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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/11/04 14:49:37 UTC
[incubator-tvm-site] branch asf-site updated: Docs build at Wed Nov
4 09:48:50 EST 2020
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new fd7e59b Docs build at Wed Nov 4 09:48:50 EST 2020
fd7e59b is described below
commit fd7e59b7e20df0b9617462a9ab8aa7951d704ce2
Author: tqchen <ti...@gmail.com>
AuthorDate: Wed Nov 4 09:48:53 2020 -0500
Docs build at Wed Nov 4 09:48:50 EST 2020
---
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docs/contribute/release_process.html | 174 +-
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docs/deploy/tensorrt.html | 174 +-
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docs/dev/debugger.html | 174 +-
docs/dev/frontend/tensorflow.html | 174 +-
docs/dev/how_to.html | 172 +-
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docs/dev/inferbound.html | 174 +-
docs/dev/introduction_to_module_serialization.html | 178 +-
docs/dev/pass_infra.html | 180 +-
docs/dev/relay_add_op.html | 174 +-
docs/dev/relay_add_pass.html | 174 +-
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docs/dev/relay_intro.html | 174 +-
docs/dev/relay_op_strategy.html | 174 +-
docs/dev/runtime.html | 174 +-
docs/dev/security.html | 174 +-
docs/dev/virtual_machine.html | 174 +-
docs/faq.html | 172 +-
docs/genindex.html | 258 ++-
docs/index.html | 172 +-
docs/install/docker.html | 174 +-
docs/install/from_source.html | 174 +-
docs/install/index.html | 172 +-
docs/install/nnpack.html | 176 +-
docs/langref/hybrid_script.html | 174 +-
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docs/langref/relay_expr.html | 174 +-
docs/langref/relay_op.html | 174 +-
docs/langref/relay_pattern.html | 174 +-
docs/langref/relay_type.html | 174 +-
docs/objects.inv | Bin 17193 -> 17289 bytes
docs/py-modindex.html | 170 +-
docs/search.html | 168 +-
docs/searchindex.js | 2 +-
.../auto_scheduler/sg_execution_times.html | 176 +-
.../auto_scheduler/tune_conv2d_layer_cuda.html | 546 +++--
docs/tutorials/auto_scheduler/tune_matmul_x86.html | 224 ++-
docs/tutorials/autotvm/sg_execution_times.html | 184 +-
docs/tutorials/autotvm/tune_conv2d_cuda.html | 221 ++-
docs/tutorials/autotvm/tune_relay_arm.html | 183 +-
docs/tutorials/autotvm/tune_relay_cuda.html | 183 +-
docs/tutorials/autotvm/tune_relay_mobile_gpu.html | 183 +-
docs/tutorials/autotvm/tune_relay_x86.html | 177 +-
docs/tutorials/autotvm/tune_simple_template.html | 197 +-
docs/tutorials/dev/bring_your_own_datatypes.html | 182 +-
docs/tutorials/dev/low_level_custom_pass.html | 174 +-
docs/tutorials/dev/sg_execution_times.html | 178 +-
docs/tutorials/dev/use_pass_infra.html | 276 ++-
docs/tutorials/frontend/build_gcn.html | 178 +-
.../frontend/deploy_model_on_android.html | 182 +-
docs/tutorials/frontend/deploy_model_on_rasp.html | 180 +-
.../frontend/deploy_object_detection_pytorch.html | 206 +-
docs/tutorials/frontend/deploy_prequantized.html | 188 +-
.../frontend/deploy_prequantized_tflite.html | 180 +-
docs/tutorials/frontend/deploy_quantized.html | 182 +-
docs/tutorials/frontend/deploy_sparse.html | 176 +-
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docs/tutorials/frontend/from_caffe2.html | 176 +-
docs/tutorials/frontend/from_coreml.html | 180 +-
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docs/tutorials/frontend/from_mxnet.html | 176 +-
docs/tutorials/frontend/from_onnx.html | 186 +-
docs/tutorials/frontend/from_pytorch.html | 182 +-
docs/tutorials/frontend/from_tensorflow.html | 178 +-
docs/tutorials/frontend/from_tflite.html | 176 +-
docs/tutorials/frontend/sg_execution_times.html | 210 +-
docs/tutorials/frontend/using_external_lib.html | 176 +-
.../get_started/cross_compilation_and_rpc.html | 180 +-
docs/tutorials/get_started/relay_quick_start.html | 280 ++-
docs/tutorials/get_started/sg_execution_times.html | 180 +-
.../get_started/tensor_expr_get_started.html | 180 +-
.../get_started/tvmc_command_line_driver.html | 174 +-
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docs/tutorials/language/extern_op.html | 174 +-
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docs/tutorials/language/tedd.html | 174 +-
docs/tutorials/language/tensorize.html | 200 +-
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docs/tutorials/optimize/opt_conv_cuda.html | 176 +-
docs/tutorials/optimize/opt_conv_tensorcore.html | 176 +-
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.../optimize/opt_matmul_auto_tensorcore.html | 174 +-
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docs/tutorials/topi/intro_topi.html | 176 +-
docs/tutorials/topi/sg_execution_times.html | 174 +-
docs/vta/dev/config.html | 176 +-
docs/vta/dev/hardware.html | 188 +-
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docs/vta/install.html | 176 +-
docs/vta/tutorials/autotvm/sg_execution_times.html | 174 +-
docs/vta/tutorials/autotvm/tune_relay_vta.html | 367 ++--
.../tutorials/frontend/deploy_classification.html | 198 +-
.../vta/tutorials/frontend/sg_execution_times.html | 174 +-
docs/vta/tutorials/index.html | 174 +-
docs/vta/tutorials/matrix_multiply.html | 190 +-
docs/vta/tutorials/optimize/convolution_opt.html | 190 +-
.../tutorials/optimize/matrix_multiply_opt.html | 192 +-
.../vta/tutorials/optimize/sg_execution_times.html | 176 +-
docs/vta/tutorials/sg_execution_times.html | 176 +-
docs/vta/tutorials/vta_get_started.html | 190 +-
2932 files changed, 157383 insertions(+), 95357 deletions(-)
diff --git a/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py b/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py
index 357abf1..b5167b3 100644
--- a/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py
+++ b/docs/_downloads/0bb862dbb3a4c434477f93fe2c147fbb/tune_simple_template.py
@@ -26,6 +26,10 @@ The first step is defining a search space.
The second step is running a search algorithm to explore through this space.
In this tutorial, you can learn how to perform these two steps in TVM.
The whole workflow is illustrated by a matrix multiplication example.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
######################################################################
diff --git a/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb b/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb
index 7af6ec2..b49ccad 100644
--- a/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb
+++ b/docs/_downloads/0d95a85fc279fdff660608ef305b9107/tune_simple_template.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nWriting tunable template and Using auto-tuner\n=============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_\n\nThis is an introduction tutorial to the auto-tuning module in TVM.\n\nThere are two steps in auto-tuning.\nThe first step is defining a search space.\nThe second step is running a search algorithm to explore through this space.\nIn this tutorial, you can learn how to perform these two steps in TVM.\nThe whole workflow is [...]
+ "\nWriting tunable template and Using auto-tuner\n=============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_\n\nThis is an introduction tutorial to the auto-tuning module in TVM.\n\nThere are two steps in auto-tuning.\nThe first step is defining a search space.\nThe second step is running a search algorithm to explore through this space.\nIn this tutorial, you can learn how to perform these two steps in TVM.\nThe whole workflow is [...]
]
},
{
diff --git a/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py b/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
index 71a8f67..593ac3c 100644
--- a/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
+++ b/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
@@ -40,7 +40,7 @@ from tvm import te
import vta
import numpy as np
from tvm import rpc
-from tvm.contrib import util
+from tvm.contrib import utils
from vta.testing import simulator
# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
@@ -86,7 +86,7 @@ elif env.TARGET in ["sim", "tsim"]:
# The last operation is a cast and copy back to DRAM, into results tensor
# :code:`C`.
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/gemm_dataflow.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/gemm_dataflow.png
# :align: center
######################################################################
@@ -107,7 +107,7 @@ elif env.TARGET in ["sim", "tsim"]:
# adding the result matrix to an accumulator matrix, as shown in the
# figure below.
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/tensor_core.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/tensor_core.png
# :align: center
# :width: 480px
#
@@ -126,7 +126,7 @@ elif env.TARGET in ["sim", "tsim"]:
# contiguous.
# The resulting tiled tensor has a shape of (2, 4, 2, 2).
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/data_tiling.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/data_tiling.png
# :align: center
# :width: 480px
#
@@ -389,7 +389,7 @@ print(vta.lower(s, [A, B, C], simple_mode=True))
my_gemm = vta.build(s, [A, B, C], "ext_dev", env.target_host, name="my_gemm")
# Write the compiled module into an object file.
-temp = util.tempdir()
+temp = utils.tempdir()
my_gemm.save(temp.relpath("gemm.o"))
# Send the executable over RPC
diff --git a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
index 5a77399..8353b85 100644
--- a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
+++ b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nTuning High Performance Convolution on NVIDIA GPUs\n=========================================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_\n\nThis is an advanced tutorial for writing high performance tunable template for\nNVIDIA GPU. By running auto-tuner on this template, we can outperform the\nvendor provided library CuDNN in many cases.\n\n"
+ "\nTuning High Performance Convolution on NVIDIA GPUs\n=========================================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_\n\nThis is an advanced tutorial for writing high performance tunable template for\nNVIDIA GPU. By running auto-tuner on this template, we can outperform the\nvendor provided library CuDNN in many cases.\n\nNote that this tutorial will not run on Windows or recent versions of macOS. To\nget it [...]
]
},
{
diff --git a/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb b/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
index 188ce15..1068889 100644
--- a/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
+++ b/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
@@ -105,7 +105,7 @@
},
"outputs": [],
"source": [
- "# save the graph, lib and params into separate files\nfrom tvm.contrib import util\n\ntemp = util.tempdir()\npath_lib = temp.relpath(\"deploy_lib.tar\")\nlib.export_library(path_lib)\nprint(temp.listdir())"
+ "# save the graph, lib and params into separate files\nfrom tvm.contrib import utils\n\ntemp = utils.tempdir()\npath_lib = temp.relpath(\"deploy_lib.tar\")\nlib.export_library(path_lib)\nprint(temp.listdir())"
]
},
{
diff --git a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
index 4636103..f9b8921 100644
--- a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
+++ b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
@@ -33,6 +33,10 @@ these operators, it will query this log file to get the best knob values.
We also released pre-tuned parameters for some NVIDIA GPUs. You can go to
`NVIDIA GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#nvidia-gpu>`_
to see the results.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
######################################################################
@@ -65,7 +69,7 @@ from tvm import autotvm
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
-from tvm.contrib.util import tempdir
+from tvm.contrib.utils import tempdir
import tvm.contrib.graph_runtime as runtime
#################################################################
@@ -313,7 +317,7 @@ def tune_and_evaluate(tuning_opt):
# import logging
# logging.getLogger('autotvm').setLevel(logging.DEBUG)
#
-# Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
#################################################################
diff --git a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
index b867ec4..52ce8d8 100644
--- a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
+++ b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nAuto-tuning a convolutional network for Mobile GPU\n==================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Eddie Yan <https://github.com/eqy>`_\n\nAuto-tuning for a specific device is critical for getting the best\nperformance. This is a tutorial about how to tune a whole convolutional\nnetwork.\n\nThe operator implementation for Mobile GPU in TVM is written in template form.\nThe template has many tunable knobs (tile [...]
+ "\nAuto-tuning a convolutional network for Mobile GPU\n==================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Eddie Yan <https://github.com/eqy>`_\n\nAuto-tuning for a specific device is critical for getting the best\nperformance. This is a tutorial about how to tune a whole convolutional\nnetwork.\n\nThe operator implementation for Mobile GPU in TVM is written in template form.\nThe template has many tunable knobs (tile [...]
]
},
{
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "import os\n\nimport numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm import autotvm\nfrom tvm import relay\nimport tvm.relay.testing\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.contrib.util import tempdir\nimport tvm.contrib.graph_runtime as runtime"
+ "import os\n\nimport numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm import autotvm\nfrom tvm import relay\nimport tvm.relay.testing\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.contrib.utils import tempdir\nimport tvm.contrib.graph_runtime as runtime"
]
},
{
@@ -140,7 +140,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
]
}
],
diff --git a/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py b/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
index ce9c198..b307077 100644
--- a/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
+++ b/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
@@ -22,6 +22,10 @@ Tuning High Performance Convolution on NVIDIA GPUs
This is an advanced tutorial for writing high performance tunable template for
NVIDIA GPU. By running auto-tuner on this template, we can outperform the
vendor provided library CuDNN in many cases.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
######################################################################
diff --git a/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb b/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb
index 289d2b4..7a4a5ca 100644
--- a/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb
+++ b/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb
@@ -33,14 +33,14 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.ge [...]
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import utils\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.g [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Computation Declaration\n-----------------------\nAs a first step, we need to describe our matrix multiplication computation.\nWe define the matrix multiplication as the computation one would find in a\nfully connected layer, defined by its batch size, input channels, and output\nchannels.\nThese have to be integer multiples of the VTA tensor shape:\n:code:`BATCH`, :code:`BLOCK_IN`, and :code:`BLOCK_OUT` respectively.\n\nWe've added extra operators to the matrix multiplication t [...]
+ "Computation Declaration\n-----------------------\nAs a first step, we need to describe our matrix multiplication computation.\nWe define the matrix multiplication as the computation one would find in a\nfully connected layer, defined by its batch size, input channels, and output\nchannels.\nThese have to be integer multiples of the VTA tensor shape:\n:code:`BATCH`, :code:`BLOCK_IN`, and :code:`BLOCK_OUT` respectively.\n\nWe've added extra operators to the matrix multiplication t [...]
]
},
{
@@ -76,7 +76,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Blocking the Computation\n~~~~~~~~~~~~~~~~~~~~~~~~\nThe matrix multiplication is by default too large for activations or weights\nto fit on VTA's on-chip buffers all at once.\nWe block the (1, 1024) by (1024, 1024) matrix multiplication into\nsmaller (1, 256) by (256, 256) matrix multiplications so the intermediate\ntensors can fit on the accelerator's on-chip SRAM.\nThis approach is similar to blocking techniques applied to CPUs and GPUs in\norder to increase cache hit rate.\n\ [...]
+ "Blocking the Computation\n~~~~~~~~~~~~~~~~~~~~~~~~\nThe matrix multiplication is by default too large for activations or weights\nto fit on VTA's on-chip buffers all at once.\nWe block the (1, 1024) by (1024, 1024) matrix multiplication into\nsmaller (1, 256) by (256, 256) matrix multiplications so the intermediate\ntensors can fit on the accelerator's on-chip SRAM.\nThis approach is similar to blocking techniques applied to CPUs and GPUs in\norder to increase cache hit rate.\n\ [...]
]
},
{
@@ -141,7 +141,7 @@
},
"outputs": [],
"source": [
- "# Compile the TVM module\nmy_gemm = vta.build(s, [data, weight, res], \"ext_dev\", env.target_host, name=\"my_gemm\")\ntemp = util.tempdir()\nmy_gemm.save(temp.relpath(\"gemm.o\"))\nremote.upload(temp.relpath(\"gemm.o\"))\nf = remote.load_module(\"gemm.o\")\n\n# Get the remote device context\nctx = remote.ext_dev(0)\n\n# Initialize the data and weight arrays randomly in the int range of (-128, 128]\ndata_np = np.random.randint(-128, 128, size=(batch_size, in_channels)).astype(da [...]
+ "# Compile the TVM module\nmy_gemm = vta.build(s, [data, weight, res], \"ext_dev\", env.target_host, name=\"my_gemm\")\ntemp = utils.tempdir()\nmy_gemm.save(temp.relpath(\"gemm.o\"))\nremote.upload(temp.relpath(\"gemm.o\"))\nf = remote.load_module(\"gemm.o\")\n\n# Get the remote device context\nctx = remote.ext_dev(0)\n\n# Initialize the data and weight arrays randomly in the int range of (-128, 128]\ndata_np = np.random.randint(-128, 128, size=(batch_size, in_channels)).astype(d [...]
]
},
{
diff --git a/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb b/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb
index 52f233d..2380bf8 100644
--- a/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb
+++ b/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb
@@ -44,7 +44,7 @@
},
"outputs": [],
"source": [
- "weights_url = \"\".join(\n [\n \"https://github.com/fchollet/deep-learning-models/releases/\",\n \"download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\",\n ]\n)\nweights_file = \"resnet50_weights.h5\"\nweights_path = download_testdata(weights_url, weights_file, module=\"keras\")\nkeras_resnet50 = keras.applications.resnet50.ResNet50(\n include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000\n)\nkeras_resnet50.load_weights(weights_path)"
+ "if tuple(keras.__version__.split(\".\")) < (\"2\", \"4\", \"0\"):\n weights_url = \"\".join(\n [\n \"https://github.com/fchollet/deep-learning-models/releases/\",\n \"download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\",\n ]\n )\n weights_file = \"resnet50_keras_old.h5\"\nelse:\n weights_url = \"\".join(\n [\n \" https://storage.googleapis.com/tensorflow/keras-applications/\",\n \"resnet/resne [...]
]
},
{
diff --git a/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py b/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py
index 0ca2243..7f1bb6a 100644
--- a/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py
+++ b/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py
@@ -210,9 +210,9 @@ else:
# - cc.create_shared calls a compiler (gcc) to create a shared library
#
from tvm.contrib import cc
-from tvm.contrib import util
+from tvm.contrib import utils
-temp = util.tempdir()
+temp = utils.tempdir()
fadd.save(temp.relpath("myadd.o"))
if tgt == "cuda":
fadd.imported_modules[0].save(temp.relpath("myadd.ptx"))
diff --git a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
index 75ce1f7..d846f69 100644
--- a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
+++ b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "import os\nimport numpy as np\nfrom PIL import Image\nimport keras\nfrom keras.applications.mobilenet_v2 import MobileNetV2\nimport tvm\nfrom tvm import te\nimport tvm.relay as relay\nfrom tvm import rpc\nfrom tvm.contrib import util, ndk, graph_runtime as runtime\nfrom tvm.contrib.download import download_testdata"
+ "import os\nimport numpy as np\nfrom PIL import Image\nimport keras\nfrom keras.applications.mobilenet_v2 import MobileNetV2\nimport tvm\nfrom tvm import te\nimport tvm.relay as relay\nfrom tvm import rpc\nfrom tvm.contrib import utils, ndk, graph_runtime as runtime\nfrom tvm.contrib.download import download_testdata"
]
},
{
@@ -119,7 +119,7 @@
},
"outputs": [],
"source": [
- "local_demo = True\n\n# by default on CPU target will execute.\n# select 'cpu', 'opencl' and 'vulkan'\ntest_target = \"cpu\"\n\n# Change target configuration.\n# Run `adb shell cat /proc/cpuinfo` to find the arch.\narch = \"arm64\"\ntarget = \"llvm -mtriple=%s-linux-android\" % arch\ntarget_host = None\n\nif local_demo:\n target_host = None\n target = \"llvm\"\nelif test_target == \"opencl\":\n target_host = target\n target = \"opencl\"\nelif test_target == \"vulkan\" [...]
+ "local_demo = True\n\n# by default on CPU target will execute.\n# select 'cpu', 'opencl' and 'vulkan'\ntest_target = \"cpu\"\n\n# Change target configuration.\n# Run `adb shell cat /proc/cpuinfo` to find the arch.\narch = \"arm64\"\ntarget = \"llvm -mtriple=%s-linux-android\" % arch\ntarget_host = None\n\nif local_demo:\n target_host = None\n target = \"llvm\"\nelif test_target == \"opencl\":\n target_host = target\n target = \"opencl\"\nelif test_target == \"vulkan\" [...]
]
},
{
diff --git a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
index 851810a..35f989c 100644
--- a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
+++ b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
@@ -34,7 +34,7 @@ import tvm
from tvm import te
import tvm.relay as relay
from tvm import rpc
-from tvm.contrib import util, ndk, graph_runtime as runtime
+from tvm.contrib import utils, ndk, graph_runtime as runtime
from tvm.contrib.download import download_testdata
@@ -282,7 +282,7 @@ with tvm.transform.PassContext(opt_level=3):
# change the parameters but keep the result of model as the same.
# Save the library at local temporary directory.
-tmp = util.tempdir()
+tmp = utils.tempdir()
lib_fname = tmp.relpath("net.so")
fcompile = ndk.create_shared if not local_demo else None
lib.export_library(lib_fname, fcompile)
diff --git a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
index 50c182a..9846b89 100644
--- a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
+++ b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "import os\nfrom mxnet.gluon.model_zoo import vision\nimport numpy as np\nfrom PIL import Image\n\nfrom tvm import topi\nimport tvm\nfrom tvm import te\nfrom tvm import rpc, autotvm, relay\nfrom tvm.contrib import graph_runtime, util, download\nfrom tvm.autotvm.measure.measure_methods import request_remote\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\n\nimport vta\nfrom vta.testing import simulator\nfrom vta.top import graph_pack"
+ "import os\nfrom mxnet.gluon.model_zoo import vision\nimport numpy as np\nfrom PIL import Image\n\nfrom tvm import topi\nimport tvm\nfrom tvm import te\nfrom tvm import rpc, autotvm, relay\nfrom tvm.contrib import graph_runtime, utils, download\nfrom tvm.autotvm.measure.measure_methods import request_remote\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\n\nimport vta\nfrom vta.testing import simulator\nfrom vta.top import graph_pack"
]
},
{
@@ -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 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 [...]
