.html">Cast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVarNode.html">GlobalVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker< Array< T > ></a> (<a class="e [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html">ObjectTy [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CastNode.html">CastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GT.html">GT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)  [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Op.html">Op</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html">ClauseNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_H"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  H  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectVisitAttrs.html">SelectVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1TypePattern.html">TypePattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html">ClipAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Operation.html">Operation</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectVisitAttrs_3_01T_00_01TraitName_00_01false_01_4.html">SelectVisitAttrs< T, Tr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Closure.html">Closure</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html">SimpleObjAllocator::Handler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1OperationNode.html">OperationNode</a> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ClosureObj.html">ClosureObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html">SEqualReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html">OpImplementation</a> (<a class="el" href="name [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html">OpImplementationNode</a> (<a class="el" href="namespacetvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLContext_01_4.html">Handler< DLContext ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a class="el" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html">CommReducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDataType_01_4.html">Handler< DLDataType ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEntry< [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html">HardwareParams</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html">OpSpecial [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html">ComputeAtStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html">HardwareParamsNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOp.html">HybridOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html">OpStrategy</a> (<a class="el" [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html">HybridOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html">OpStrategyNode</a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_I"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  I  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html">ShapePatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Unframer.html">Unframer</a> (<a class="el" hr [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html">ComputeDAGNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Or.html">Or</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer.html">SHashReducer</a> (<a class="el" href="namespacetvm. [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Id.html">Id</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1OrNode.html">OrNode</a> (<a class="el" href="namesp [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IdNode.html">IdNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_P"></a><table border="0" cellspacing="0" ce [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html">ShuffleNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_V"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  V  </div></td></tr></table>
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html">HybridOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html">OpStrategyNode</a> (<a class="e [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html">ComputeDAGNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_I"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  I  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html">ShapePattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1Unframer.html">Unframer</a> (<a class="el" href="name [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Or.html">Or</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html">ShapePatternNode</a> (<a class=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Id.html">Id</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1OrNode.html">OrNode</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IdNode.html">IdNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_P"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  P   [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Shuffle.html">Shuffle</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_V"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  V  </div></td></tr></table>
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1If.html">If</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator.html">SimpleObjAllocator</a> (<a class="el" href="namespacetvm_1_1runtime.html">tv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfNode.html">IfNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">PackedFunc</a> (<a class="el" href="namespacetvm_1_1runtime.html">tv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html">ComputeRootStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html">IfThenElse</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html">PackedFun [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html">ComputeRootStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html">IfThenElseNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html">ConcatenateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce.html">ImplSEqualReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.h [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce_3_01T_00_01true_01_4.html">ImplSEqualReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverte [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html">ConstantNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce.html">ImplSHashReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4.html"> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html">ConstantPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncV [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html">ConstantPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1PacketFieldSizeBytes.html">Pac [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html">ConstIntBound</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html">PadA [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pass.html">Pass</a> (<a class="el" href="namespacetvm_1_1transform. [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class="el [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a class="el" href="namespacetvm_1_1transform.ht [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoNode</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassNode.html">PassNode</a> (<a cla [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1If.html">If</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html">ShuffleNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html">ComputeRootStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfNode.html">IfNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">PackedFunc</a> (<a class=" [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html">ComputeRootStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html">IfThenElse</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html">P [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html">ConcatenateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html">IfThenElseNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01T_01_4_01_4.html [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce.html">ImplSEqualReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html">PackedFuncVa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html">ConstantNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce_3_01T_00_01true_01_4.html">ImplSEqualReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValue [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html">ConstantPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce.html">ImplSHashReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4. [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html">ConstantPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1Pac [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html">ConstIntBound</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1PacketFieldSizeBytes.html">PacketFieldSize [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Pad [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pass.html">Pass</a> (<a class="el" href="namespacetvm_1_1transform.html">tv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a class="el" href="namespacetvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class="el" href="namespacetvm_1_1transform.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a class="el" href="namespacetvm_1_1tran [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoNode</a> (<a class [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassNode.html">PassNode</a> [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Pattern.html">Pattern</a> (<a class="el" href="na [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html">IntConstraintsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html">PatternCons [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html">IntConstraintsTransform</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructor [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html">PatternFunctor</a> [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html">PatternFunctor [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html">Conv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html">PatternMutator</a> (<a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Pattern.html">Pattern</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html">IntConstraintsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html">Pat [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html">IntConstraintsTransform</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html">PatternConstructor [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html"> [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html">Conv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html">PatternFunctor< R(const Patte [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html">PatternMutator</a> [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternNode.html">PatternNode</a> [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html">Conv2DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html">PatternTuple</a> (<a class="el [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html">Conv2DWinogradNNPACKWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html">Conv2DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternNode.html">PatternNode</a> ( [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html">Conv2DWinogradNNPACKWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Pattern [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html">Conv3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html">PatternTupleNode</a> (<a class="el [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html">Conv3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)  [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html">Conv3DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">PatternVarNode</a> (<a class="el" href="namespacetvm_1_1rel [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html">Conv3DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html">PatternVisitor</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html">ConvGemmWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWi [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html">ConvWinogradWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html">IntSetNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">Patte [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModule.html">IRModule</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html">PlaceholderOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a> [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html">Conv3DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::re [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html">Conv3DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">PatternVarNode</a> (<a class="el" href="namespacetvm_1_1relay [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html">ConvGemmWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html">PatternVisitor</a> (<a cla [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html">ConvWinogradWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">P [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html">IntSetNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode</a> (<a class="el [...]
<tr><td></td><td></td><td></td><td></td><td></td></tr>
</table>
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