]
},
{
@@ -158,7 +158,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
]
}
],
diff --git a/docs/_downloads/4dd41316d6ea7ff2b6993aab65428bf5/cross_compilation_and_rpc.ipynb b/docs/_downloads/4dd41316d6ea7ff2b6993aab65428bf5/cross_compilation_and_rpc.ipynb
index 73d5104..0d6f59c 100644
--- a/docs/_downloads/4dd41316d6ea7ff2b6993aab65428bf5/cross_compilation_and_rpc.ipynb
+++ b/docs/_downloads/4dd41316d6ea7ff2b6993aab65428bf5/cross_compilation_and_rpc.ipynb
@@ -47,7 +47,7 @@
},
"outputs": [],
"source": [
- "import numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm import rpc\nfrom tvm.contrib import util\n\nn = tvm.runtime.convert(1024)\nA = te.placeholder((n,), name=\"A\")\nB = te.compute((n,), lambda i: A[i] + 1.0, name=\"B\")\ns = te.create_schedule(B.op)"
+ "import numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm import rpc\nfrom tvm.contrib import utils\n\nn = tvm.runtime.convert(1024)\nA = te.placeholder((n,), name=\"A\")\nB = te.compute((n,), lambda i: A[i] + 1.0, name=\"B\")\ns = te.create_schedule(B.op)"
]
},
{
@@ -65,7 +65,7 @@
},
"outputs": [],
"source": [
- "local_demo = True\n\nif local_demo:\n target = \"llvm\"\nelse:\n target = \"llvm -mtriple=armv7l-linux-gnueabihf\"\n\nfunc = tvm.build(s, [A, B], target=target, name=\"add_one\")\n# save the lib at a local temp folder\ntemp = util.tempdir()\npath = temp.relpath(\"lib.tar\")\nfunc.export_library(path)"
+ "local_demo = True\n\nif local_demo:\n target = \"llvm\"\nelse:\n target = \"llvm -mtriple=armv7l-linux-gnueabihf\"\n\nfunc = tvm.build(s, [A, B], target=target, name=\"add_one\")\n# save the lib at a local temp folder\ntemp = utils.tempdir()\npath = temp.relpath(\"lib.tar\")\nfunc.export_library(path)"
]
},
{
diff --git a/docs/_downloads/4f4a49a5483a0d0aa4af30f58c3c8664/deploy_quantized.ipynb b/docs/_downloads/4f4a49a5483a0d0aa4af30f58c3c8664/deploy_quantized.ipynb
index 2406d9e..94eabbc 100644
--- a/docs/_downloads/4f4a49a5483a0d0aa4af30f58c3c8664/deploy_quantized.ipynb
+++ b/docs/_downloads/4f4a49a5483a0d0aa4af30f58c3c8664/deploy_quantized.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nDeploy a Quantized Model on Cuda\n================================\n**Author**: `Wuwei Lin <https://github.com/vinx13>`_\n\nThis article is an introductory tutorial of automatic quantization with TVM.\nAutomatic quantization is one of the quantization modes in TVM. More details on\nthe quantization story in TVM can be found\n`here <https://discuss.tvm.ai/t/quantization-story/3920>`_.\nIn this tutorial, we will import a GluonCV pre-trained model on ImageNet to\nRelay, quantize [...]
+ "\nDeploy a Quantized Model on Cuda\n================================\n**Author**: `Wuwei Lin <https://github.com/vinx13>`_\n\nThis article is an introductory tutorial of automatic quantization with TVM.\nAutomatic quantization is one of the quantization modes in TVM. More details on\nthe quantization story in TVM can be found\n`here <https://discuss.tvm.apache.org/t/quantization-story/3920>`_.\nIn this tutorial, we will import a GluonCV pre-trained model on ImageNet to\nRelay, q [...]
]
},
{
diff --git a/docs/_downloads/5b32f1dc3e9e2fc5ac5be0918758b967/deploy_quantized.py b/docs/_downloads/5b32f1dc3e9e2fc5ac5be0918758b967/deploy_quantized.py
index 093bd73..e75f6e9 100644
--- a/docs/_downloads/5b32f1dc3e9e2fc5ac5be0918758b967/deploy_quantized.py
+++ b/docs/_downloads/5b32f1dc3e9e2fc5ac5be0918758b967/deploy_quantized.py
@@ -22,7 +22,7 @@ Deploy a Quantized Model on Cuda
This article is an introductory tutorial of automatic quantization with TVM.
Automatic quantization is one of the quantization modes in TVM. More details on
the quantization story in TVM can be found
-`here <https://discuss.tvm.ai/t/quantization-story/3920>`_.
+`here <https://discuss.tvm.apache.org/t/quantization-story/3920>`_.
In this tutorial, we will import a GluonCV pre-trained model on ImageNet to
Relay, quantize the Relay model and then perform the inference.
"""
diff --git a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
index cb36040..7f04424 100644
--- a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
+++ b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
@@ -62,7 +62,7 @@ from tvm import topi
import tvm
from tvm import te
from tvm import rpc, autotvm, relay
-from tvm.contrib import graph_runtime, util, download
+from tvm.contrib import graph_runtime, utils, download
from tvm.autotvm.measure.measure_methods import request_remote
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
@@ -424,7 +424,7 @@ def tune_and_evaluate(tuning_opt):
# Export library
print("Upload...")
- temp = util.tempdir()
+ temp = utils.tempdir()
lib.save(temp.relpath("graphlib.o"))
remote.upload(temp.relpath("graphlib.o"))
lib = remote.load_module("graphlib.o")
@@ -507,4 +507,4 @@ tune_and_evaluate(tuning_option)
# import logging
# logging.getLogger('autotvm').setLevel(logging.DEBUG)
#
-# Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
diff --git a/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb b/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb
index a68a501..da58016 100644
--- a/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb
+++ b/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb
@@ -116,7 +116,7 @@
},
"outputs": [],
"source": [
- "def gemv_impl():\n cc_code = \"\"\"\n extern \"C\" int gemv_update(float *cc, float *aa, float *bb, int m, int l, int stride) {\n for (int i = 0; i < m; ++i) {\n for (int j = 0; j < l; ++j) {\n cc[i] += aa[j] * bb[i * stride + j];\n }\n }\n return 0;\n }\n \"\"\"\n from tvm.contrib import util, clang\n\n temp = util.tempdir()\n ll_path = temp.relpath(\"temp.ll\")\n # Create LLVM ir from c sourc [...]
+ "def gemv_impl():\n cc_code = \"\"\"\n extern \"C\" int gemv_update(float *cc, float *aa, float *bb, int m, int l, int stride) {\n for (int i = 0; i < m; ++i) {\n for (int j = 0; j < l; ++j) {\n cc[i] += aa[j] * bb[i * stride + j];\n }\n }\n return 0;\n }\n \"\"\"\n from tvm.contrib import utils, clang\n\n temp = utils.tempdir()\n ll_path = temp.relpath(\"temp.ll\")\n # Create LLVM ir from c sou [...]
]
},
{
@@ -152,7 +152,7 @@
},
"outputs": [],
"source": [
- "func = tvm.build(s, [A, B, C], target=\"llvm\", name=\"gemv\")\n\nfrom tvm.topi.util import get_const_tuple\n\ndtype = A.dtype\nctx = tvm.context(\"cpu\", 0)\na = np.random.uniform(size=get_const_tuple(A.shape)).astype(dtype)\nb = np.random.uniform(size=get_const_tuple(B.shape)).astype(dtype)\nc = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=dtype), ctx)\nfunc(tvm.nd.array(a, ctx), tvm.nd.array(b, ctx), c)\ntvm.testing.assert_allclose(c.asnumpy(), np.dot(a, b.T), rtol=1e-3)"
+ "func = tvm.build(s, [A, B, C], target=\"llvm\", name=\"gemv\")\n\nfrom tvm.topi.utils import get_const_tuple\n\ndtype = A.dtype\nctx = tvm.context(\"cpu\", 0)\na = np.random.uniform(size=get_const_tuple(A.shape)).astype(dtype)\nb = np.random.uniform(size=get_const_tuple(B.shape)).astype(dtype)\nc = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=dtype), ctx)\nfunc(tvm.nd.array(a, ctx), tvm.nd.array(b, ctx), c)\ntvm.testing.assert_allclose(c.asnumpy(), np.dot(a, b.T), rtol=1e-3)"
]
},
{
@@ -188,7 +188,7 @@
},
"outputs": [],
"source": [
- "def gemv_impl():\n cc_code = \"\"\"\n extern \"C\" int gemv_update(float *cc, float *aa, float *bb, int m, int l, int stride) {\n for (int i = 0; i < m; ++i) {\n for (int j = 0; j < l; ++j) {\n cc[i] += aa[j] * bb[i * stride + j];\n }\n }\n return 0;\n }\n extern \"C\" int gemv_reset(float *cc, int m) {\n for (int i = 0; i < m; ++i) {\n cc[i] = 0.0;\n }\n return 0;\n [...]
+ "def gemv_impl():\n cc_code = \"\"\"\n extern \"C\" int gemv_update(float *cc, float *aa, float *bb, int m, int l, int stride) {\n for (int i = 0; i < m; ++i) {\n for (int j = 0; j < l; ++j) {\n cc[i] += aa[j] * bb[i * stride + j];\n }\n }\n return 0;\n }\n extern \"C\" int gemv_reset(float *cc, int m) {\n for (int i = 0; i < m; ++i) {\n cc[i] = 0.0;\n }\n return 0;\n [...]
]
},
{
diff --git a/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py b/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py
index b800eb4..42273bf 100644
--- a/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py
+++ b/docs/_downloads/678f3c372a599a18d909aed0fefb30be/tune_conv2d_layer_cuda.py
@@ -30,8 +30,14 @@ The auto-scheduler can automatically generate a large search space and
find a good schedule in the space.
We use a convolution layer as an example in this tutorial.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
+import os
+
import numpy as np
import tvm
from tvm import te, auto_scheduler, topi
@@ -88,11 +94,12 @@ print(task.compute_dag)
# * see :any:`auto_scheduler.TuningOptions`,
# :any:`auto_scheduler.LocalRPCMeasureContext` for more parameters.
+log_file = "conv2d.json"
measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=10,
runner=measure_ctx.runner,
- measure_callbacks=[auto_scheduler.RecordToFile("conv2d.json")],
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
######################################################################
@@ -157,7 +164,7 @@ print(
# print the equivalent python schedule API, and build the binary again.
# Load the measuremnt record for the best schedule
-inp, res = auto_scheduler.load_best("conv2d.json", task.workload_key)
+inp, res = auto_scheduler.load_best(log_file, task.workload_key)
# Print equivalent python schedule API. This can be used for debugging and
# learning the behavior of the auto-scheduler.
@@ -176,7 +183,6 @@ func = tvm.build(sch, args, target)
# In the example below we resume the status and do more 5 trials.
-log_file = "conv2d.json"
cost_model = auto_scheduler.XGBModel()
cost_model.update_from_file(log_file)
search_policy = auto_scheduler.SketchPolicy(
diff --git a/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py b/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py
index 8797c3e..c9d1c13 100644
--- a/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py
+++ b/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py
@@ -43,7 +43,7 @@ from tvm import te
import vta
import numpy as np
from tvm import rpc
-from tvm.contrib import util
+from tvm.contrib import utils
from vta.testing import simulator
# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
@@ -88,7 +88,7 @@ elif env.TARGET in ["sim", "tsim"]:
# matrix multiplication followed by a rectified linear activation.
# We describe the TVM dataflow graph of the fully connected layer below:
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/fc_dataflow.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/fc_dataflow.png
# :align: center
#
# This computation is intentionally too large to fit onto VTA's on-chip
@@ -183,7 +183,7 @@ print(tvm.lower(s, [data, weight, res], simple_mode=True))
# We show the outcome of blocking on the computation schedule in the diagram
# below:
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/blocking.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/blocking.png
# :align: center
# :width: 480px
#
@@ -311,7 +311,7 @@ print(vta.lower(s, [data, weight, res], simple_mode=True))
# Compile the TVM module
my_gemm = vta.build(s, [data, weight, res], "ext_dev", env.target_host, name="my_gemm")
-temp = util.tempdir()
+temp = utils.tempdir()
my_gemm.save(temp.relpath("gemm.o"))
remote.upload(temp.relpath("gemm.o"))
f = remote.load_module("gemm.o")
diff --git a/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py b/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
index 141defe..1557ea5 100644
--- a/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
+++ b/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
@@ -111,4 +111,4 @@ plt.show()
# retains that dynamism upon import, and the compiler attemps to convert the model
# 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.ai if you hit an error with dynamic kernels.
+# please file an issue on discuss.tvm.apache.org if you hit an error with dynamic kernels.
diff --git a/docs/_downloads/6a91d98d4242322072303282a1f2de9c/relay_quick_start.py b/docs/_downloads/6a91d98d4242322072303282a1f2de9c/relay_quick_start.py
index cece7ab..6da62f5 100644
--- a/docs/_downloads/6a91d98d4242322072303282a1f2de9c/relay_quick_start.py
+++ b/docs/_downloads/6a91d98d4242322072303282a1f2de9c/relay_quick_start.py
@@ -129,9 +129,9 @@ print(out.flatten()[0:10])
####################################################
# save the graph, lib and params into separate files
-from tvm.contrib import util
+from tvm.contrib import utils
-temp = util.tempdir()
+temp = utils.tempdir()
path_lib = temp.relpath("deploy_lib.tar")
lib.export_library(path_lib)
print(temp.listdir())
diff --git a/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb b/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
index f4a039a..6cb27ba 100644
--- a/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
+++ b/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nCompile PyTorch Models\n======================\n**Author**: `Alex Wong <https://github.com/alexwong/>`_\n\nThis article is an introductory tutorial to deploy PyTorch models with Relay.\n\nFor us to begin with, PyTorch should be installed.\nTorchVision is also required since we will be using it as our model zoo.\n\nA quick solution is to install via pip\n\n.. code-block:: bash\n\n pip install torch==1.4.0\n pip install torchvision==0.5.0\n\nor please refer to official sit [...]
+ "\nCompile PyTorch Models\n======================\n**Author**: `Alex Wong <https://github.com/alexwong/>`_\n\nThis article is an introductory tutorial to deploy PyTorch models with Relay.\n\nFor us to begin with, PyTorch should be installed.\nTorchVision is also required since we will be using it as our model zoo.\n\nA quick solution is to install via pip\n\n.. code-block:: bash\n\n pip install torch==1.7.0\n pip install torchvision==0.8.1\n\nor please refer to official sit [...]
]
},
{
diff --git a/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb b/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb
index 04be549..6032e06 100644
--- a/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb
+++ b/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb
@@ -184,7 +184,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib import cc\nfrom tvm.contrib import util\n\ntemp = util.tempdir()\nfadd.save(temp.relpath(\"myadd.o\"))\nif tgt == \"cuda\":\n fadd.imported_modules[0].save(temp.relpath(\"myadd.ptx\"))\nif tgt == \"rocm\":\n fadd.imported_modules[0].save(temp.relpath(\"myadd.hsaco\"))\nif tgt.startswith(\"opencl\"):\n fadd.imported_modules[0].save(temp.relpath(\"myadd.cl\"))\ncc.create_shared(temp.relpath(\"myadd.so\"), [temp.relpath(\"myadd.o\")])\nprint(temp.listdir())"
+ "from tvm.contrib import cc\nfrom tvm.contrib import utils\n\ntemp = utils.tempdir()\nfadd.save(temp.relpath(\"myadd.o\"))\nif tgt == \"cuda\":\n fadd.imported_modules[0].save(temp.relpath(\"myadd.ptx\"))\nif tgt == \"rocm\":\n fadd.imported_modules[0].save(temp.relpath(\"myadd.hsaco\"))\nif tgt.startswith(\"opencl\"):\n fadd.imported_modules[0].save(temp.relpath(\"myadd.cl\"))\ncc.create_shared(temp.relpath(\"myadd.so\"), [temp.relpath(\"myadd.o\")])\nprint(temp.listdir())"
]
},
{
diff --git a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
index a8360bc..8fb25bf 100644
--- a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
+++ b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nAuto-tuning a convolutional network for NVIDIA GPU\n==================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Eddie Yan <https://github.com/eqy/>`_\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 convolutional\nnetwork for NVIDIA GPU.\n\nThe operator implementation for NVIDIA GPU in TVM is written in template form.\nThe template ha [...]
+ "\nAuto-tuning a convolutional network for NVIDIA GPU\n==================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Eddie Yan <https://github.com/eqy/>`_\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 convolutional\nnetwork for NVIDIA GPU.\n\nThe operator implementation for NVIDIA GPU in TVM is written in template form.\nThe template ha [...]
]
},
{
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "import os\n\nimport numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm import autotvm\nfrom tvm import relay\nimport tvm.relay.testing\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.contrib.util import tempdir\nimport tvm.contrib.graph_runtime as runtime"
+ "import os\n\nimport numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm import autotvm\nfrom tvm import relay\nimport tvm.relay.testing\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.contrib.utils import tempdir\nimport tvm.contrib.graph_runtime as runtime"
]
},
{
@@ -126,7 +126,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
]
},
{
diff --git a/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb b/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
index c554f0c..d7017cc 100644
--- a/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
+++ b/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nDeploy a Framework-prequantized Model with TVM\n==============================================\n**Author**: `Masahiro Masuda <https://github.com/masahi>`_\n\nThis is a tutorial on loading models quantized by deep learning frameworks into TVM.\nPre-quantized model import is one of the quantization support we have in TVM. More details on\nthe quantization story in TVM can be found\n`here <https://discuss.tvm.ai/t/quantization-story/3920>`_.\n\nHere, we demonstrate how to load an [...]
+ "\nDeploy a Framework-prequantized Model with TVM\n==============================================\n**Author**: `Masahiro Masuda <https://github.com/masahi>`_\n\nThis is a tutorial on loading models quantized by deep learning frameworks into TVM.\nPre-quantized model import is one of the quantization support we have in TVM. More details on\nthe quantization story in TVM can be found\n`here <https://discuss.tvm.apache.org/t/quantization-story/3920>`_.\n\nHere, we demonstrate how to [...]
]
},
{
diff --git a/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py b/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
index 8b49a21..3687991 100644
--- a/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
+++ b/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
@@ -30,7 +30,7 @@ import tvm
from tvm import te
import tvm.relay as relay
from tvm import rpc
-from tvm.contrib import util, graph_runtime as runtime
+from tvm.contrib import utils, graph_runtime as runtime
from tvm.contrib.download import download_testdata
######################################################################
@@ -193,7 +193,7 @@ with tvm.transform.PassContext(opt_level=3):
# change the parameters but keep the result of model as the same.
# Save the library at local temporary directory.
-tmp = util.tempdir()
+tmp = utils.tempdir()
lib_fname = tmp.relpath("net.tar")
lib.export_library(lib_fname)
diff --git a/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py b/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py
index 1dd947f..b1b7ca2 100644
--- a/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py
+++ b/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py
@@ -23,6 +23,10 @@ Auto-tuning a convolutional network for x86 CPU
This is a tutorial about how to tune convolution neural network
for x86 CPU.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
import os
import numpy as np
diff --git a/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py b/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
index e9f1a4c..beba332 100644
--- a/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
+++ b/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
@@ -22,7 +22,7 @@ Deploy a Framework-prequantized Model with TVM
This is a tutorial on loading models quantized by deep learning frameworks into TVM.
Pre-quantized model import is one of the quantization support we have in TVM. More details on
the quantization story in TVM can be found
-`here <https://discuss.tvm.ai/t/quantization-story/3920>`_.
+`here <https://discuss.tvm.apache.org/t/quantization-story/3920>`_.
Here, we demonstrate how to load and run models quantized by PyTorch, MXNet, and TFLite.
Once loaded, we can run compiled, quantized models on any hardware TVM supports.
diff --git a/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py b/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py
index 35c4744..0f2ebe0 100644
--- a/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py
+++ b/docs/_downloads/91b0339c8f3cc2594cee580dc450149a/tune_matmul_x86.py
@@ -27,8 +27,14 @@ The auto-scheduler can automatically generate a large search space and
find a good schedule in the space.
We use matrix multiplication as an example in this tutorial.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
+import os
+
import numpy as np
import tvm
from tvm import te, auto_scheduler
@@ -80,8 +86,9 @@ print(task.compute_dag)
# and do more analyses later.
# * see :any:`auto_scheduler.TuningOptions` for more parameters
+log_file = "matmul.json"
tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
+ num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]
)
######################################################################
@@ -141,7 +148,7 @@ print(
# print the equivalent python schedule API, and build the binary again.
# Load the measuremnt record for the best schedule
-inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)
+inp, res = auto_scheduler.load_best(log_file, task.workload_key)
# Print equivalent python schedule API. This can be used for debugging and
# learning the behavior of the auto-scheduler.
@@ -160,19 +167,21 @@ func = tvm.build(sch, args)
# In the example below we resume the status and do more 5 trials.
-def resume_search(task, log_file):
+def resume_search(task, log_file_name):
cost_model = auto_scheduler.XGBModel()
- cost_model.update_from_file(log_file)
+ cost_model.update_from_file(log_file_name)
search_policy = auto_scheduler.SketchPolicy(
- task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
+ task,
+ cost_model,
+ init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file_name)],
)
tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]
+ num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file_name)]
)
sch, args = auto_scheduler.auto_schedule(task, search_policy, tuning_options=tune_option)
-# resume_search(task, "matmul.json")
+# resume_search(task, log_file)
######################################################################
# .. note::
diff --git a/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb b/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb
index fb8aad4..f072ef2 100644
--- a/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb
+++ b/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nAuto-tuning a convolutional network for x86 CPU\n===============================================\n**Author**: `Yao Wang <https://github.com/kevinthesun>`_, `Eddie Yan <https://github.com/eqy>`_\n\nThis is a tutorial about how to tune convolution neural network\nfor x86 CPU.\n\n"
+ "\n\nAuto-tuning a convolutional network for x86 CPU\n===============================================\n**Author**: `Yao Wang <https://github.com/kevinthesun>`_, `Eddie Yan <https://github.com/eqy>`_\n\nThis is a tutorial about how to tune convolution neural network\nfor x86 CPU.\n\nNote that this tutorial will not run on Windows or recent versions of macOS. To\nget it to run, you will need to wrap the body of this tutorial in a :code:`if\n__name__ == \"__main__\":` block.\n\n"
]
},
{
diff --git a/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py b/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py
index 601adb8..e91cfe4 100644
--- a/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py
+++ b/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py
@@ -160,9 +160,9 @@ def gemv_impl():
return 0;
}
"""
- from tvm.contrib import util, clang
+ from tvm.contrib import utils, clang
- temp = util.tempdir()
+ temp = utils.tempdir()
ll_path = temp.relpath("temp.ll")
# Create LLVM ir from c source code
ll_code = clang.create_llvm(cc_code, output=ll_path)
@@ -182,7 +182,7 @@ print(tvm.lower(s, [A, B, C], simple_mode=True))
#
func = tvm.build(s, [A, B, C], target="llvm", name="gemv")
-from tvm.topi.util import get_const_tuple
+from tvm.topi.utils import get_const_tuple
dtype = A.dtype
ctx = tvm.context("cpu", 0)
@@ -228,9 +228,9 @@ def gemv_impl():
return 0;
}
"""
- from tvm.contrib import util, clang
+ from tvm.contrib import utils, clang
- temp = util.tempdir()
+ temp = utils.tempdir()
ll_path = temp.relpath("temp.ll")
# Create LLVM ir from c source code
ll_code = clang.create_llvm(cc_code, output=ll_path)
diff --git a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
index f024ba4..7514ee7 100644
--- a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
+++ b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
@@ -35,6 +35,10 @@ these operators, it will query this log file to get the best knob values.
We also released pre-tuned parameters for some arm devices. You can go to
`ARM CPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#arm-cpu>`_
to see the results.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
######################################################################
@@ -67,7 +71,7 @@ from tvm import autotvm
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
-from tvm.contrib.util import tempdir
+from tvm.contrib.utils import tempdir
import tvm.contrib.graph_runtime as runtime
#################################################################
@@ -412,4 +416,4 @@ def tune_and_evaluate(tuning_opt):
# import logging
# logging.getLogger('autotvm').setLevel(logging.DEBUG)
#
-# Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
diff --git a/docs/_downloads/bba10250678e70879823196c946734fc/vta_get_started.py b/docs/_downloads/bba10250678e70879823196c946734fc/vta_get_started.py
index 8f37b2d..1a097b8 100644
--- a/docs/_downloads/bba10250678e70879823196c946734fc/vta_get_started.py
+++ b/docs/_downloads/bba10250678e70879823196c946734fc/vta_get_started.py
@@ -67,7 +67,7 @@ env = vta.get_env()
# We'll need the TVM RPC module and the VTA simulator module
from tvm import rpc
-from tvm.contrib import util
+from tvm.contrib import utils
from vta.testing import simulator
# We read the Pynq RPC host IP address and port number from the OS environment
@@ -115,7 +115,7 @@ elif env.TARGET == "sim":
# The last operation is a cast and copy back to DRAM, into results tensor
# :code:`C`.
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/vadd_dataflow.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/vadd_dataflow.png
# :align: center
######################################################################
@@ -320,7 +320,7 @@ my_vadd = vta.build(s, [A, B, C], "ext_dev", env.target_host, name="my_vadd")
# execution.
# Write the compiled module into an object file.
-temp = util.tempdir()
+temp = utils.tempdir()
my_vadd.save(temp.relpath("vadd.o"))
# Send the executable over RPC
diff --git a/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb b/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb
index a5ae635..6960e9b 100644
--- a/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb
+++ b/docs/_downloads/bcb4a24e8acc1ca84214bc8d7fb7954b/tune_conv2d_layer_cuda.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nAuto-scheduling a convolution layer for GPU\n===========================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Chengfan Jia <https://github.com/jcf94/>`_\n\n\nDifferent from the existing `autotvm <tutorials-autotvm-sec>` which relies on \nmanual templates to define the search space, the auto-scheduler does not require any templates.\nUsers only need to write the computation declaration without any schedule commands or tem [...]
+ "\n\nAuto-scheduling a convolution layer for GPU\n===========================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Chengfan Jia <https://github.com/jcf94/>`_\n\n\nDifferent from the existing `autotvm <tutorials-autotvm-sec>` which relies on \nmanual templates to define the search space, the auto-scheduler does not require any templates.\nUsers only need to write the computation declaration without any schedule commands or tem [...]
]
},
{
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "import numpy as np\nimport tvm\nfrom tvm import te, auto_scheduler, topi\nfrom tvm.topi.testing import conv2d_nchw_python"
+ "import os\n\nimport numpy as np\nimport tvm\nfrom tvm import te, auto_scheduler, topi\nfrom tvm.topi.testing import conv2d_nchw_python"
]
},
{
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)\ntune_option = auto_scheduler.TuningOptions(\n num_measure_trials=10,\n runner=measure_ctx.runner,\n measure_callbacks=[auto_scheduler.RecordToFile(\"conv2d.json\")],\n)"
+ "log_file = \"conv2d.json\"\nmeasure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)\ntune_option = auto_scheduler.TuningOptions(\n num_measure_trials=10,\n runner=measure_ctx.runner,\n measure_callbacks=[auto_scheduler.RecordToFile(log_file)],\n)"
]
},
{
@@ -159,7 +159,7 @@
},
"outputs": [],
"source": [
- "# Load the measuremnt record for the best schedule\ninp, res = auto_scheduler.load_best(\"conv2d.json\", task.workload_key)\n\n# Print equivalent python schedule API. This can be used for debugging and\n# learning the behavior of the auto-scheduler.\nprint(\"Equivalent python schedule:\")\nprint(task.compute_dag.print_python_code_from_state(inp.state))\n\n# Rebuild the binary. This shows how you can apply the best schedule from a\n# log file without reruning the search again.\ns [...]
+ "# Load the measuremnt record for the best schedule\ninp, res = auto_scheduler.load_best(log_file, task.workload_key)\n\n# Print equivalent python schedule API. This can be used for debugging and\n# learning the behavior of the auto-scheduler.\nprint(\"Equivalent python schedule:\")\nprint(task.compute_dag.print_python_code_from_state(inp.state))\n\n# Rebuild the binary. This shows how you can apply the best schedule from a\n# log file without reruning the search again.\nsch, arg [...]
]
},
{
@@ -177,7 +177,7 @@
},
"outputs": [],
"source": [
- "log_file = \"conv2d.json\"\ncost_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_fi [...]
+ "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)\nsch, args = auto_s [...]
]
}
],
diff --git a/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb b/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
index 53e9cb6..b6c4e85 100644
--- a/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
+++ b/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "import tvm\nfrom tvm import te\nimport tvm.relay as relay\nfrom tvm import rpc\nfrom tvm.contrib import util, graph_runtime as runtime\nfrom tvm.contrib.download import download_testdata"
+ "import tvm\nfrom tvm import te\nimport tvm.relay as relay\nfrom tvm import rpc\nfrom tvm.contrib import utils, graph_runtime as runtime\nfrom tvm.contrib.download import download_testdata"
]
},
{
@@ -155,7 +155,7 @@
},
"outputs": [],
"source": [
- "local_demo = True\n\nif local_demo:\n target = tvm.target.Target(\"llvm\")\nelse:\n target = tvm.target.arm_cpu(\"rasp3b\")\n # The above line is a simple form of\n # target = tvm.target.Target('llvm -device=arm_cpu -model=bcm2837 -mtriple=armv7l-linux-gnueabihf -mattr=+neon')\n\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(func, target, params=params)\n\n# After `relay.build`, you will get three return values: graph,\n# library and the new par [...]
+ "local_demo = True\n\nif local_demo:\n target = tvm.target.Target(\"llvm\")\nelse:\n target = tvm.target.arm_cpu(\"rasp3b\")\n # The above line is a simple form of\n # target = tvm.target.Target('llvm -device=arm_cpu -model=bcm2837 -mtriple=armv7l-linux-gnueabihf -mattr=+neon')\n\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(func, target, params=params)\n\n# After `relay.build`, you will get three return values: graph,\n# library and the new par [...]
]
},
{
diff --git a/docs/_downloads/c49dbffd05b18e5db4049ffe6480aca2/deploy_object_detection_pytorch.ipynb b/docs/_downloads/c49dbffd05b18e5db4049ffe6480aca2/deploy_object_detection_pytorch.ipynb
index c1c6eb0..3bba116 100644
--- a/docs/_downloads/c49dbffd05b18e5db4049ffe6480aca2/deploy_object_detection_pytorch.ipynb
+++ b/docs/_downloads/c49dbffd05b18e5db4049ffe6480aca2/deploy_object_detection_pytorch.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nCompile PyTorch Object Detection Models\n=======================================\nThis article is an introductory tutorial to deploy PyTorch object\ndetection models with Relay VM.\n\nFor us to begin with, PyTorch should be installed.\nTorchVision is also required since we will be using it as our model zoo.\n\nA quick solution is to install via pip\n\n.. code-block:: bash\n\n pip install torch==1.4.0\n pip install torchvision==0.5.0\n\nor please refer to official site\nh [...]
+ "\nCompile PyTorch Object Detection Models\n=======================================\nThis article is an introductory tutorial to deploy PyTorch object\ndetection models with Relay VM.\n\nFor us to begin with, PyTorch should be installed.\nTorchVision is also required since we will be using it as our model zoo.\n\nA quick solution is to install via pip\n\n.. code-block:: bash\n\n pip install torch==1.7.0\n pip install torchvision==0.8.1\n\nor please refer to official site\nh [...]
]
},
{
diff --git a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
index b0d0677..21d8062 100644
--- a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
+++ b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport argparse, json, os, requests, sys, time\nfrom io import BytesIO\nfrom os.path import join, isfile\nfrom PIL import Image\n\nfrom mxnet.gluon.model_zoo import vision\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimport tvm\nfrom tvm import te\nfrom tvm import rpc, autotvm, relay\nfrom tvm.contrib import graph_runtime, util, download\nfrom tvm.contrib.debugger import debug_runtime\nfrom tvm.relay impor [...]
+ "from __future__ import absolute_import, print_function\n\nimport argparse, json, os, requests, sys, time\nfrom io import BytesIO\nfrom os.path import join, isfile\nfrom PIL import Image\n\nfrom mxnet.gluon.model_zoo import vision\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimport tvm\nfrom tvm import te\nfrom tvm import rpc, autotvm, relay\nfrom tvm.contrib import graph_runtime, utils, download\nfrom tvm.contrib.debugger import debug_runtime\nfrom tvm.relay impo [...]
]
},
{
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n\n # Populate the shape and data type dictionary for ImageNet classifier input\n dtype_dict = {\"data\": \"float32\"}\n shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n # Get off the shelf gluon model, and convert to relay\n gluon_model = vision.get_model(model, pretrained=True)\n\n # Measure build start time\n build_start = time.time()\n\n # Start front end compilation\n [...]
+ "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n\n # Populate the shape and data type dictionary for ImageNet classifier input\n dtype_dict = {\"data\": \"float32\"}\n shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n # Get off the shelf gluon model, and convert to relay\n gluon_model = vision.get_model(model, pretrained=True)\n\n # Measure build start time\n build_start = time.time()\n\n # Start front end compilation\n [...]
]
},
{
diff --git a/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb b/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb
index ed8fd11..af8d19c 100644
--- a/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb
+++ b/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb
@@ -33,14 +33,14 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nimport tvm.testing\nfrom tvm import te\nimport vta\nimport numpy as np\n\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\npo [...]
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nimport tvm.testing\nfrom tvm import te\nimport vta\nimport numpy as np\n\nfrom tvm import rpc\nfrom tvm.contrib import utils\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\np [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Computation Declaration\n-----------------------\nAs a first step, we need to describe our 2D convolution computation\nin NCHW format.\n\nWe define the 2D convolution shape by the batch size,\nspatial dimensions, input channels, output channels, kernel dimensions,\nkernel dimensions, padding dimensions, and stride dimensions.\n\nWe pick the shape of the 9th convolutional layer of the ResNet-18\narchitecture as our convolution workload parameters.\n\nWe've added extra operators t [...]
+ "Computation Declaration\n-----------------------\nAs a first step, we need to describe our 2D convolution computation\nin NCHW format.\n\nWe define the 2D convolution shape by the batch size,\nspatial dimensions, input channels, output channels, kernel dimensions,\nkernel dimensions, padding dimensions, and stride dimensions.\n\nWe pick the shape of the 9th convolutional layer of the ResNet-18\narchitecture as our convolution workload parameters.\n\nWe've added extra operators t [...]
]
},
{
@@ -94,7 +94,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Virtual Threading\n~~~~~~~~~~~~~~~~~\nVirtual threading is a mechanism that increases task-level pipeline\nparallelism in the VTA hardware design.\nPut it another way, it increases compute resource utilization by hiding\nmemory access latency.\n\nIn the implementation below, virtual threading distributes work across two\nthreads split along the output channel axis.\nWe show how work is split when computing the 2D convolution in the figure\nbelow.\n\n![](https://raw.githubusercon [...]
+ "Virtual Threading\n~~~~~~~~~~~~~~~~~\nVirtual threading is a mechanism that increases task-level pipeline\nparallelism in the VTA hardware design.\nPut it another way, it increases compute resource utilization by hiding\nmemory access latency.\n\nIn the implementation below, virtual threading distributes work across two\nthreads split along the output channel axis.\nWe show how work is split when computing the 2D convolution in the figure\nbelow.\n\n![](https://raw.githubusercon [...]
]
},
{
@@ -159,7 +159,7 @@
},
"outputs": [],
"source": [
- "# This library facilitates 2D convolution testing\nfrom tvm.topi.testing import conv2d_nchw_python\n\n# Compile the TVM module\nmy_conv = vta.build(s, [data, kernel, res], \"ext_dev\", env.target_host, name=\"my_conv\")\ntemp = util.tempdir()\nmy_conv.save(temp.relpath(\"conv2d.o\"))\nremote.upload(temp.relpath(\"conv2d.o\"))\nf = remote.load_module(\"conv2d.o\")\n\n# Get the remote device context\nctx = remote.ext_dev(0)\n\n# Initialize the data and kernel arrays randomly in th [...]
+ "# This library facilitates 2D convolution testing\nfrom tvm.topi.testing import conv2d_nchw_python\n\n# Compile the TVM module\nmy_conv = vta.build(s, [data, kernel, res], \"ext_dev\", env.target_host, name=\"my_conv\")\ntemp = utils.tempdir()\nmy_conv.save(temp.relpath(\"conv2d.o\"))\nremote.upload(temp.relpath(\"conv2d.o\"))\nf = remote.load_module(\"conv2d.o\")\n\n# Get the remote device context\nctx = remote.ext_dev(0)\n\n# Initialize the data and kernel arrays randomly in t [...]
]
},
{
diff --git a/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py b/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py
index 2888f34..185b71f 100644
--- a/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py
+++ b/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py
@@ -45,7 +45,7 @@ import vta
import numpy as np
from tvm import rpc
-from tvm.contrib import util
+from tvm.contrib import utils
from vta.testing import simulator
# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
@@ -93,7 +93,7 @@ elif env.TARGET in ["sim", "tsim"]:
# convolution followed by a rectified linear activation.
# We describe the TVM dataflow graph of the 2D convolution layer below:
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/conv2d_dataflow.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/conv2d_dataflow.png
# :align: center
#
# This computation is intentionally too large to fit onto VTA's on-chip
@@ -120,7 +120,7 @@ elif env.TARGET in ["sim", "tsim"]:
# loaded from DRAM into VTA's SRAM, following a 2D strided and padded memory
# read.
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/padding.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/padding.png
# :align: center
# :width: 480px
@@ -292,7 +292,7 @@ s[res_conv].reorder(ic_out, b_inn, oc_inn, y_inn, ic_inn, dy, dx, x_inn, b_tns,
# We show how work is split when computing the 2D convolution in the figure
# below.
#
-# .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/virtual_threading.png
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/virtual_threading.png
# :align: center
# :width: 480px
@@ -370,7 +370,7 @@ from tvm.topi.testing import conv2d_nchw_python
# Compile the TVM module
my_conv = vta.build(s, [data, kernel, res], "ext_dev", env.target_host, name="my_conv")
-temp = util.tempdir()
+temp = utils.tempdir()
my_conv.save(temp.relpath("conv2d.o"))
remote.upload(temp.relpath("conv2d.o"))
f = remote.load_module("conv2d.o")
diff --git a/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb b/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
index c031e24..5b26c9e 100644
--- a/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
+++ b/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "# %%\n# Setup\n# -----\n#\n# To get started, TFLite package needs to be installed as prerequisite.\n#\n# install tflite\n#\n# .. code-block:: bash\n#\n# pip install tflite=2.1.0 --user\n#\n# or you could generate TFLite package yourself. The steps are the following:\n#\n# Get the flatc compiler.\n# Please refer to https://github.com/google/flatbuffers for details\n# and make sure it is properly installed.\n#\n# .. code-block:: bash\n#\n# flatc --version\n#\n# Get the T [...]
+ "# %%\n# Setup\n# -----\n#\n# To get started, TFLite package needs to be installed as prerequisite.\n#\n# install tflite\n#\n# .. code-block:: bash\n#\n# pip install tflite=2.1.0 --user\n#\n# or you could generate TFLite package yourself. The steps are the following:\n#\n# Get the flatc compiler.\n# Please refer to https://github.com/google/flatbuffers for details\n# and make sure it is properly installed.\n#\n# .. code-block:: bash\n#\n# flatc --version\n#\n# Get the T [...]
]
},
{
diff --git a/docs/_downloads/cfc40d6a8f25c9fce268b6a4f277d915/cross_compilation_and_rpc.py b/docs/_downloads/cfc40d6a8f25c9fce268b6a4f277d915/cross_compilation_and_rpc.py
index 572ebb8..69284c0 100644
--- a/docs/_downloads/cfc40d6a8f25c9fce268b6a4f277d915/cross_compilation_and_rpc.py
+++ b/docs/_downloads/cfc40d6a8f25c9fce268b6a4f277d915/cross_compilation_and_rpc.py
@@ -98,7 +98,7 @@ import numpy as np
import tvm
from tvm import te
from tvm import rpc
-from tvm.contrib import util
+from tvm.contrib import utils
n = tvm.runtime.convert(1024)
A = te.placeholder((n,), name="A")
@@ -120,7 +120,7 @@ else:
func = tvm.build(s, [A, B], target=target, name="add_one")
# save the lib at a local temp folder
-temp = util.tempdir()
+temp = utils.tempdir()
path = temp.relpath("lib.tar")
func.export_library(path)
diff --git a/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb b/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb
index 7f8a2f8..54cb9b1 100644
--- a/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb
+++ b/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb
@@ -33,14 +33,14 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.ge [...]
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import utils\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.g [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Computation Declaration\n-----------------------\nIn this example we describe a simple matrix multiplication addition, which\nrequires multiple computation stages, as shown in the dataflow diagram below.\nFirst we describe the input tensors :code:`A` and :code:`B` that are living\nin main memory.\nSecond, we need to declare intermediate tensors :code:`A_buf` and\n:code:`B_buf`, which will live in VTA's on-chip buffers.\nHaving this extra computational stage allows us to explicit [...]
+ "Computation Declaration\n-----------------------\nIn this example we describe a simple matrix multiplication addition, which\nrequires multiple computation stages, as shown in the dataflow diagram below.\nFirst we describe the input tensors :code:`A` and :code:`B` that are living\nin main memory.\nSecond, we need to declare intermediate tensors :code:`A_buf` and\n:code:`B_buf`, which will live in VTA's on-chip buffers.\nHaving this extra computational stage allows us to explicit [...]
]
},
{
@@ -54,7 +54,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>**Data Tiling**\n\n One source of complexity when targeting accelerators is to make sure\n that the data layout matches the layout imposed by the accelerator design.\n VTA is designed around a *tensor core* that performs, one matrix-matrix\n operation per cycle between an activation matrix and a weight matrix,\n adding the result matrix to an accumulator matrix, as shown in the\n figure below.\n\n .. image:: https://raw.gith [...]
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Data Tiling**\n\n One source of complexity when targeting accelerators is to make sure\n that the data layout matches the layout imposed by the accelerator design.\n VTA is designed around a *tensor core* that performs, one matrix-matrix\n operation per cycle between an activation matrix and a weight matrix,\n adding the result matrix to an accumulator matrix, as shown in the\n figure below.\n\n .. image:: https://raw.gith [...]
]
},
{
@@ -240,7 +240,7 @@
},
"outputs": [],
"source": [
- "# Build GEMM VTA kernel\nmy_gemm = vta.build(s, [A, B, C], \"ext_dev\", env.target_host, name=\"my_gemm\")\n\n# Write the compiled module into an object file.\ntemp = util.tempdir()\nmy_gemm.save(temp.relpath(\"gemm.o\"))\n\n# Send the executable over RPC\nremote.upload(temp.relpath(\"gemm.o\"))\n\n# Load the compiled module\nf = remote.load_module(\"gemm.o\")"
+ "# Build GEMM VTA kernel\nmy_gemm = vta.build(s, [A, B, C], \"ext_dev\", env.target_host, name=\"my_gemm\")\n\n# Write the compiled module into an object file.\ntemp = utils.tempdir()\nmy_gemm.save(temp.relpath(\"gemm.o\"))\n\n# Send the executable over RPC\nremote.upload(temp.relpath(\"gemm.o\"))\n\n# Load the compiled module\nf = remote.load_module(\"gemm.o\")"
]
},
{
diff --git a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
index 6125466..b7fbf89 100644
--- a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
+++ b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
@@ -33,6 +33,10 @@ these operators, it will query this log file to get the best knob values.
We also released pre-tuned parameters for some arm devices. You can go to
`Mobile GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#mobile-gpu>`_
to see the results.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
"""
######################################################################
@@ -66,7 +70,7 @@ from tvm import autotvm
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
-from tvm.contrib.util import tempdir
+from tvm.contrib.utils import tempdir
import tvm.contrib.graph_runtime as runtime
#################################################################
@@ -417,4 +421,4 @@ def tune_and_evaluate(tuning_opt):
# import logging
# logging.getLogger('autotvm').setLevel(logging.DEBUG)
#
-# Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
diff --git a/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb b/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
index 8694d06..8953d2d 100644
--- a/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
+++ b/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
@@ -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.ai 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\n"
]
}
],
diff --git a/docs/_downloads/ec94e7a109437cf90cddcc60a7b5aaea/deploy_object_detection_pytorch.py b/docs/_downloads/ec94e7a109437cf90cddcc60a7b5aaea/deploy_object_detection_pytorch.py
index 6408685..2852dd3 100644
--- a/docs/_downloads/ec94e7a109437cf90cddcc60a7b5aaea/deploy_object_detection_pytorch.py
+++ b/docs/_downloads/ec94e7a109437cf90cddcc60a7b5aaea/deploy_object_detection_pytorch.py
@@ -27,8 +27,8 @@ A quick solution is to install via pip
.. code-block:: bash
- pip install torch==1.4.0
- pip install torchvision==0.5.0
+ pip install torch==1.7.0
+ pip install torchvision==0.8.1
or please refer to official site
https://pytorch.org/get-started/locally/
@@ -36,7 +36,7 @@ https://pytorch.org/get-started/locally/
PyTorch versions should be backwards compatible but should be used
with the proper TorchVision version.
-Currently, TVM supports PyTorch 1.4 and 1.3. Other versions may
+Currently, TVM supports PyTorch 1.7 and 1.4. Other versions may
be unstable.
"""
diff --git a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
index 582eb03..1bf4161 100644
--- a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
+++ b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
@@ -52,7 +52,7 @@ from matplotlib import pyplot as plt
import tvm
from tvm import te
from tvm import rpc, autotvm, relay
-from tvm.contrib import graph_runtime, util, download
+from tvm.contrib import graph_runtime, utils, download
from tvm.contrib.debugger import debug_runtime
from tvm.relay import transform
@@ -204,7 +204,7 @@ with autotvm.tophub.context(target):
print(model + " inference graph built in {0:.2f}s!".format(build_time))
# Send the inference library over to the remote RPC server
- temp = util.tempdir()
+ temp = utils.tempdir()
lib.export_library(temp.relpath("graphlib.tar"))
remote.upload(temp.relpath("graphlib.tar"))
lib = remote.load_module("graphlib.tar")
diff --git a/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb b/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb
index eac1893..ad43051 100644
--- a/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb
+++ b/docs/_downloads/f1a09967bab66114252357e4a9babb45/tune_matmul_x86.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nAuto-scheduling matrix multiplication for CPU\n=============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Chengfan Jia <https://github.com/jcf94/>`_\n\nDifferent from the existing `autotvm <tutorials-autotvm-sec>` which relies on \nmanual templates to define the search space, the auto-scheduler does not require any templates.\nUsers only need to write the computation declaration without any schedule commands or tem [...]
+ "\nAuto-scheduling matrix multiplication for CPU\n=============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Chengfan Jia <https://github.com/jcf94/>`_\n\nDifferent from the existing `autotvm <tutorials-autotvm-sec>` which relies on \nmanual templates to define the search space, the auto-scheduler does not require any templates.\nUsers only need to write the computation declaration without any schedule commands or tem [...]
]
},
{
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "import numpy as np\nimport tvm\nfrom tvm import te, auto_scheduler"
+ "import os\n\nimport numpy as np\nimport tvm\nfrom tvm import te, auto_scheduler"
]
},
{
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile(\"matmul.json\")]\n)"
+ "log_file = \"matmul.json\"\ntune_option = auto_scheduler.TuningOptions(\n num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]\n)"
]
},
{
@@ -159,7 +159,7 @@
},
"outputs": [],
"source": [
- "# Load the measuremnt record for the best schedule\ninp, res = auto_scheduler.load_best(\"matmul.json\", task.workload_key)\n\n# Print equivalent python schedule API. This can be used for debugging and\n# learning the behavior of the auto-scheduler.\nprint(\"Equivalent python schedule:\")\nprint(task.compute_dag.print_python_code_from_state(inp.state))\n\n# Rebuild the binary. This shows how you can apply the best schedule from a\n# log file without reruning the search again.\ns [...]
+ "# Load the measuremnt record for the best schedule\ninp, res = auto_scheduler.load_best(log_file, task.workload_key)\n\n# Print equivalent python schedule API. This can be used for debugging and\n# learning the behavior of the auto-scheduler.\nprint(\"Equivalent python schedule:\")\nprint(task.compute_dag.print_python_code_from_state(inp.state))\n\n# Rebuild the binary. This shows how you can apply the best schedule from a\n# log file without reruning the search again.\nsch, arg [...]
]
},
{
@@ -177,7 +177,7 @@
},
"outputs": [],
"source": [
- "def resume_search(task, log_file):\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 sch, args = auto_scheduler.auto_schedule(task, search_po [...]
+ "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 sch, args = auto_ [...]
]
},
{
diff --git a/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py b/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py
index 3dcefd5..25a1e5c 100644
--- a/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py
+++ b/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py
@@ -45,13 +45,25 @@ import numpy as np
# Load pretrained keras model
# ----------------------------
# We load a pretrained resnet-50 classification model provided by keras.
-weights_url = "".join(
- [
- "https://github.com/fchollet/deep-learning-models/releases/",
- "download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
- ]
-)
-weights_file = "resnet50_weights.h5"
+
+if tuple(keras.__version__.split(".")) < ("2", "4", "0"):
+ weights_url = "".join(
+ [
+ "https://github.com/fchollet/deep-learning-models/releases/",
+ "download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
+ ]
+ )
+ weights_file = "resnet50_keras_old.h5"
+else:
+ weights_url = "".join(
+ [
+ " https://storage.googleapis.com/tensorflow/keras-applications/",
+ "resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
+ ]
+ )
+ weights_file = "resnet50_keras_new.h5"
+
+
weights_path = download_testdata(weights_url, weights_file, module="keras")
keras_resnet50 = keras.applications.resnet50.ResNet50(
include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
diff --git a/docs/_downloads/f5b39ff89c432f7750c338ebf15407f6/vta_get_started.ipynb b/docs/_downloads/f5b39ff89c432f7750c338ebf15407f6/vta_get_started.ipynb
index 114cab3..9163c5e 100644
--- a/docs/_downloads/f5b39ff89c432f7750c338ebf15407f6/vta_get_started.ipynb
+++ b/docs/_downloads/f5b39ff89c432f7750c338ebf15407f6/vta_get_started.ipynb
@@ -62,14 +62,14 @@
},
"outputs": [],
"source": [
- "# We'll need the TVM RPC module and the VTA simulator module\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.get(\"VTA_RPC_PORT\", \"9091\"))\n\n# We configure both the bitstream and the runtime system on the Pynq\n# to match the VTA configuration specified by the vta_config.json [...]
+ "# We'll need the TVM RPC module and the VTA simulator module\nfrom tvm import rpc\nfrom tvm.contrib import utils\nfrom vta.testing import simulator\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.get(\"VTA_RPC_PORT\", \"9091\"))\n\n# We configure both the bitstream and the runtime system on the Pynq\n# to match the VTA configuration specified by the vta_config.json [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Computation Declaration\n-----------------------\nAs a first step, we need to describe our computation.\nTVM adopts tensor semantics, with each intermediate result\nrepresented as multi-dimensional array. The user needs to describe\nthe computation rule that generates the output tensors.\n\nIn this example we describe a vector addition, which requires multiple\ncomputation stages, as shown in the dataflow diagram below.\nFirst we describe the input tensors :code:`A` and :code:`B [...]
+ "Computation Declaration\n-----------------------\nAs a first step, we need to describe our computation.\nTVM adopts tensor semantics, with each intermediate result\nrepresented as multi-dimensional array. The user needs to describe\nthe computation rule that generates the output tensors.\n\nIn this example we describe a vector addition, which requires multiple\ncomputation stages, as shown in the dataflow diagram below.\nFirst we describe the input tensors :code:`A` and :code:`B [...]
]
},
{
@@ -284,7 +284,7 @@
},
"outputs": [],
"source": [
- "# Write the compiled module into an object file.\ntemp = util.tempdir()\nmy_vadd.save(temp.relpath(\"vadd.o\"))\n\n# Send the executable over RPC\nremote.upload(temp.relpath(\"vadd.o\"))"
+ "# Write the compiled module into an object file.\ntemp = utils.tempdir()\nmy_vadd.save(temp.relpath(\"vadd.o\"))\n\n# Send the executable over RPC\nremote.upload(temp.relpath(\"vadd.o\"))"
]
},
{
diff --git a/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py b/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
index 33a0588..b5bcdf6 100644
--- a/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
+++ b/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
@@ -28,8 +28,8 @@ A quick solution is to install via pip
.. code-block:: bash
- pip install torch==1.4.0
- pip install torchvision==0.5.0
+ pip install torch==1.7.0
+ pip install torchvision==0.8.1
or please refer to official site
https://pytorch.org/get-started/locally/
@@ -37,7 +37,7 @@ https://pytorch.org/get-started/locally/
PyTorch versions should be backwards compatible but should be used
with the proper TorchVision version.
-Currently, TVM supports PyTorch 1.4 and 1.3. Other versions may
+Currently, TVM supports PyTorch 1.7 and 1.4. Other versions may
be unstable.
"""
diff --git a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
index 040c9bc..b8bf4f4 100644
--- a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
+++ b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nAuto-tuning a convolutional network for ARM CPU\n===============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Zhao Wu <https://github.com/FrozenGene>`_, `Eddie Yan <https://github.com/eqy>`_\n\nAuto-tuning for a specific ARM device is critical for getting the best\nperformance. This is a tutorial about how to tune a whole convolutional\nnetwork.\n\nThe operator implementation for ARM CPU in TVM is written in template form.\n [...]
+ "\n\nAuto-tuning a convolutional network for ARM CPU\n===============================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Zhao Wu <https://github.com/FrozenGene>`_, `Eddie Yan <https://github.com/eqy>`_\n\nAuto-tuning for a specific ARM device is critical for getting the best\nperformance. This is a tutorial about how to tune a whole convolutional\nnetwork.\n\nThe operator implementation for ARM CPU in TVM is written in template form.\n [...]
]
},
{
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "import os\n\nimport numpy as np\nimport tvm\nfrom tvm import te\nfrom tvm import autotvm\nfrom tvm import relay\nimport tvm.relay.testing\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.contrib.util import tempdir\nimport tvm.contrib.graph_runtime as runtime"
+ "import os\n\nimport numpy as np\nimport tvm\nfrom tvm import te\nfrom tvm import autotvm\nfrom tvm import relay\nimport tvm.relay.testing\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.contrib.utils import tempdir\nimport tvm.contrib.graph_runtime as runtime"
]
},
{
@@ -140,7 +140,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Experiencing Difficulties?**\n\n The auto tuning module is error-prone. If you always see \" 0.00/ 0.00 GFLOPS\",\n then there must be something wrong.\n\n First, make sure you set the correct configuration of your device.\n Then, you can print debug information by adding these lines in the beginning\n of the script. It will print every measurement result, where you can find useful\n error messages.\n\n .. code-block:: pyt [...]
]
}
],
diff --git a/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py b/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
index 6fd2de1..293f95c 100644
--- a/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
+++ b/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
@@ -98,7 +98,7 @@ import numpy as np
import tvm
import tvm.micro as micro
from tvm.contrib.download import download_testdata
-from tvm.contrib import graph_runtime, util
+from tvm.contrib import graph_runtime, utils
from tvm import relay
# %%
diff --git a/docs/_sources/api/python/contrib.rst.txt b/docs/_sources/api/python/contrib.rst.txt
index 8ac4e1f..0eb3024 100644
--- a/docs/_sources/api/python/contrib.rst.txt
+++ b/docs/_sources/api/python/contrib.rst.txt
@@ -122,9 +122,9 @@ tvm.contrib.tar
:members:
-tvm.contrib.util
-~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.util
+tvm.contrib.utils
+~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.utils
:members:
diff --git a/docs/_sources/contribute/error_handling.rst.txt b/docs/_sources/contribute/error_handling.rst.txt
index 8f71ee6..d31b401 100644
--- a/docs/_sources/contribute/error_handling.rst.txt
+++ b/docs/_sources/contribute/error_handling.rst.txt
@@ -37,14 +37,14 @@ raise an error of the corresponding type.
Note that you do not have to add a new type
:py:class:`tvm.error.TVMError` will be raised by default when
there is no error type prefix in the message.
-This mechanism works for both ``LOG(FATAL)`` and ``CHECK`` macros.
+This mechanism works for both ``LOG(FATAL)`` and ``ICHECK`` macros.
The following code gives an example on how to do so.
.. code:: c
// src/api_test.cc
void ErrorTest(int x, int y) {
- CHECK_EQ(x, y) << "ValueError: expect x and y to be equal."
+ ICHECK_EQ(x, y) << "ValueError: expect x and y to be equal."
if (x == 1) {
LOG(FATAL) << "InternalError: cannot reach here";
}
diff --git a/docs/_sources/dev/convert_layout.rst.txt b/docs/_sources/dev/convert_layout.rst.txt
index 07ebc20..6c9890f 100644
--- a/docs/_sources/dev/convert_layout.rst.txt
+++ b/docs/_sources/dev/convert_layout.rst.txt
@@ -157,7 +157,7 @@ First example is for layout agnostic operators. These operators do not have any
Layout ret;
if (new_in_layouts.defined()) {
- CHECK_GE(new_in_layouts.size(), 1);
+ ICHECK_GE(new_in_layouts.size(), 1);
ret = new_in_layouts[0];
} else {
for (size_t i = 0; i < old_in_layouts.size(); ++i) {
diff --git a/docs/_sources/dev/introduction_to_module_serialization.rst.txt b/docs/_sources/dev/introduction_to_module_serialization.rst.txt
index 5451b84..6b2f2ad 100644
--- a/docs/_sources/dev/introduction_to_module_serialization.rst.txt
+++ b/docs/_sources/dev/introduction_to_module_serialization.rst.txt
@@ -32,7 +32,7 @@ Let us build one ResNet-18 workload for GPU as an example first.
from tvm import relay
from tvm.relay import testing
- from tvm.contrib import util
+ from tvm.contrib import utils
import tvm
# Resnet18 workload
@@ -43,7 +43,7 @@ Let us build one ResNet-18 workload for GPU as an example first.
_, resnet18_lib, _ = relay.build_module.build(resnet18_mod, "cuda", params=resnet18_params)
# create one tempory directory
- temp = util.tempdir()
+ temp = utils.tempdir()
# path lib
file_name = "deploy.so"
diff --git a/docs/_sources/dev/pass_infra.rst.txt b/docs/_sources/dev/pass_infra.rst.txt
index 1427608..898e517 100644
--- a/docs/_sources/dev/pass_infra.rst.txt
+++ b/docs/_sources/dev/pass_infra.rst.txt
@@ -276,12 +276,12 @@ order that they were appended to the pass list.
const PassContext& pass_ctx) const {
Module mod = module;
for (const Pass& pass : passes) {
- CHECK(pass.defined()) << "Found undefined pass for optimization.";
+ ICHECK(pass.defined()) << "Found undefined pass for optimization.";
const PassInfo& pass_info = pass->Info();
if (!PassEnabled(pass_info)) continue;
for (const auto& it : pass_info->required) {
const auto* name = it.as<tvm::ir::StringImm>();
- CHECK(name);
+ ICHECK(name);
mod = GetPass(name->value)(mod, pass_ctx);
}
mod = pass(mod, pass_ctx);
@@ -306,7 +306,7 @@ pass is registered with an API endpoint as we will show later.
using tvm::runtime::Registry;
std::string fpass_name = "relay._transform." + pass_name;
const auto* f = Registry::Get(fpass_name);
- CHECK(f != nullptr) << "Cannot find " << fpass_name
+ ICHECK(f != nullptr) << "Cannot find " << fpass_name
<< "to create the pass " << pass_name;
return (*f)();
}
diff --git a/docs/_sources/dev/relay_bring_your_own_codegen.rst.txt b/docs/_sources/dev/relay_bring_your_own_codegen.rst.txt
index f4ee58a..a4d4ebd 100644
--- a/docs/_sources/dev/relay_bring_your_own_codegen.rst.txt
+++ b/docs/_sources/dev/relay_bring_your_own_codegen.rst.txt
@@ -296,7 +296,7 @@ As mentioned in the previous step, in addition to the subgraph input and output
// This example only supports single output.
auto type_node = call->checked_type().as<TensorTypeNode>();
- CHECK(type_node != nullptr && runtime::TypeMatch(type_node->dtype, kDLFloat, 32))
+ ICHECK(type_node != nullptr && runtime::TypeMatch(type_node->dtype, kDLFloat, 32))
<< "Only support single output tensor with float type";
// Generate a unique buffer name.
@@ -410,7 +410,7 @@ Implement GenCFunc
.. code-block:: c++
void GenCFunc(const Function& func) {
- CHECK(func.defined()) << "Input error: expect a Relay function.";
+ ICHECK(func.defined()) << "Input error: expect a Relay function.";
// Record the external symbol for runtime lookup.
auto sid = GetExtSymbol(func);
@@ -474,7 +474,7 @@ This function creates a runtime module for the external library. In this example
// Create a CSourceModule
const auto* pf = runtime::Registry::Get("module.csource_module_create");
- CHECK(pf != nullptr) << "Cannot find csource module to create the external runtime module";
+ ICHECK(pf != nullptr) << "Cannot find csource module to create the external runtime module";
return (*pf)(code_stream_.str(), "cc");
}
@@ -556,7 +556,7 @@ In this section, our goal is to implement the following customized TVM runtime m
ExampleJsonCodeGen codegen(ref);
std::string code = codegen.gen(); // Note 1
const auto* pf = runtime::Registry::Get("module.examplejson_module_create"); // Note 2
- CHECK(pf != nullptr) << "Cannot find ExampleJson module to create the external runtime module";
+ ICHECK(pf != nullptr) << "Cannot find ExampleJson module to create the external runtime module";
return (*pf)(code);
}
TVM_REGISTER_GLOBAL("relay.ext.examplejsoncompiler").set_body_typed(ExampleJsonCompiler);
@@ -785,7 +785,7 @@ After the construction, we should have the above class variables ready. We then
// Copy input tensors to corresponding data entries.
for (auto i = 0; i < args.size(); ++i) {
- CHECK(args[i].type_code() == kNDArrayContainer || args[i].type_code() == kArrayHandle)
+ ICHECK(args[i].type_code() == kNDArrayContainer || args[i].type_code() == kArrayHandle)
<< "Expect NDArray or DLTensor as inputs\n";
if (args[i].type_code() == kArrayHandle) {
DLTensor* arg = args[i];
@@ -800,7 +800,7 @@ After the construction, we should have the above class variables ready. We then
for (const auto& it : this->graph_[this->curr_subgraph_]) {
this->Run(it.id, it.inputs, it.output);
}
- CHECK_GT(graph_.count(this->curr_subgraph_), 0U);
+ ICHECK_GT(graph_.count(this->curr_subgraph_), 0U);
// Copy the output from a data entry back to TVM runtime argument.
auto out_idx = graph_[this->curr_subgraph_].back().output;
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 8856d90..bfe3d78 100644
--- a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**05:10.193** total execution time for **tutorials_auto_scheduler** files:
+**03:25.669** total execution time for **tutorials_auto_scheduler** files:
-- **03:13.532**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:56.661**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_matmul_x86.py` (``tune_matmul_x86.py``)
+- **01:48.967**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_matmul_x86.py` (``tune_matmul_x86.py``)
+- **01:36.702**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.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 10ea58e..614a707 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
@@ -22,10 +22,16 @@ find a good schedule in the space.
We use a convolution layer as an example in this tutorial.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
.. code-block:: default
+ import os
+
import numpy as np
import tvm
from tvm import te, auto_scheduler, topi
@@ -124,11 +130,12 @@ mainly specify how we do the measurement during the search and auto-tuning.
.. code-block:: default
+ log_file = "conv2d.json"
measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=10,
runner=measure_ctx.runner,
- measure_callbacks=[auto_scheduler.RecordToFile("conv2d.json")],
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
@@ -202,14 +209,14 @@ 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" = 128;
+ 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, [7]);
attr [pad_temp.shared: Pointer(float32)] "storage_scope" = "shared";
- allocate(pad_temp.shared, float32, [504]);
+ allocate(pad_temp.shared, float32, [54]);
attr [kernel.shared: Pointer(float32)] "storage_scope" = "shared";
- allocate(kernel.shared, float32, [96]);
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14 {
+ allocate(kernel.shared, float32, [576]);
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32 {
compute_3[0] = 0f32
compute_3[1] = 0f32
compute_3[2] = 0f32
@@ -217,142 +224,185 @@ cooperative fetching, unrolling and operator fusion.
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" = 14;
- pad_temp.shared[threadIdx.x_1] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv(threadIdx.x_1, 9)*7)) + (ry.outer.outer*7)) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 14)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 5), 9)) && (floormod((threadIdx.x_1 + 5), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 14), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 28)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 1), 9)) && (floormod((threadIdx.x_1 + 1), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 28), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 42)] = @tir.if_then_else(((((floordiv((threadIdx.x_1 + 42), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 42), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 56), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 70)] = @tir.if_then_else((((1 <= (floordiv(floormod((threadIdx.x_1 + 7), 63), 9) + ry.outer.outer)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 70), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 84)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 3), 9)) && (floormod((threadIdx.x_1 + 3), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 84), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 98)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 8), 9)) && (floormod((threadIdx.x_1 + 8), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 98), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer) < 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 + 112), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 126)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv(threadIdx.x_1, 9)*7)) + (ry.outer.outer*7)) + floormod(threadIdx.x_1, 9)) + 90)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 140)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 5), 9)) && (floormod((threadIdx.x_1 + 5), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 140), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 154)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 1), 9)) && (floormod((threadIdx.x_1 + 1), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 154), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 168)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 42), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 168), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 182)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 182), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 196)] = @tir.if_then_else((((1 <= (floordiv(floormod((threadIdx.x_1 + 7), 63), 9) + ry.outer.outer)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 196), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 210)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 3), 9)) && (floormod((threadIdx.x_1 + 3), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 210), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 224)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 8), 9)) && (floormod((threadIdx.x_1 + 8), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 224), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 238)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer) < 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 + 238), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 252)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv(threadIdx.x_1, 9)*7)) + (ry.outer.outer*7)) + floormod(threadIdx.x_1, 9)) + 188)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 266)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 5), 9)) && (floormod((threadIdx.x_1 + 5), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 266), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 280)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 1), 9)) && (floormod((threadIdx.x_1 + 1), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 280), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 294)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 42), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 294), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 308)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 308), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 322)] = @tir.if_then_else((((1 <= (floordiv(floormod((threadIdx.x_1 + 7), 63), 9) + ry.outer.outer)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 322), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 336)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 3), 9)) && (floormod((threadIdx.x_1 + 3), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 336), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 350)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 8), 9)) && (floormod((threadIdx.x_1 + 8), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 350), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 364)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer) < 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 + 364), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 378)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv(threadIdx.x_1, 9)*7)) + (ry.outer.outer*7)) + floormod(threadIdx.x_1, 9)) + 286)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 392)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 5), 9)) && (floormod((threadIdx.x_1 + 5), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 392), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 406)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 1), 9)) && (floormod((threadIdx.x_1 + 1), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 406), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 420)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 42), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 420), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 434)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 434), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 448)] = @tir.if_then_else((((1 <= (floordiv(floormod((threadIdx.x_1 + 7), 63), 9) + ry.outer.outer)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 448), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 462)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 3), 9)) && (floormod((threadIdx.x_1 + 3), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 462), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 476)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 8), 9)) && (floormod((threadIdx.x_1 + 8), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 476), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- pad_temp.shared[(threadIdx.x_1 + 490)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 49), 63), 9) + ry.outer.outer) < 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 + 490), 9)*7)) + (ry.outer.outer*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- kernel.shared[threadIdx.x_2] = (float32*)kernel_2[(((((blockIdx.x*18432) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- kernel.shared[(threadIdx.x_2 + 14)] = (float32*)kernel_2[((((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 14), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 14), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 2), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- kernel.shared[(threadIdx.x_2 + 28)] = (float32*)kernel_2[((((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 28), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 4), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- kernel.shared[(threadIdx.x_2 + 42)] = (float32*)kernel_2[((((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 42), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 18), 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- kernel.shared[(threadIdx.x_2 + 56)] = (float32*)kernel_2[((((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 56), 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" = 14;
- kernel.shared[(threadIdx.x_2 + 70)] = (float32*)kernel_2[((((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 70), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 22), 24), 3)*9)) + (ry.outer.outer*3)) + floormod((threadIdx.x_2 + 1), 3))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 14;
- if @tir.likely((threadIdx.x_2 < 12), dtype=bool) {
- kernel.shared[(threadIdx.x_2 + 84)] = (float32*)kernel_2[((((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 84), 24)*4608)) + (rc.outer.outer*72)) + (floordiv(floormod((threadIdx.x_2 + 12), 24), 3)*9)) + (ry.outer.outer*3)) + floormod(threadIdx.x_2, 3))]
+ for (rc.outer.outer: int32, 0, 256) {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32 {
+ if @tir.likely((threadIdx.x_1 < 27), dtype=bool) {
+ pad_temp.shared[(threadIdx.x_1*2)] = @tir.if_then_else(((((3 <= floormod((threadIdx.x_1*2), 27)) && (floormod((threadIdx.x_1*2), 27) < 24)) && (1 <= (floormod(blockIdx.x, 7) + floormod((threadIdx.x_1*2), 3)))) && ((floormod(blockIdx.x, 7) + floormod((threadIdx.x_1*2), 3)) < 8)), (float32*)data_2[((((((rc.outer.outer*98) + (floordiv((threadIdx.x_1*2), 27)*49)) + (floordiv(floormod((threadIdx.x_1*2), 27), 3)*7)) + floormod(blockIdx.x, 7)) + floormod((threadIdx.x_1*2), 3)) - 8 [...]
}
- for (rc.outer.inner: int32, 0, 4) {
- for (rx.outer.inner: int32, 0, 3) {
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 27)]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 36)]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 45)]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 54)]*(float32*)kernel.shared[(((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[(((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 27)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 36)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 45)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 54)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 24)]))
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 63)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 72)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 81)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 90)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 99)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 108)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 117)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 3)]))
- compute_3[7] = ((float32*)compute_3[7] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 63)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- compute_3[8] = ((float32*)compute_3[8] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 72)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- compute_3[9] = ((float32*)compute_3[9] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 81)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- compute_3[10] = ((float32*)compute_3[10] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 90)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- compute_3[11] = ((float32*)compute_3[11] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 99)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- compute_3[12] = ((float32*)compute_3[12] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 108)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- compute_3[13] = ((float32*)compute_3[13] + ((float32*)pad_temp.shared[((((rc.outer.inner*126) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 117)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*48) + (rc.outer.inner*6)) + rx.outer.inner) + 27)]))
- }
+ if @tir.likely((threadIdx.x_1 < 27), dtype=bool) {
+ pad_temp.shared[((threadIdx.x_1*2) + 1)] = @tir.if_then_else(((((3 <= floormod(((threadIdx.x_1*2) + 1), 27)) && (floormod(((threadIdx.x_1*2) + 1), 27) < 24)) && (1 <= (floormod(blockIdx.x, 7) + floormod(((threadIdx.x_1*2) + 1), 3)))) && ((floormod(blockIdx.x, 7) + floormod(((threadIdx.x_1*2) + 1), 3)) < 8)), (float32*)data_2[((((((rc.outer.outer*98) + (floordiv(((threadIdx.x_1*2) + 1), 27)*49)) + (floordiv(floormod(((threadIdx.x_1*2) + 1), 27), 3)*7)) + floormod(blockIdx.x, [...]
}
}
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ 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" = 32;
+ kernel.shared[(threadIdx.x_2 + 32)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 32), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 14), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 64)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 64), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 10), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 96)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 96), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 6), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 128)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 128), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 2), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 160)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 160), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 16), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 192)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 192), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 12), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ 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" = 32;
+ kernel.shared[(threadIdx.x_2 + 256)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 256), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 4), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 288)] = (float32*)kernel_2[(((((floordiv(blockIdx.x, 7)*147456) + (floordiv(threadIdx.x_2, 18)*4608)) + (rc.outer.outer*18)) + floormod(threadIdx.x_2, 18)) + 73728)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 320)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 320), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 14), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 352)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 352), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 10), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 384)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 384), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 6), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 416)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 416), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 2), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ 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" = 32;
+ kernel.shared[(threadIdx.x_2 + 480)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 480), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 12), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 512)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 512), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 8), 18))]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
+ kernel.shared[(threadIdx.x_2 + 544)] = (float32*)kernel_2[((((floordiv(blockIdx.x, 7)*147456) + (floordiv((threadIdx.x_2 + 544), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 4), 18))]
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[0]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[9]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[18]*(float32*)kernel.shared[(threadIdx.x*18)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[1]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*18) + 1)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[2]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[8]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[17]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*18) + 2)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[3]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[9]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[18]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*18) + 3)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[4]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*18) + 4)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[5]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[8]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[17]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*18) + 5)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[6]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[9]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[12]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[15]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[18]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[21]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[24]*(float32*)kernel.shared[((threadIdx.x*18) + 6)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[7]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[10]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[13]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[16]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[19]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[22]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[25]*(float32*)kernel.shared[((threadIdx.x*18) + 7)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[8]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[11]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[14]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[17]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[20]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[23]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[26]*(float32*)kernel.shared[((threadIdx.x*18) + 8)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[27]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[36]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[45]*(float32*)kernel.shared[((threadIdx.x*18) + 9)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[28]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*18) + 10)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[29]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[35]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[44]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*18) + 11)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[30]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[36]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[45]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*18) + 12)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[31]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*18) + 13)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[32]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[35]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[44]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*18) + 14)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[33]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[36]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[39]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[42]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[45]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[48]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[51]*(float32*)kernel.shared[((threadIdx.x*18) + 15)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[34]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[37]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[40]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[43]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[46]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[49]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[52]*(float32*)kernel.shared[((threadIdx.x*18) + 16)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[35]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[38]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[41]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[44]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[47]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[50]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[53]*(float32*)kernel.shared[((threadIdx.x*18) + 17)]))
}
- for (i1.inner: int32, 0, 2) {
- for (i2.inner: int32, 0, 7) {
- compute_2[(((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7))] = max(((float32*)compute_3[((i1.inner*7) + i2.inner)] + (float32*)bias_2[(((blockIdx.x*4) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
- }
- }
+ compute_2[(((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7))] = max(((float32*)compute_3[0] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
+ compute_2[((((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7)) + 7)] = max(((float32*)compute_3[1] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
+ compute_2[((((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7)) + 14)] = max(((float32*)compute_3[2] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
+ compute_2[((((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7)) + 21)] = max(((float32*)compute_3[3] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
+ compute_2[((((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7)) + 28)] = max(((float32*)compute_3[4] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
+ compute_2[((((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7)) + 35)] = max(((float32*)compute_3[5] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
+ compute_2[((((floordiv(blockIdx.x, 7)*1568) + (threadIdx.x*49)) + floormod(blockIdx.x, 7)) + 42)] = max(((float32*)compute_3[6] + (float32*)bias_2[((floordiv(blockIdx.x, 7)*32) + threadIdx.x)]), 0f32)
}
}
@@ -432,7 +482,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.354 ms
+ Execution time of this operator: 0.175 ms
@@ -450,7 +500,7 @@ print the equivalent python schedule API, and build the binary again.
# Load the measuremnt record for the best schedule
- inp, res = auto_scheduler.load_best("conv2d.json", task.workload_key)
+ inp, res = auto_scheduler.load_best(log_file, task.workload_key)
# Print equivalent python schedule API. This can be used for debugging and
# learning the behavior of the auto-scheduler.
@@ -484,33 +534,33 @@ print the equivalent python schedule API, and build the binary again.
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=1)
- compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=16)
+ compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=32)
compute_ff_o_o_o_o, compute_ff_o_o_o_i = s[compute].split(compute_ff_o_o_o_i, factor=1)
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=7)
- compute_yy_o_o_o_o, compute_yy_o_o_o_i = s[compute].split(compute_yy_o_o_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=7)
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_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=1)
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=8)
- compute_rc_o_o, compute_rc_o_i = s[compute].split(compute_rc_o_i, factor=2)
+ 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=1)
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)
+ compute_rx_o_i, compute_rx_i = s[compute].split(compute_rx, factor=3)
+ compute_rx_o_o, compute_rx_o_i = s[compute].split(compute_rx_o_i, factor=1)
s[compute].reorder(compute_nn_o_o_o_o, compute_ff_o_o_o_o, compute_yy_o_o_o_o, compute_xx_o_o_o_o, compute_nn_o_o_o_i, compute_ff_o_o_o_i, compute_yy_o_o_o_i, compute_xx_o_o_o_i, compute_nn_o_o_i, compute_ff_o_o_i, compute_yy_o_o_i, compute_xx_o_o_i, compute_rc_o_o, compute_ry_o_o, compute_rx_o_o, compute_rc_o_i, compute_ry_o_i, compute_rx_o_i, compute_nn_o_i, compute_ff_o_i, compute_yy_o_i, compute_xx_o_i, compute_rc_i, compute_ry_i, compute_rx_i, compute_nn_i, compute_ff_i, compute [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
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=1)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=32)
compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
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=7)
- 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_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=7)
+ 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=1)
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)
@@ -529,16 +579,16 @@ print the equivalent python schedule API, and build the binary again.
compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused = s[compute].fuse(compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i)
s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread_axis("threadIdx.x"))
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=2)
+ 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=112)
+ 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=32)
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=9)
+ 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=2)
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=112)
+ 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=32)
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", 1024)
+ s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 512)
s[compute].pragma(compute_nn_o_o_o_o, "unroll_explicit", True)
@@ -554,7 +604,6 @@ In the example below we resume the status and do more 5 trials.
- log_file = "conv2d.json"
cost_model = auto_scheduler.XGBModel()
cost_model.update_from_file(log_file)
search_policy = auto_scheduler.SketchPolicy(
@@ -588,7 +637,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 13.532 seconds)
+ **Total running time of the script:** ( 1 minutes 36.702 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 2c0dc5e..24876f2 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
@@ -19,10 +19,16 @@ find a good schedule in the space.
We use matrix multiplication as an example in this tutorial.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
.. code-block:: default
+ import os
+
import numpy as np
import tvm
from tvm import te, auto_scheduler
@@ -114,8 +120,9 @@ Next, we set parameters for the auto-scheduler.
.. code-block:: default
+ log_file = "matmul.json"
tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
+ num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]
)
@@ -270,7 +277,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 2.213 ms
+ Execution time of this operator: 2.010 ms
@@ -288,7 +295,7 @@ print the equivalent python schedule API, and build the binary again.
# Load the measuremnt record for the best schedule
- inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)
+ inp, res = auto_scheduler.load_best(log_file, task.workload_key)
# Print equivalent python schedule API. This can be used for debugging and
# learning the behavior of the auto-scheduler.
@@ -313,21 +320,21 @@ print the equivalent python schedule API, and build the binary again.
Equivalent python schedule:
matmul_i, matmul_j, matmul_k = tuple(matmul.op.axis) + tuple(matmul.op.reduce_axis)
out_i, out_j = tuple(out.op.axis) + tuple(out.op.reduce_axis)
- matmul_i_o_i, matmul_i_i = s[matmul].split(matmul_i, factor=1)
- matmul_i_o_o_i, matmul_i_o_i = s[matmul].split(matmul_i_o_i, factor=128)
- matmul_i_o_o_o, matmul_i_o_o_i = s[matmul].split(matmul_i_o_o_i, factor=1)
- matmul_j_o_i, matmul_j_i = s[matmul].split(matmul_j, factor=16)
- matmul_j_o_o_i, matmul_j_o_i = s[matmul].split(matmul_j_o_i, factor=1)
- matmul_j_o_o_o, matmul_j_o_o_i = s[matmul].split(matmul_j_o_o_i, factor=8)
- matmul_k_o, matmul_k_i = s[matmul].split(matmul_k, factor=16)
+ matmul_i_o_i, matmul_i_i = s[matmul].split(matmul_i, factor=4)
+ matmul_i_o_o_i, matmul_i_o_i = s[matmul].split(matmul_i_o_i, factor=2)
+ matmul_i_o_o_o, matmul_i_o_o_i = s[matmul].split(matmul_i_o_o_i, factor=4)
+ matmul_j_o_i, matmul_j_i = s[matmul].split(matmul_j, factor=2)
+ matmul_j_o_o_i, matmul_j_o_i = s[matmul].split(matmul_j_o_i, factor=32)
+ matmul_j_o_o_o, matmul_j_o_o_i = s[matmul].split(matmul_j_o_o_i, factor=1)
+ matmul_k_o, matmul_k_i = s[matmul].split(matmul_k, factor=2)
s[matmul].reorder(matmul_i_o_o_o, matmul_j_o_o_o, matmul_i_o_o_i, matmul_j_o_o_i, matmul_k_o, matmul_i_o_i, matmul_j_o_i, matmul_k_i, matmul_i_i, matmul_j_i)
- out_i_o, out_i_i = s[out].split(out_i, factor=128)
- out_j_o, out_j_i = s[out].split(out_j, factor=128)
+ out_i_o, out_i_i = s[out].split(out_i, factor=32)
+ out_j_o, out_j_i = s[out].split(out_j, factor=64)
s[out].reorder(out_i_o, out_j_o, out_i_i, out_j_i)
s[matmul].compute_at(s[out], out_j_o)
out_i_o_j_o_fused = s[out].fuse(out_i_o, out_j_o)
s[out].parallel(out_i_o_j_o_fused)
- s[matmul].pragma(matmul_i_o_o_o, "auto_unroll_max_step", 64)
+ s[matmul].pragma(matmul_i_o_o_o, "auto_unroll_max_step", 0)
s[matmul].pragma(matmul_i_o_o_o, "unroll_explicit", True)
s[matmul].vectorize(matmul_j_i)
@@ -344,19 +351,21 @@ In the example below we resume the status and do more 5 trials.
- def resume_search(task, log_file):
+ def resume_search(task, log_file_name):
cost_model = auto_scheduler.XGBModel()
- cost_model.update_from_file(log_file)
+ cost_model.update_from_file(log_file_name)
search_policy = auto_scheduler.SketchPolicy(
- task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
+ task,
+ cost_model,
+ init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file_name)],
)
tune_option = auto_scheduler.TuningOptions(
- num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file)]
+ num_measure_trials=5, measure_callbacks=[auto_scheduler.RecordToFile(log_file_name)]
)
sch, args = auto_scheduler.auto_schedule(task, search_policy, tuning_options=tune_option)
- # resume_search(task, "matmul.json")
+ # resume_search(task, log_file)
@@ -384,7 +393,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 56.661 seconds)
+ **Total running time of the script:** ( 1 minutes 48.967 seconds)
.. _sphx_glr_download_tutorials_auto_scheduler_tune_matmul_x86.py:
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index 6e38bcf..734abf1 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
=================
-**01:10.365** total execution time for **tutorials_autotvm** files:
-
-- **00:45.502**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:24.048**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:00.224**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.199**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.198**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.195**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+**00:52.058** total execution time for **tutorials_autotvm** files:
+
+- **00:29.054**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:22.389**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:00.167**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.155**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.147**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.147**: :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 f5c6d2c..e05998c 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -15,6 +15,10 @@ This is an advanced tutorial for writing high performance tunable template for
NVIDIA GPU. By running auto-tuner on this template, we can outperform the
vendor provided library CuDNN in many cases.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
Install dependencies
--------------------
To use autotvm package in tvm, we need to install some extra dependencies.
@@ -238,26 +242,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- No: 1 GFLOPS: 30.06/30.06 result: MeasureResult(costs=(0.007702383,), error_no=0, all_cost=2.472616672515869, timestamp=1603167485.6522112) [('tile_f', [-1, 32, 1, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7166780
- No: 2 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 3 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 4 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 5 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 6 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 7 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 8 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 9 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 10 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 11 GFLOPS: 0.00/30.06 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 12 GFLOPS: 49.34/49.34 result: MeasureResult(costs=(0.004692016045454546,), error_no=0, all_cost=2.7959516048431396, timestamp=1603167495.4044144) [('tile_f', [-1, 2, 8, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2077980
- No: 13 GFLOPS: 0.00/49.34 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 14 GFLOPS: 73.85/73.85 result: MeasureResult(costs=(0.00313478265625,), error_no=0, all_cost=1.9385488033294678, timestamp=1603167496.8821692) [('tile_f', [-1, 2, 16, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8726459
- No: 15 GFLOPS: 27.80/73.85 result: MeasureResult(costs=(0.008327293230769231,), error_no=0, all_cost=1.758265495300293, timestamp=1603167497.9899476) [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5905444
- No: 16 GFLOPS: 1.62/73.85 result: MeasureResult(costs=(0.14270683425,), error_no=0, all_cost=4.567987680435181, timestamp=1603167500.6006465) [('tile_f', [-1, 2, 8, 8]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7428895
- No: 17 GFLOPS: 0.00/73.85 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (3) /workspace/build/libtvm.so(+0x6841d4) [0x7f0321f3e1d4]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f0321f3ea36]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 18 GFLOPS: 0.00/73.85 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (4) /workspace/build/libtvm.so(+0x1257ae2) [0x7f0322b11ae2]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7f0322b130cb]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::Call [...]
- No: 19 GFLOPS: 23.84/73.85 result: MeasureResult(costs=(0.009710596090909091,), error_no=0, all_cost=1.434156894683838, timestamp=1603167508.960667) [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,782066
- No: 20 GFLOPS: 0.00/73.85 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f0322ac69f1]\n [bt] (4) /workspace/build/libtvm.so(+0x1257ae2) [0x7f0322b11ae2]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7f0322b130cb]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::Call [...]
+ No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 4 GFLOPS: 8.01/8.01 result: MeasureResult(costs=(0.02889490325,), error_no=0, all_cost=1.394493579864502, timestamp=1604488711.9078188) [('tile_f', [-1, 64, 4, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,201325
+ No: 5 GFLOPS: 0.00/8.01 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 6 GFLOPS: 0.00/8.01 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 7 GFLOPS: 0.00/8.01 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 8 GFLOPS: 0.00/8.01 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 9 GFLOPS: 16.76/16.76 result: MeasureResult(costs=(0.013811034,), error_no=0, all_cost=4.084443092346191, timestamp=1604488713.8433964) [('tile_f', [-1, 16, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10187783
+ No: 10 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 11 GFLOPS: 6.73/16.76 result: MeasureResult(costs=(0.03439282125,), error_no=0, all_cost=1.3185703754425049, timestamp=1604488715.061667) [('tile_f', [-1, 2, 2, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 16]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,5741229
+ No: 12 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 13 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 14 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 15 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 16 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 17 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 18 GFLOPS: 0.00/16.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 19 GFLOPS: 18.76/18.76 result: MeasureResult(costs=(0.012339261272727274,), error_no=0, all_cost=1.5916728973388672, timestamp=1604488719.308212) [('tile_f', [-1, 16, 2, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,678328
+ No: 20 GFLOPS: 0.00/18.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (3) /workspace/build/libtvm.so(+0x69a977) [0x7f7ae837a977]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f7ae8379d8e]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
@@ -309,8 +313,8 @@ and measure running time.
Best config:
- [('tile_f', [-1, 2, 16, 8]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8726459
- Time cost of this operator: 0.003577
+ [('tile_f', [-1, 16, 2, 2]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,678328
+ Time cost of this operator: 0.012350
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
index bf6d9db..7a5b805 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
@@ -28,6 +28,10 @@ We also released pre-tuned parameters for some arm devices. You can go to
`ARM CPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#arm-cpu>`_
to see the results.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
Install dependencies
--------------------
To use the autotvm package in tvm, we need to install some extra dependencies.
@@ -61,7 +65,7 @@ Now return to python code. Import packages.
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
- from tvm.contrib.util import tempdir
+ from tvm.contrib.utils import tempdir
import tvm.contrib.graph_runtime as runtime
@@ -441,7 +445,7 @@ It takes about 2 hours on a 32T AMD Ryzen Threadripper.
import logging
logging.getLogger('autotvm').setLevel(logging.DEBUG)
- Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+ Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
.. _sphx_glr_download_tutorials_autotvm_tune_relay_arm.py:
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
index c9dc0bf..d0fd274 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
@@ -26,6 +26,10 @@ We also released pre-tuned parameters for some NVIDIA GPUs. You can go to
`NVIDIA GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#nvidia-gpu>`_
to see the results.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
Install dependencies
--------------------
To use the autotvm package in tvm, we need to install some extra dependencies.
@@ -59,7 +63,7 @@ Now return to python code. Import packages.
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
- from tvm.contrib.util import tempdir
+ from tvm.contrib.utils import tempdir
import tvm.contrib.graph_runtime as runtime
@@ -344,7 +348,7 @@ As a reference baseline, the time cost of MXNet + TensorRT on resnet-18 is 1.30m
import logging
logging.getLogger('autotvm').setLevel(logging.DEBUG)
- Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+ Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
Scale up measurement by using multiple devices
----------------------------------------------
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 b094783..ea7dd8f 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
@@ -26,6 +26,10 @@ We also released pre-tuned parameters for some arm devices. You can go to
`Mobile GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#mobile-gpu>`_
to see the results.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
Install dependencies
--------------------
To use the autotvm package in tvm, we need to install some extra dependencies.
@@ -60,7 +64,7 @@ Now return to python code. Import packages.
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
- from tvm.contrib.util import tempdir
+ from tvm.contrib.utils import tempdir
import tvm.contrib.graph_runtime as runtime
@@ -446,7 +450,7 @@ One sample output is listed below. It takes about 3 hours on a 32T AMD Ryzen Thr
import logging
logging.getLogger('autotvm').setLevel(logging.DEBUG)
- Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+ Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
.. _sphx_glr_download_tutorials_autotvm_tune_relay_mobile_gpu.py:
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
index 175522a..c6d5933 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
@@ -16,6 +16,10 @@ Auto-tuning a convolutional network for x86 CPU
This is a tutorial about how to tune convolution neural network
for x86 CPU.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
.. code-block:: default
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index 64e9159..b4f18af 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -19,6 +19,10 @@ The second step is running a search algorithm to explore through this space.
In this tutorial, you can learn how to perform these two steps in TVM.
The whole workflow is illustrated by a matrix multiplication example.
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+
Install dependencies
--------------------
To use autotvm package in TVM, we need to install some extra dependencies.
@@ -365,16 +369,16 @@ used to get the best config later.
.. code-block:: none
Get devices for measurement successfully!
- No: 1 GFLOPS: 9.86/9.86 result: MeasureResult(costs=(0.0272191584,), error_no=0, all_cost=0.9334566593170166, timestamp=1603167460.0891333) [('tile_y', [-1, 8]), ('tile_x', [-1, 32])],None,53
- No: 2 GFLOPS: 12.47/12.47 result: MeasureResult(costs=(0.0215312668,), error_no=0, all_cost=0.8999619483947754, timestamp=1603167461.0835187) [('tile_y', [-1, 128]), ('tile_x', [-1, 256])],None,87
- No: 3 GFLOPS: 15.16/15.16 result: MeasureResult(costs=(0.0177029772,), error_no=0, all_cost=0.8587379455566406, timestamp=1603167462.0166414) [('tile_y', [-1, 8]), ('tile_x', [-1, 512])],None,93
- No: 4 GFLOPS: 12.74/15.16 result: MeasureResult(costs=(0.021062226,), error_no=0, all_cost=0.9739360809326172, timestamp=1603167462.9981241) [('tile_y', [-1, 128]), ('tile_x', [-1, 512])],None,97
- No: 5 GFLOPS: 2.04/15.16 result: MeasureResult(costs=(0.1314596194,), error_no=0, all_cost=2.6746816635131836, timestamp=1603167465.759177) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
- No: 6 GFLOPS: 9.16/15.16 result: MeasureResult(costs=(0.0293005322,), error_no=0, all_cost=1.0533380508422852, timestamp=1603167466.8741796) [('tile_y', [-1, 4]), ('tile_x', [-1, 32])],None,52
- No: 7 GFLOPS: 13.69/15.16 result: MeasureResult(costs=(0.0196138782,), error_no=0, all_cost=0.86008620262146, timestamp=1603167467.833241) [('tile_y', [-1, 2]), ('tile_x', [-1, 512])],None,91
- No: 8 GFLOPS: 11.94/15.16 result: MeasureResult(costs=(0.022477699,), error_no=0, all_cost=0.8363852500915527, timestamp=1603167468.8411372) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
- No: 9 GFLOPS: 0.92/15.16 result: MeasureResult(costs=(0.2919428998,), error_no=0, all_cost=5.138051271438599, timestamp=1603167475.0591493) [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
- No: 10 GFLOPS: 1.15/15.16 result: MeasureResult(costs=(0.2326493638,), error_no=0, all_cost=4.178379774093628, timestamp=1603167479.4227843) [('tile_y', [-1, 1]), ('tile_x', [-1, 2])],None,10
+ No: 1 GFLOPS: 27.76/27.76 result: MeasureResult(costs=(0.0096711008,), error_no=0, all_cost=0.5818359851837158, timestamp=1604488684.0510147) [('tile_y', [-1, 1]), ('tile_x', [-1, 64])],None,60
+ No: 2 GFLOPS: 20.85/27.76 result: MeasureResult(costs=(0.012874134,), error_no=0, all_cost=0.544644832611084, timestamp=1604488684.6589487) [('tile_y', [-1, 512]), ('tile_x', [-1, 512])],None,99
+ No: 3 GFLOPS: 21.63/27.76 result: MeasureResult(costs=(0.012409730800000001,), error_no=0, all_cost=0.5541179180145264, timestamp=1604488685.2746277) [('tile_y', [-1, 8]), ('tile_x', [-1, 512])],None,93
+ No: 4 GFLOPS: 1.51/27.76 result: MeasureResult(costs=(0.1780390712,), error_no=0, all_cost=3.2551794052124023, timestamp=1604488688.5435152) [('tile_y', [-1, 2]), ('tile_x', [-1, 16])],None,41
+ No: 5 GFLOPS: 21.39/27.76 result: MeasureResult(costs=(0.0125475062,), error_no=0, all_cost=0.7667622566223145, timestamp=1604488689.156474) [('tile_y', [-1, 2]), ('tile_x', [-1, 128])],None,71
+ No: 6 GFLOPS: 7.32/27.76 result: MeasureResult(costs=(0.0366672218,), error_no=0, all_cost=1.2662115097045898, timestamp=1604488690.1566975) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 7 GFLOPS: 0.00/27.76 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f7ae8f542c1]\n [bt] (4) /workspace/build/libtvm.so(+0x12bf7d2) [0x7f7ae8f9f7d2]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x255) [0x7f7ae8fa21d5]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::Call [...]
+ No: 8 GFLOPS: 4.47/27.76 result: MeasureResult(costs=(0.060092829800000004,), error_no=0, all_cost=1.3890645503997803, timestamp=1604488701.954656) [('tile_y', [-1, 8]), ('tile_x', [-1, 64])],None,63
+ No: 9 GFLOPS: 21.15/27.76 result: MeasureResult(costs=(0.0126893048,), error_no=0, all_cost=0.5474710464477539, timestamp=1604488702.5631373) [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
+ No: 10 GFLOPS: 23.99/27.76 result: MeasureResult(costs=(0.011189972400000001,), error_no=0, all_cost=0.6243994235992432, timestamp=1604488703.1490448) [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
diff --git a/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt b/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
index 1f72f3b..f437fd3 100644
--- a/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
@@ -155,7 +155,7 @@ Additionally, note the "32" after the datatype: this is the bitwidth of the cust
.. code-block:: none
- TVMError: Check failed: name_to_code_.find(type_name) != name_to_code_.end(): Type name myfloat not registered
+ Check failed: name_to_code_.find(type_name) != name_to_code_.end() == false: Type name myfloat not registered
@@ -247,7 +247,7 @@ Now that we can express our program without errors, let's try running it!
.. code-block:: none
- TVMError: Check failed: lower: Cast lowering function for target llvm destination type 150 source type 2 not found
+ Check failed: lower == false: Cast lowering function for target llvm destination type 150 source type 2 not found
@@ -319,7 +319,7 @@ For more information, see `the source code <https://github.com/apache/incubator-
.. code-block:: none
- TVMError: Check failed: lower: Add lowering function for target llvm type 150 not found
+ Check failed: lower == false: Add lowering function for target llvm type 150 not found
@@ -521,7 +521,7 @@ Now, to actually convert the entire network, we have written `a pass in Relay <h
.. code-block:: none
- TVMError: Check failed: lower: FloatImm lowering function for target llvm type 150 not found
+ Check failed: lower == false: FloatImm lowering function for target llvm type 150 not found
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index 4b1980d..4b387f0 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
=================
-**00:38.272** total execution time for **tutorials_dev** files:
+**00:29.375** total execution time for **tutorials_dev** files:
-- **00:36.191**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:01.852**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.229**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:27.564**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:01.648**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.163**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/dev/use_pass_infra.rst.txt b/docs/_sources/tutorials/dev/use_pass_infra.rst.txt
index 499a787..ee82c02 100644
--- a/docs/_sources/tutorials/dev/use_pass_infra.rst.txt
+++ b/docs/_sources/tutorials/dev/use_pass_infra.rst.txt
@@ -267,8 +267,8 @@ Manually Apply Optimization Passes
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAPL/F1kCJSJZYkkhWZLGI1n6glVZcqhfWfL2SlkidEpZUukvWXIpPlk6+11ZuhI1WYJCCVninzRZ0gUwWXKiTVk6QRJZwlwuWXIbD1lyAYNZQvkuWVqUDFm6DS1ZojsZWUq+M1k6rm5ZAp47WWJPJVmCcDtZkv0YWeoiSVnypRNZItoRWTLeH1lSlfBYukIGWXrsWVmiuApZimkJWZKGEFkKCS1Z4mdAWdJdMlkiOTlZstZAWdIOD1mCwuRYujFCWUKeFlni+UJZOhwoWZrmPFmyLBBZkpExWTLRX1ni+k9ZQiRoWaISGVmiyUlZKkwLWcq6LFlCjilZ4ikUWUIUWVkiBC9Z6vM4WZpCPFlS5MZYgoQ [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAPL/F1gCJSJYYkkhWJLGI1j6glVYcqhfWPL2SlgidEpYUukvWHIpPlg6+11YuhI1WIJCCVjinzRY0gUwWHKiTVg6QRJYwlwuWHIbD1hyAYNYQvkuWFqUDFi6DS1YojsZWEq+M1g6rm5YAp47WGJPJViCcDtYkv0YWOoiSVjypRNYItoRWDLeH1hSlfBXukIGWHrsWViiuApYimkJWJKGEFgKCS1Y4mdAWNJdMlgiOTlYstZAWNIOD1iCwuRXujFCWEKeFlji+UJYOhwoWJrmPFiyLBBYkpExWDLRX1ji+k9YQiRoWKISGViiyUlYKkwLWMq6LFhCjilY4ikUWEIUWVgiBC9Y6vM4WJpCPFhS5MZXgoQ [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -418,8 +418,8 @@ eliminate the common expressions that used by `z` and `z1`.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAPL/F1kCJSJZYkkhWZLGI1n6glVZcqhfWfL2SlkidEpZUukvWXIpPlk6+11ZuhI1WYJCCVninzRZ0gUwWXKiTVk6QRJZwlwuWXIbD1lyAYNZQvkuWVqUDFm6DS1ZojsZWUq+M1k6rm5ZAp47WWJPJVmCcDtZkv0YWeoiSVnypRNZItoRWTLeH1lSlfBYukIGWXrsWVmiuApZimkJWZKGEFkKCS1Z4mdAWdJdMlkiOTlZstZAWdIOD1mCwuRYujFCWUKeFlni+UJZOhwoWZrmPFmyLBBZkpExWTLRX1ni+k9ZQiRoWaISGVmiyUlZKkwLWcq6LFlCjilZ4ikUWUIUWVkiBC9Z6vM4WZpCPFlS5MZYgoQ [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAPL/F1gCJSJYYkkhWJLGI1j6glVYcqhfWPL2SlgidEpYUukvWHIpPlg6+11YuhI1WIJCCVjinzRY0gUwWHKiTVg6QRJYwlwuWHIbD1hyAYNYQvkuWFqUDFi6DS1YojsZWEq+M1g6rm5YAp47WGJPJViCcDtYkv0YWOoiSVjypRNYItoRWDLeH1hSlfBXukIGWHrsWViiuApYimkJWJKGEFgKCS1Y4mdAWNJdMlgiOTlYstZAWNIOD1iCwuRXujFCWEKeFlji+UJYOhwoWJrmPFiyLBBYkpExWDLRX1ji+k9YQiRoWKISGViiyUlYKkwLWMq6LFhCjilY4ikUWEIUWVgiBC9Y6vM4WJpCPFhS5MZXgoQ [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -585,8 +585,8 @@ opt level 0 will not allow operators to be fused together. Users can pass the
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAP///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAPL/F1kCJSJZYkkhWZLGI1n6glVZcqhfWfL2SlkidEpZUukvWXIpPlk6+11ZuhI1WYJCCVninzRZ0gUwWXKiTVk6QRJZwlwuWXIbD1lyAYNZQvkuWVqUDFm6DS1ZojsZWUq+M1k6rm5ZAp47WWJPJVmCcDtZkv0YWeoiSVnypRNZItoRWTLeH1lSlfBYukIGWXrsWVmiuApZimkJWZKGEFkKCS1Z4mdAWdJdMlkiOTlZstZAWdIOD1mCwuRYujFCWUKeFlni+UJZOhwoWZrmPFmyLBBZkpExWTLRX1ni+k9ZQiRoWaISGVmiyUlZKkwLWcq6LFlCjilZ4ikUWUIUWVkiBC9Z6vM4WZpCPFlS5MZYgoQ [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAPL/F1gCJSJYYkkhWJLGI1j6glVYcqhfWPL2SlgidEpYUukvWHIpPlg6+11YuhI1WIJCCVjinzRY0gUwWHKiTVg6QRJYwlwuWHIbD1hyAYNYQvkuWFqUDFi6DS1YojsZWEq+M1g6rm5YAp47WGJPJViCcDtYkv0YWOoiSVjypRNYItoRWDLeH1hSlfBXukIGWHrsWViiuApYimkJWJKGEFgKCS1Y4mdAWNJdMlgiOTlYstZAWNIOD1iCwuRXujFCWEKeFlji+UJYOhwoWJrmPFiyLBBYkpExWDLRX1ji+k9YQiRoWKISGViiyUlYKkwLWMq6LFhCjilY4ikUWEIUWVgiBC9Y6vM4WJpCPFhS5MZXgoQ [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -769,8 +769,8 @@ pass.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kEAAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kAAAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3QAAIB/AACAfwAAgH/KWoNsAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3MAAIB/AACAfwAAgH/KWoNrAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -930,8 +930,8 @@ for users to customize the optimization level that they want to execute.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kEAAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kAAAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3QAAIB/AACAfwAAgH/KWoNsAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3MAAIB/AACAfwAAgH/KWoNrAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1092,8 +1092,8 @@ identical addition operations.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kEAAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kAAAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3QAAIB/AACAfwAAgH/KWoNsAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3MAAIB/AACAfwAAgH/KWoNrAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1255,8 +1255,8 @@ alteration pass falls in such category.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kEAAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kAAAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3QAAIB/AACAfwAAgH/KWoNsAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3MAAIB/AACAfwAAgH/KWoNrAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1343,7 +1343,7 @@ alteration pass falls in such category.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAH8K+kAAAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAACAfwAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf8OqU3MAAIB/AACAfwAAgH/KWoNrAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1482,7 +1482,7 @@ customized pass.
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKWlpZSwoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH9cCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgoKSgoKCkoKCgpKCgo [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH9kCgpaCjpDAnoJCloKOkMCegkKWgo6QwJ6CQpaCjpDAnoJCloKOkMCegkKWgo6QwIKCgpaCgoKSnoJCloKOkMCegkKWgo6QwJ6CQpaCjpDAnoJCloAAIB/egkKWgAAAACaCwpaCjpDAnoJCloKOkMCegkKWgo6QwJ6CQpaCjpDAnoJCloKOkMCegkKWgo6QwJ6CQpaCjpDAnoJCloKOkMCCgo [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1591,7 +1591,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1631,7 +1631,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1711,7 +1711,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1794,7 +1794,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1877,7 +1877,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -1962,7 +1962,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2002,7 +2002,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYEyegAAAAAKCopKCgqKSgoSiko6CwpLCgqKSgoKikoKcrpLCgqKSgoKikpaGzpMCgqKSgoKikoKCopKCjrDAgoqSnsKCopKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKiloKOsMCegmKWgo6wwJ6CYpaCjrDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNR+RkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2082,7 +2082,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYEyegAAAAAKCopKCgqKSgoSiko6CwpLCgqKSgoKikoKcrpLCgqKSgoKikpaGzpMCgqKSgoKikoKCopKCjrDAgoqSnsKCopKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKiloKOsMCegmKWgo6wwJ6CYpaCjrDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNR+RkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2165,7 +2165,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYEyegAAAAAKCopKCgqKSgoSiko6CwpLCgqKSgoKikoKcrpLCgqKSgoKikpaGzpMCgqKSgoKikoKCopKCjrDAgoqSnsKCopKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKiloKOsMCegmKWgo6wwJ6CYpaCjrDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNR+RkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2248,7 +2248,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYEyegAAAAAKCopKCgqKSgoSiko6CwpLCgqKSgoKikoKcrpLCgqKSgoKikpaGzpMCgqKSgoKikoKCopKCjrDAgoqSnsKCopKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKiloKOsMCegmKWgo6wwJ6CYpaCjrDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNR+RkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2334,7 +2334,7 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYEyegAAAAAKCopKCgqKSgoSiko6CwpLCgqKSgoKikoKcrpLCgqKSgoKikpaGzpMCgqKSgoKikoKCopKCjrDAgoqSnsKCopKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKiloKOsMCegmKWgo6wwJ6CYpaCjrDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNR+RkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2386,8 +2386,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2519,8 +2519,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2652,8 +2652,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2785,8 +2785,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -2917,8 +2917,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3049,8 +3049,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3181,8 +3181,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3316,8 +3316,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3451,8 +3451,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3586,8 +3586,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3725,8 +3725,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
@@ -3861,8 +3861,8 @@ could be like the following to enable IR dumping for ``FoldConstant`` optimizati
}
],
"b64ndarrays": [
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyegAAAAAKCgpLCgoKSwoSCks6C4pLCgoKSwoKCksKcjpMCgoKSwoKCktaG7pMCgoKSwoKCksKCgpLCjpDAwoqynsKCgpLAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKClsKOkMDegkKWwo6QwN6CQpbCjpDCwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
- "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgH8AAAAAEYGyeQAAAAAKCgpKCgoKSgoSCko6C4pKCgoKSgoKCkoKcjpLCgoKSgoKCkpaG7pLCgoKSgoKCkoKCgpKCjpDAgoqynoKCgpKAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf1wKCloKOkMCegkKWgo6QwJ6CQpaCjpDCgAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAC [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRoAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
+ "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAABAAAAAIgAQABAAAAAAAAAEAAAAAAAAAANgAAAAAAAAA2AAAAAAAAAABkCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAAAAAAAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AACAfwAAgH8AAAAAAACAfwAAgH8AAIB/AACAfwAAgH8AAAAAAAAAAAAAAAAAAAAAAACAfwAAAAAAAAAAAACAfwNReRkAAAAAAAAAAAAAAAAAAIB/AAAAAAAAAAAAAIB/AAA [...]
],
"attrs": {"tvm_version": "0.8.dev0"}
}
diff --git a/docs/_sources/tutorials/frontend/build_gcn.rst.txt b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
index e3fa277..9fcc7e5 100644
--- a/docs/_sources/tutorials/frontend/build_gcn.rst.txt
+++ b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
@@ -71,8 +71,6 @@ This part reuses the code from the above example.
.. code-block:: none
Using backend: pytorch
- /usr/local/lib/python3.6/dist-packages/dgl/base.py:45: DGLWarning: Detected an old version of PyTorch. Suggest using torch>=1.5.0 for the best experience.
- return warnings.warn(message, category=category, stacklevel=1)
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 66e7387..5ce5e80 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -28,7 +28,7 @@ This is an example of using Relay to compile a keras model and deploy it on Andr
from tvm import te
import tvm.relay as relay
from tvm import rpc
- from tvm.contrib import util, ndk, graph_runtime as runtime
+ from tvm.contrib import utils, ndk, graph_runtime as runtime
from tvm.contrib.download import download_testdata
@@ -333,7 +333,7 @@ to run this tutorial with a real device.
# change the parameters but keep the result of model as the same.
# Save the library at local temporary directory.
- tmp = util.tempdir()
+ tmp = utils.tempdir()
lib_fname = tmp.relpath("net.so")
fcompile = ndk.create_shared if not local_demo else None
lib.export_library(lib_fname, fcompile)
@@ -421,7 +421,7 @@ Execute on TVM
TVM prediction top-1: tiger cat
Evaluate inference time cost...
- Mean inference time (std dev): 13.45 ms (0.10 ms)
+ Mean inference time (std dev): 3.76 ms (0.03 ms)
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
index d5cf3d9..5344fb9 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
@@ -24,7 +24,7 @@ it on Raspberry Pi.
from tvm import te
import tvm.relay as relay
from tvm import rpc
- from tvm.contrib import util, graph_runtime as runtime
+ from tvm.contrib import utils, graph_runtime as runtime
from tvm.contrib.download import download_testdata
@@ -254,7 +254,7 @@ to run this tutorial with a real device.
# change the parameters but keep the result of model as the same.
# Save the library at local temporary directory.
- tmp = util.tempdir()
+ tmp = utils.tempdir()
lib_fname = tmp.relpath("net.tar")
lib.export_library(lib_fname)
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 07b6392..a5dd162 100644
--- a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
@@ -19,8 +19,8 @@ A quick solution is to install via pip
.. code-block:: bash
- pip install torch==1.4.0
- pip install torchvision==0.5.0
+ pip install torch==1.7.0
+ pip install torchvision==0.8.1
or please refer to official site
https://pytorch.org/get-started/locally/
@@ -28,7 +28,7 @@ https://pytorch.org/get-started/locally/
PyTorch versions should be backwards compatible but should be used
with the proper TorchVision version.
-Currently, TVM supports PyTorch 1.4 and 1.3. Other versions may
+Currently, TVM supports PyTorch 1.7 and 1.4. Other versions may
be unstable.
@@ -107,9 +107,19 @@ Load pre-trained maskrcnn from torchvision and do tracing
.. code-block:: none
- /usr/local/lib/python3.6/dist-packages/torch/tensor.py:461: RuntimeWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
+ /usr/local/lib/python3.6/dist-packages/torch/tensor.py:593: RuntimeWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
'incorrect results).', category=RuntimeWarning)
- /usr/local/lib/python3.6/dist-packages/torchvision/models/detection/roi_heads.py:392: 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).
+ /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3123: 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).
+ dtype=torch.float32)).float())) for i in range(dim)]
+ /usr/local/lib/python3.6/dist-packages/torchvision/models/detection/anchor_utils.py:147: 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).
+ torch.tensor(image_size[1] // g[1], dtype=torch.int64, device=device)] for g in grid_sizes]
+ /usr/local/lib/python3.6/dist-packages/torchvision/ops/boxes.py:128: 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).
+ boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
+ /usr/local/lib/python3.6/dist-packages/torchvision/ops/boxes.py:130: 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).
+ boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
+ /usr/local/lib/python3.6/dist-packages/torchvision/models/detection/transform.py:271: 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).
+ 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)
@@ -237,7 +247,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 2.379 seconds)
+ **Total running time of the script:** ( 1 minutes 27.428 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 68633db..1192fe8 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -14,7 +14,7 @@ Deploy a Framework-prequantized Model with TVM
This is a tutorial on loading models quantized by deep learning frameworks into TVM.
Pre-quantized model import is one of the quantization support we have in TVM. More details on
the quantization story in TVM can be found
-`here <https://discuss.tvm.ai/t/quantization-story/3920>`_.
+`here <https://discuss.tvm.apache.org/t/quantization-story/3920>`_.
Here, we demonstrate how to load and run models quantized by PyTorch, MXNet, and TFLite.
Once loaded, we can run compiled, quantized models on any hardware TVM supports.
@@ -223,7 +223,9 @@ on the PyTorch website to learn about quantization and jit.
.. code-block:: none
- /usr/local/lib/python3.6/dist-packages/torch/quantization/observer.py:803: UserWarning: must run observer before calling calculate_qparams. Returning default scale and zero point
+ /usr/local/lib/python3.6/dist-packages/torch/quantization/observer.py:121: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.
+ reduce_range will be deprecated in a future release of PyTorch."
+ /usr/local/lib/python3.6/dist-packages/torch/quantization/observer.py:990: UserWarning: must run observer before calling calculate_qparams. Returning default scale and zero point
Returning default scale and zero point "
@@ -297,7 +299,7 @@ We should see identical labels printed.
.. code-block:: none
- PyTorch top3 labels: ['tiger cat', 'Egyptian cat', 'tabby, tabby cat']
+ PyTorch top3 labels: ['tiger cat', 'lynx, catamount', 'Egyptian cat']
TVM top3 labels: ['tiger cat', 'Egyptian cat', 'tabby, tabby cat']
@@ -321,7 +323,7 @@ output values are identical out of 1000 outputs from mobilenet v2.
.. code-block:: none
- 196 in 1000 raw floating outputs identical.
+ 156 in 1000 raw floating outputs identical.
@@ -348,7 +350,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 19.40410308
+ Elapsed average ms: 12.12642622
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index a0ea3d5..e138cd3 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: 37.783540480000006
+ Elapsed average ms: 29.19852615
@@ -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 39.708 seconds)
+ **Total running time of the script:** ( 1 minutes 58.844 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
index 0fb5227..1a8a957 100644
--- a/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
@@ -14,7 +14,7 @@ Deploy a Quantized Model on Cuda
This article is an introductory tutorial of automatic quantization with TVM.
Automatic quantization is one of the quantization modes in TVM. More details on
the quantization story in TVM can be found
-`here <https://discuss.tvm.ai/t/quantization-story/3920>`_.
+`here <https://discuss.tvm.apache.org/t/quantization-story/3920>`_.
In this tutorial, we will import a GluonCV pre-trained model on ImageNet to
Relay, quantize the Relay model and then perform the inference.
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index 297bce7..9bcad37 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -319,7 +319,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 59.058 seconds)
+ **Total running time of the script:** ( 1 minutes 12.579 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/tutorials/frontend/from_keras.rst.txt b/docs/_sources/tutorials/frontend/from_keras.rst.txt
index 6cc6688..5ef27df 100644
--- a/docs/_sources/tutorials/frontend/from_keras.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_keras.rst.txt
@@ -49,13 +49,25 @@ We load a pretrained resnet-50 classification model provided by keras.
.. code-block:: default
- weights_url = "".join(
- [
- "https://github.com/fchollet/deep-learning-models/releases/",
- "download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
- ]
- )
- weights_file = "resnet50_weights.h5"
+
+ if tuple(keras.__version__.split(".")) < ("2", "4", "0"):
+ weights_url = "".join(
+ [
+ "https://github.com/fchollet/deep-learning-models/releases/",
+ "download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
+ ]
+ )
+ weights_file = "resnet50_keras_old.h5"
+ else:
+ weights_url = "".join(
+ [
+ " https://storage.googleapis.com/tensorflow/keras-applications/",
+ "resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
+ ]
+ )
+ weights_file = "resnet50_keras_new.h5"
+
+
weights_path = download_testdata(weights_url, weights_file, module="keras")
keras_resnet50 = keras.applications.resnet50.ResNet50(
include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
@@ -72,7 +84,7 @@ We load a pretrained resnet-50 classification model provided by keras.
.. code-block:: none
- File /workspace/.tvm_test_data/keras/resnet50_weights.h5 exists, skip.
+ File /workspace/.tvm_test_data/keras/resnet50_keras_new.h5 exists, skip.
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index 68071bf..1713699 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -130,7 +130,7 @@ Compile the model with relay
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/onnx.py:2677: UserWarning: Mismatched attribute type in ' : kernel_shape'
+ /workspace/docs/../python/tvm/relay/frontend/onnx.py:2694: 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))
@@ -156,7 +156,7 @@ Execute on TVM
.. code-block:: none
-
...47%, 0.01 MB, 193 KB/s, 0 seconds passed
...94%, 0.02 MB, 322 KB/s, 0 seconds passed
...100%, 0.02 MB, 482 KB/s, 0 seconds passed
+
...47%, 0.01 MB, 43 KB/s, 0 seconds passed
...94%, 0.02 MB, 86 KB/s, 0 seconds passed
...100%, 0.02 MB, 130 KB/s, 0 seconds passed
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 224, 224), 'float32'), ('TENSOR', (9, 32, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 224, 224), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1, 224, 224), 'float32'), ('TENSOR', (64, 1, 5, 5), 'float32'), (1, 1), (2, 2, 2, 2), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -197,7 +197,7 @@ By default, ONNX defines models in terms of dynamic shapes. The ONNX importer
retains that dynamism upon import, and the compiler attemps to convert the model
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.ai if you hit an error with dynamic kernels.
+please file an issue on discuss.tvm.apache.org if you hit an error with dynamic kernels.
.. _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 3cc217f..a559b95 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -20,8 +20,8 @@ A quick solution is to install via pip
.. code-block:: bash
- pip install torch==1.4.0
- pip install torchvision==0.5.0
+ pip install torch==1.7.0
+ pip install torchvision==0.8.1
or please refer to official site
https://pytorch.org/get-started/locally/
@@ -29,7 +29,7 @@ https://pytorch.org/get-started/locally/
PyTorch versions should be backwards compatible but should be used
with the proper TorchVision version.
-Currently, TVM supports PyTorch 1.4 and 1.3. Other versions may
+Currently, TVM supports PyTorch 1.7 and 1.4. Other versions may
be unstable.
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index ffaef7b..aabfc36 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -195,7 +195,7 @@ Results:
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2852: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
+ /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2948: 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:735: 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")
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index b18101a..3143076 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
=================
-**11:00.624** total execution time for **tutorials_frontend** files:
+**07:35.363** total execution time for **tutorials_frontend** files:
-- **02:39.708**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **02:02.379**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **01:59.058**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:41.527**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:38.026**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:29.540**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:26.378**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:24.076**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:17.631**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:15.814**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:14.872**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:12.388**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:12.147**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:11.251**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:09.261**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:03.152**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.919**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
-- **00:01.268**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:00.228**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
+- **01:58.844**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:27.428**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **01:12.579**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:25.217**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:22.474**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:20.137**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:19.996**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:18.257**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:13.288**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:11.599**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:10.068**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:09.004**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:08.343**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:06.834**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:06.793**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:01.893**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.550**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:00.904**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:00.154**: :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 95eb5c9..4c72aa2 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
@@ -90,7 +90,7 @@ Here we will declare a simple kernel on the local machine:
import tvm
from tvm import te
from tvm import rpc
- from tvm.contrib import util
+ from tvm.contrib import utils
n = tvm.runtime.convert(1024)
A = te.placeholder((n,), name="A")
@@ -121,7 +121,7 @@ on our webpage building server. See the detailed note in the following block.
func = tvm.build(s, [A, B], target=target, name="add_one")
# save the lib at a local temp folder
- temp = util.tempdir()
+ temp = utils.tempdir()
path = temp.relpath("lib.tar")
func.export_library(path)
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.228e-07 secs/op
+ 1.693e-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 fed9962..9921c31 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, 204 KB/s, 0 seconds passed
...3%, 0.02 MB, 345 KB/s, 0 seconds passed
...5%, 0.02 MB, 516 KB/s, 0 seconds passed
...7%, 0.03 MB, 679 KB/s, 0 seconds passed
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...41%, 0.16 MB, 2563 K
B/s, 0 seconds passed
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ssed
...83%, 0.33 MB, 4583 KB/s, 0 seconds passed
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+
...1%, 0.01 MB, 49 KB/s, 0 seconds passed
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...11%, 0.05 MB, 290 KB/s, 0 seconds passed
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ssed
...43%, 0.17 MB, 1031 KB/s, 0 seconds passed
...45%, 0.18 MB, 1076 KB/s, 0 seconds passed
...47%, 0.19 MB, 1121 KB/s, 0 seconds passed
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0.33 MB, 1905 KB/s, 0 seconds passed
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...100%, 0.40 MB, 2289 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.
@@ -277,9 +277,9 @@ back in deploy environment.
# save the graph, lib and params into separate files
- from tvm.contrib import util
+ from tvm.contrib import utils
- temp = util.tempdir()
+ temp = utils.tempdir()
path_lib = temp.relpath("deploy_lib.tar")
lib.export_library(path_lib)
print(temp.listdir())
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 b4c987d..8c6a198 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
=================
-**00:17.001** total execution time for **tutorials_get_started** files:
+**00:13.258** total execution time for **tutorials_get_started** files:
-- **00:16.359**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.385**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.165**: :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``)
+- **00:12.670**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.350**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.168**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.070**: :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 b155468..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
@@ -302,9 +302,9 @@ The following code first performs the following steps:
.. code-block:: default
from tvm.contrib import cc
- from tvm.contrib import util
+ from tvm.contrib import utils
- temp = util.tempdir()
+ temp = utils.tempdir()
fadd.save(temp.relpath("myadd.o"))
if tgt == "cuda":
fadd.imported_modules[0].save(temp.relpath("myadd.ptx"))
@@ -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 c38f5cb..db08b97 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -196,13 +196,13 @@ Compile Deep Learning Models
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This article is an introductory tutorial to deploy keras models with Relay.">
+ <div class="sphx-glr-thumbcontainer" tooltip="For us to begin with, PyTorch should be installed. TorchVision is also required since we will b...">
.. only:: html
- .. figure:: /tutorials/frontend/images/thumb/sphx_glr_from_keras_thumb.png
+ .. figure:: /tutorials/frontend/images/thumb/sphx_glr_deploy_object_detection_pytorch_thumb.png
- :ref:`sphx_glr_tutorials_frontend_from_keras.py`
+ :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py`
.. raw:: html
@@ -212,17 +212,17 @@ Compile Deep Learning Models
.. toctree::
:hidden:
- /tutorials/frontend/from_keras
+ /tutorials/frontend/deploy_object_detection_pytorch
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="For us to begin with, PyTorch should be installed. TorchVision is also required since we will b...">
+ <div class="sphx-glr-thumbcontainer" tooltip="This article is an introductory tutorial to deploy keras models with Relay.">
.. only:: html
- .. figure:: /tutorials/frontend/images/thumb/sphx_glr_deploy_object_detection_pytorch_thumb.png
+ .. figure:: /tutorials/frontend/images/thumb/sphx_glr_from_keras_thumb.png
- :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py`
+ :ref:`sphx_glr_tutorials_frontend_from_keras.py`
.. raw:: html
@@ -232,7 +232,7 @@ Compile Deep Learning Models
.. toctree::
:hidden:
- /tutorials/frontend/deploy_object_detection_pytorch
+ /tutorials/frontend/from_keras
.. raw:: html
@@ -834,7 +834,7 @@ AutoTVM : Template-based Auto Tuning
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This is a tutorial about how to tune convolution neural network for x86 CPU. ">
+ <div class="sphx-glr-thumbcontainer" tooltip="This is a tutorial about how to tune convolution neural network for x86 CPU.">
.. only:: html
diff --git a/docs/_sources/tutorials/language/reduction.rst.txt b/docs/_sources/tutorials/language/reduction.rst.txt
index c3475ee..93fd475 100644
--- a/docs/_sources/tutorials/language/reduction.rst.txt
+++ b/docs/_sources/tutorials/language/reduction.rst.txt
@@ -530,8 +530,8 @@ Here is an example for 2D convolution with filter size = [3, 3] and strides = [1
#[version = "0.0.5"]
primfn(Input_1: handle, Filter_1: handle, Output_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {Output: Buffer(Output_2: Pointer(float32), float32, [(n: int32 - 2), (n - 2)], []),
- Filter: Buffer(Filter_2: Pointer(float32), float32, [3, 3], []),
+ buffers = {Filter: Buffer(Filter_2: Pointer(float32), float32, [3, 3], []),
+ Output: Buffer(Output_2: Pointer(float32), float32, [(n: int32 - 2), (n - 2)], []),
Input: Buffer(Input_2: Pointer(float32), float32, [n, n], [stride: int32, stride_1: int32], type="auto")}
buffer_map = {Input_1: Input, Filter_1: Filter, Output_1: Output} {
for (i: int32, 0, (n - 2)) {
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index 5bae1bc..a3c696d 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -86,13 +86,13 @@ schedule computes tensor in a serial manner in a row-major order.
#[version = "0.0.5"]
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, n: int32], [stride: int32, stride_1: int32], type="auto"),
- C: Buffer(C_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B: Buffer(B_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
A: Buffer(A_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- C_2[((i*stride_2) + (j*stride_3))] = ((float32*)A_2[((i*stride_4) + (j*stride_5))]*(float32*)B_2[((i*stride) + (j*stride_1))])
+ C_2[((i*stride) + (j*stride_1))] = ((float32*)A_2[((i*stride_4) + (j*stride_5))]*(float32*)B_2[((i*stride_2) + (j*stride_3))])
}
}
}
@@ -824,15 +824,15 @@ compute_root
#[version = "0.0.5"]
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) {
- B_2[(i*stride_1)] = ((float32*)A_2[(i*stride_2)] + 1f32)
+ B_2[(i*stride)] = ((float32*)A_2[(i*stride_2)] + 1f32)
}
for (i_1: int32, 0, m) {
- C_2[(i_1*stride)] = ((float32*)B_2[(i_1*stride_1)]*2f32)
+ C_2[(i_1*stride_1)] = ((float32*)B_2[(i_1*stride)]*2f32)
}
}
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index dbc9f00..19016ee 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
=================
-**00:05.478** total execution time for **tutorials_language** files:
+**00:03.729** total execution time for **tutorials_language** files:
-- **00:02.057**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:01.013**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.723**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.691**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.310**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.258**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.228**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
-- **00:00.198**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:01.371**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.664**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.492**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.479**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.223**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.177**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.171**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:00.151**: :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 c7e517d..08536d3 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -358,9 +358,9 @@ it is a naive implementation of GEMV, just for demonstration.
return 0;
}
"""
- from tvm.contrib import util, clang
+ from tvm.contrib import utils, clang
- temp = util.tempdir()
+ temp = utils.tempdir()
ll_path = temp.relpath("temp.ll")
# Create LLVM ir from c source code
ll_code = clang.create_llvm(cc_code, output=ll_path)
@@ -400,8 +400,8 @@ The importing needs to happen before the tensorized GEMV being executed.
B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
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/tmprxwasumt/input0.cc'
- source_filename = "/tmp/tmprxwasumt/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp15t0ohtr/input0.cc'
+ source_filename = "/tmp/tmp15t0ohtr/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
@@ -540,7 +540,7 @@ ensure our implementation is correct.
func = tvm.build(s, [A, B, C], target="llvm", name="gemv")
- from tvm.topi.util import get_const_tuple
+ from tvm.topi.utils import get_const_tuple
dtype = A.dtype
ctx = tvm.context("cpu", 0)
@@ -604,9 +604,9 @@ which defines the "update" computing strategy.
return 0;
}
"""
- from tvm.contrib import util, clang
+ from tvm.contrib import utils, clang
- temp = util.tempdir()
+ temp = utils.tempdir()
ll_path = temp.relpath("temp.ll")
# Create LLVM ir from c source code
ll_code = clang.create_llvm(cc_code, output=ll_path)
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 3c407f6..254ff4e 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -65,15 +65,15 @@ together in the next schedule procedure.
#[version = "0.0.5"]
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"),
+ buffers = {B.v0: Buffer(B.v0_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B.v1: Buffer(B.v1_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"),
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_2) + (j*stride_3))] = ((float32*)A1_2[((i*stride_4) + (j*stride_5))]*3f32)
}
}
}
diff --git a/docs/_sources/tutorials/micro/micro_tflite.rst.txt b/docs/_sources/tutorials/micro/micro_tflite.rst.txt
index 7831b9a..17930ef 100644
--- a/docs/_sources/tutorials/micro/micro_tflite.rst.txt
+++ b/docs/_sources/tutorials/micro/micro_tflite.rst.txt
@@ -93,7 +93,7 @@ model with Relay.
import tvm
import tvm.micro as micro
from tvm.contrib.download import download_testdata
- from tvm.contrib import graph_runtime, util
+ from tvm.contrib import graph_runtime, utils
from tvm import relay
# %%
diff --git a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
index df8810e..79960ec 100644
--- a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:08.216** total execution time for **tutorials_micro** files:
+**00:05.266** total execution time for **tutorials_micro** files:
-- **00:08.216**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
+- **00:05.266**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index 6282de1..a6f2731 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: 46.914991 ms
+ Convolution: 19.662737 ms
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index 9004616..5c613d5 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -682,7 +682,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 13.392441 ms
+ conv2d with tensor core: 6.653226 ms
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index d888530..0a6b30f 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: 0.007604
- Baseline: 3.368604
+ Numpy running time: 0.006807
+ Baseline: 6.010087
@@ -235,7 +235,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.292741
+ Opt1: 0.122450
@@ -358,7 +358,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.330208
+ Opt2: 0.137888
@@ -476,7 +476,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.120003
+ Opt3: 0.060201
@@ -615,7 +615,7 @@ the corresponding value from the packed array.
.. code-block:: none
- Opt4: 0.106440
+ Opt4: 0.062000
@@ -754,7 +754,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.097885
+ Opt5: 0.060224
@@ -779,8 +779,8 @@ Here is the generated IR after blocking.
#[version = "0.0.5"]
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} {
attr [packedB: Pointer(float32)] "storage_scope" = "global";
@@ -899,7 +899,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.032098
+ Opt6: 0.016101
@@ -924,8 +924,8 @@ Here is the generated IR after parallelization.
#[version = "0.0.5"]
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, 1024], []),
- B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {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} {
attr [packedB: Pointer(float32)] "storage_scope" = "global";
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index 05d4376..92417c5 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
=================
-**00:27.712** total execution time for **tutorials_optimize** files:
+**00:27.475** total execution time for **tutorials_optimize** files:
-- **00:25.014**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.347**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.115**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.237**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:25.643**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:00.939**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.732**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.160**: :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 6304e58..6c67009 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -387,7 +387,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0xcc2d32c0)), stage(b, placeholder(b, 0x1d2b75dd0)), 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(m [...]
+ [stage(a, placeholder(a, 0x637d7620)), stage(b, placeholder(b, 0x8368e360)), 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(mi [...]
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index 30700e0..050978e 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
=================
-**00:00.794** total execution time for **tutorials_topi** files:
+**00:00.605** total execution time for **tutorials_topi** files:
-- **00:00.794**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.605**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/dev/hardware.rst.txt b/docs/_sources/vta/dev/hardware.rst.txt
index c8d5433..1e3c0ac 100644
--- a/docs/_sources/vta/dev/hardware.rst.txt
+++ b/docs/_sources/vta/dev/hardware.rst.txt
@@ -36,7 +36,7 @@ In addition the design adopts decoupled access-execute to hide memory access lat
To a broader extent, VTA can serve as a template deep learning accelerator design for full stack optimization, exposing a generic tensor computation interface to the compiler stack.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/blogpost/vta_overview.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/blogpost/vta_overview.png
:align: center
:width: 80%
@@ -175,7 +175,7 @@ Finally, the ``STORE`` instructions are executed by the store module exclusively
The fields of each instruction is described in the figure below.
The meaning of each field will be further explained in the :ref:`vta-uarch` section.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/developer/vta_instructions.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/developer/vta_instructions.png
:align: center
:width: 100%
@@ -191,7 +191,7 @@ VTA relies on dependence FIFO queues between hardware modules to synchronize the
The figure below shows how a given hardware module can execute concurrently from its producer and consumer modules in a dataflow fashion through the use of dependence FIFO queues, and single-reader/single-writer SRAM buffers.
Each module is connected to its consumer and producer via read-after-write (RAW) and write-after-read (WAR) dependence queues.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/developer/dataflow.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/developer/dataflow.png
:align: center
:width: 100%
@@ -258,7 +258,7 @@ There are two types of compute micro-ops: ALU and GEMM operations.
To minimize the footprint of micro-op kernels, while avoiding the need for control-flow instructions such as conditional jumps, the compute module executes micro-op sequences inside a two-level nested loop that computes the location of each tensor register location via an affine function.
This compression approach helps reduce the micro-kernel instruction footprint, and applies to both matrix multiplication and 2D convolution, commonly found in neural network operators.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/developer/gemm_core.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/developer/gemm_core.png
:align: center
:width: 100%
@@ -269,7 +269,7 @@ This tensorization intrinsic is defined by the dimensions of the input, weight a
Each data type can have a different integer precision: typically both weight and input types are low-precision (8-bits or less), while the accumulator tensor has a wider type to prevent overflows (32-bits).
In order to keep the GEMM core busy, each of the input buffer, weight buffer, and register file have to expose sufficient read/write bandwidth.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/developer/alu_core.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/developer/alu_core.png
:align: center
:width: 100%
@@ -289,7 +289,7 @@ The micro-code in the context of tensor ALU computation only takes care of speci
Load and Store Modules
~~~~~~~~~~~~~~~~~~~~~~
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/developer/2d_dma.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/developer/2d_dma.png
:align: center
:width: 100%
diff --git a/docs/_sources/vta/dev/index.rst.txt b/docs/_sources/vta/dev/index.rst.txt
index d95f6e2..2b71574 100644
--- a/docs/_sources/vta/dev/index.rst.txt
+++ b/docs/_sources/vta/dev/index.rst.txt
@@ -20,7 +20,7 @@ VTA Design and Developer Guide
This developer guide details the complete VTA-TVM hardware-software stack.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/blogpost/vta_stack.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/blogpost/vta_stack.png
:align: center
:width: 60%
diff --git a/docs/_sources/vta/install.rst.txt b/docs/_sources/vta/install.rst.txt
index 4cd1ee9..bb5c1c9 100644
--- a/docs/_sources/vta/install.rst.txt
+++ b/docs/_sources/vta/install.rst.txt
@@ -202,7 +202,7 @@ This time again, we will run the 2D convolution testbench.
Beforehand, we need to program the Pynq board FPGA with a VTA bitstream, and build the VTA runtime via RPC.
The following ``test_program_rpc.py`` script will perform two operations:
-* FPGA programming, by downloading a pre-compiled bitstream from a `VTA bitstream repository <https://github.com/uwsaml/vta-distro>`_ that matches the default ``vta_config.json`` configuration set by the host, and sending it over to the Pynq via RPC to program the Pynq's FPGA.
+* FPGA programming, by downloading a pre-compiled bitstream from a `VTA bitstream repository <https://github.com/uwsampl/vta-distro>`_ that matches the default ``vta_config.json`` configuration set by the host, and sending it over to the Pynq via RPC to program the Pynq's FPGA.
* Runtime building on the Pynq, which needs to be run every time the ``vta_config.json`` configuration is modified. This ensures that the VTA software runtime that generates the accelerator's executable via just-in-time (JIT) compilation matches the specifications of the VTA design that is programmed on the FPGA. The build process takes about 30 seconds to complete so be patient!
.. code:: bash
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 c11758a..c050d28 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
=================
-**00:08.640** total execution time for **vta_tutorials_autotvm** files:
+**00:08.439** total execution time for **vta_tutorials_autotvm** files:
-- **00:08.640**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:08.439**: :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 a3703a1..b8560b5 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -56,7 +56,7 @@ Now return to python code. Import packages.
import tvm
from tvm import te
from tvm import rpc, autotvm, relay
- from tvm.contrib import graph_runtime, util, download
+ from tvm.contrib import graph_runtime, utils, download
from tvm.autotvm.measure.measure_methods import request_remote
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
@@ -459,7 +459,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
# Export library
print("Upload...")
- temp = util.tempdir()
+ temp = utils.tempdir()
lib.save(temp.relpath("graphlib.o"))
remote.upload(temp.relpath("graphlib.o"))
lib = remote.load_module("graphlib.o")
@@ -497,7 +497,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
.. code-block:: none
Extract tasks...
-
...1%, 0.01 MB, 23 KB/s, 0 seconds passed
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ds passed
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+
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...29%, 0.21 MB, 1202 KB/s, 0 seconds passed
...30%, 0.22 MB, 1242 KB/s, 0 seconds passed
...31%, 0.23 MB, 1286 KB/s, 0 seconds passed
...32%, 0.23 MB, 1329 KB/s, 0 seconds passed
...33%, 0.24 MB, 1370 KB/s, 0 seconds passed
...34%, 0.25 MB, 1413 KB/s, 0 seconds passed
...35%, 0.26 MB, 1456 KB/s, 0 seconds passed
...36%, 0.27 MB, 1498 KB/s, 0 seconds passed
...38%, 0.27 MB, 1542 KB/s, 0 seconds passed
...39%, 0.28 MB, 1580 KB/s, 0 seconds passed
...40%, 0.29 MB, 1624 KB/s, 0 seconds passed
...41%, 0.30 MB, 1665 KB/s, 0 seconds passed
...42%, 0.30 MB, 1708 KB/s, 0 seconds passed
...43%, 0.31 MB, 1747 KB/s, 0 seconds passed
...44%, 0.32 MB, 1790 KB/s, 0 seconds passed
...45%, 0.33
MB, 1833 KB/s, 0 seconds passed
...46%, 0.34 MB, 1875 KB/s, 0 seconds passed
...47%, 0.34 MB, 1916 KB/s, 0 seconds passed
...48%, 0.35 MB, 1959 KB/s, 0 seconds passed
...49%, 0.36 MB, 1992 KB/s, 0 seconds passed
...51%, 0.37 MB, 2034 KB/s, 0 seconds passed
...52%, 0.38 MB, 2077 KB/s, 0 seconds passed
...53%, 0.38 MB, 2119 KB/s, 0 seconds passed
...54%, 0.39 MB, 2155 KB/s, 0 seconds passed
...55%, 0.40 MB, 2197 KB/s, 0 seconds passed
...56%, 0.41 MB, 2234 KB/s, 0 seconds passed
...57%, 0.41 MB, 2276 KB/s, 0 seconds passed
...58%, 0.42 MB, 2313 KB/s, 0 seconds passed
...59%, 0.43 MB, 2355 KB/s, 0 seconds passed
...60%, 0.44 MB, 2396 KB/s, 0 seconds passed
...61%, 0.45 MB, 2438 KB/s, 0 seconds passed
...63%, 0.45 MB, 2477 KB/s, 0 seconds passed
...64%, 0.46 MB, 2519 KB/s, 0 seconds passed
...65%, 0.47 MB, 2557 KB/s, 0 seconds passed
...66%, 0.48 MB, 2599 KB/s, 0 seconds passed
...67%, 0.48 MB, 2633 KB/s, 0
seconds passed
...68%, 0.49 MB, 2674 KB/s, 0 seconds passed
...69%, 0.50 MB, 2715 KB/s, 0 seconds passed
...70%, 0.51 MB, 2757 KB/s, 0 seconds passed
...71%, 0.52 MB, 2797 KB/s, 0 seconds passed
...72%, 0.52 MB, 2839 KB/s, 0 seconds passed
...73%, 0.53 MB, 2873 KB/s, 0 seconds passed
...74%, 0.54 MB, 2914 KB/s, 0 seconds passed
...76%, 0.55 MB, 2955 KB/s, 0 seconds passed
...77%, 0.55 MB, 2996 KB/s, 0 seconds passed
...78%, 0.56 MB, 3033 KB/s, 0 seconds passed
...79%, 0.57 MB, 3074 KB/s, 0 seconds passed
...80%, 0.58 MB, 3114 KB/s, 0 seconds passed
...81%, 0.59 MB, 3155 KB/s, 0 seconds passed
...82%, 0.59 MB, 3191 KB/s, 0 seconds passed
...83%, 0.60 MB, 3232 KB/s, 0 seconds passed
...84%, 0.61 MB, 3265 KB/s, 0 seconds passed
...85%, 0.62 MB, 3305 KB/s, 0 seconds passed
...86%, 0.62 MB, 3346 KB/s, 0 seconds passed
...87%, 0.63 MB, 3386 KB/s, 0 seconds passed
...89%, 0.64 MB, 3422 KB/s, 0 seconds passed
...90%, 0.65 MB, 3463 KB/s, 0 seconds passed
...91%, 0.66 MB, 3503 KB/s, 0 seconds passed
...92%, 0.66 MB, 3541 KB/s, 0 seconds passed
...93%, 0.67 MB, 3581 KB/s, 0 seconds passed
...94%, 0.68 MB, 3616 KB/s, 0 seconds passed
...95%, 0.69 MB, 3657 KB/s, 0 seconds passed
...96%, 0.70 MB, 3696 KB/s, 0 seconds passed
...97%, 0.70 MB, 3736 KB/s, 0 seconds passed
...98%, 0.71 MB, 3775 KB/s, 0 seconds passed
...99%, 0.72 MB, 3815 KB/s, 0 seconds passed
...100%, 0.73 MB, 3852 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)
@@ -565,7 +565,7 @@ It takes about 2 hours on a 16T CPU, and 6 Pynq boards.
import logging
logging.getLogger('autotvm').setLevel(logging.DEBUG)
- Finally, always feel free to ask our community for help on https://discuss.tvm.ai
+ Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
.. _sphx_glr_download_vta_tutorials_autotvm_tune_relay_vta.py:
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index a2f2ca5..addf991 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -46,7 +46,7 @@ Now return to the python code. Import packages.
import tvm
from tvm import te
from tvm import rpc, autotvm, relay
- from tvm.contrib import graph_runtime, util, download
+ from tvm.contrib import graph_runtime, utils, download
from tvm.contrib.debugger import debug_runtime
from tvm.relay import transform
@@ -225,7 +225,7 @@ The compilation steps are:
print(model + " inference graph built in {0:.2f}s!".format(build_time))
# Send the inference library over to the remote RPC server
- temp = util.tempdir()
+ temp = utils.tempdir()
lib.export_library(temp.relpath("graphlib.tar"))
remote.upload(temp.relpath("graphlib.tar"))
lib = remote.load_module("graphlib.tar")
@@ -243,8 +243,8 @@ The compilation steps are:
.. code-block:: none
-
...12%, 0.01 MB, 39 KB/s, 0 seconds passed
...25%, 0.02 MB, 77 KB/s, 0 seconds passed
...38%, 0.02 MB, 115 KB/s, 0 seconds passed
...51%, 0.03 MB, 153 KB/s, 0 seconds passed
...64%, 0.04 MB, 191 KB/s, 0 seconds passed
...77%, 0.05 MB, 223 KB/s, 0 seconds passed
...89%, 0.05 MB, 260 KB/s, 0 seconds passed
...100%, 0.06 MB, 296 KB/s, 0 seconds passed
- resnet18_v1 inference graph built in 9.23s!
+
...12%, 0.01 MB, 35 KB/s, 0 seconds passed
...25%, 0.02 MB, 71 KB/s, 0 seconds passed
...38%, 0.02 MB, 107 KB/s, 0 seconds passed
...51%, 0.03 MB, 142 KB/s, 0 seconds passed
...64%, 0.04 MB, 177 KB/s, 0 seconds passed
...77%, 0.05 MB, 213 KB/s, 0 seconds passed
...89%, 0.05 MB, 247 KB/s, 0 seconds passed
...100%, 0.06 MB, 282 KB/s, 0 seconds passed
+ resnet18_v1 inference graph built in 6.93s!
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 93a81a2..d9f4c8e 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
=================
-**00:30.995** total execution time for **vta_tutorials_frontend** files:
+**00:28.958** total execution time for **vta_tutorials_frontend** files:
-- **00:30.995**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:28.958**: :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 7ed981c..27afcbd 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -34,7 +34,7 @@ as we did in the VTA introductory tutorial.
import vta
import numpy as np
from tvm import rpc
- from tvm.contrib import util
+ from tvm.contrib import utils
from vta.testing import simulator
# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
@@ -85,7 +85,7 @@ Third, we describe the matrix multiplication computation over
The last operation is a cast and copy back to DRAM, into results tensor
:code:`C`.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/gemm_dataflow.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/gemm_dataflow.png
:align: center
Data Layout
@@ -104,7 +104,7 @@ format to match the data layout requirements imposed by the VTA tensor core.
adding the result matrix to an accumulator matrix, as shown in the
figure below.
- .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/tensor_core.png
+ .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/tensor_core.png
:align: center
:width: 480px
@@ -123,7 +123,7 @@ format to match the data layout requirements imposed by the VTA tensor core.
contiguous.
The resulting tiled tensor has a shape of (2, 4, 2, 2).
- .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/data_tiling.png
+ .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/data_tiling.png
:align: center
:width: 480px
@@ -594,8 +594,8 @@ by the VTA runtime JIT compiler.
#[version = "0.0.5"]
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
- B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+ buffers = {B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+ C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 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";
@@ -683,7 +683,7 @@ into a TVM function.
my_gemm = vta.build(s, [A, B, C], "ext_dev", env.target_host, name="my_gemm")
# Write the compiled module into an object file.
- temp = util.tempdir()
+ temp = utils.tempdir()
my_gemm.save(temp.relpath("gemm.o"))
# Send the executable over RPC
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index 63dabae..96b2fb0 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -39,7 +39,7 @@ We start by programming the Pynq's FPGA and building its RPC runtime.
import numpy as np
from tvm import rpc
- from tvm.contrib import util
+ from tvm.contrib import utils
from vta.testing import simulator
# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
@@ -92,7 +92,7 @@ shifting and clipping to the output in order to mimic a fixed-point
convolution followed by a rectified linear activation.
We describe the TVM dataflow graph of the 2D convolution layer below:
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/conv2d_dataflow.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/conv2d_dataflow.png
:align: center
This computation is intentionally too large to fit onto VTA's on-chip
@@ -119,7 +119,7 @@ manageable chunks.
loaded from DRAM into VTA's SRAM, following a 2D strided and padded memory
read.
- .. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/padding.png
+ .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/padding.png
:align: center
:width: 480px
@@ -446,7 +446,7 @@ threads split along the output channel axis.
We show how work is split when computing the 2D convolution in the figure
below.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/virtual_threading.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/virtual_threading.png
:align: center
:width: 480px
@@ -690,8 +690,8 @@ and mapping the shift, and clipping computation to the vector ALU.
#[version = "0.0.5"]
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- 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], []),
+ 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], []),
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 [res_conv: Pointer(int32)] "storage_scope" = "local.acc_buffer";
@@ -838,7 +838,7 @@ ensure correctness.
# Compile the TVM module
my_conv = vta.build(s, [data, kernel, res], "ext_dev", env.target_host, name="my_conv")
- temp = util.tempdir()
+ temp = utils.tempdir()
my_conv.save(temp.relpath("conv2d.o"))
remote.upload(temp.relpath("conv2d.o"))
f = remote.load_module("conv2d.o")
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 a563aa4..ddc15e8 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -37,7 +37,7 @@ We start by programming the Pynq's FPGA and building its RPC runtime.
import vta
import numpy as np
from tvm import rpc
- from tvm.contrib import util
+ from tvm.contrib import utils
from vta.testing import simulator
# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
@@ -87,7 +87,7 @@ shifting and clipping to the output in order to mimic a fixed-point
matrix multiplication followed by a rectified linear activation.
We describe the TVM dataflow graph of the fully connected layer below:
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/fc_dataflow.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/fc_dataflow.png
:align: center
This computation is intentionally too large to fit onto VTA's on-chip
@@ -296,7 +296,7 @@ TVM to pattern-match tensorization.
We show the outcome of blocking on the computation schedule in the diagram
below:
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/blocking.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/blocking.png
:align: center
:width: 480px
@@ -381,8 +381,8 @@ below:
#[version = "0.0.5"]
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 [data_buf: Pointer(int8)] "storage_scope" = "global";
@@ -553,8 +553,8 @@ and mapping the shift, and clipping computation to the vector ALU.
#[version = "0.0.5"]
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";
@@ -667,7 +667,7 @@ ensure correctness.
# Compile the TVM module
my_gemm = vta.build(s, [data, weight, res], "ext_dev", env.target_host, name="my_gemm")
- temp = util.tempdir()
+ temp = utils.tempdir()
my_gemm.save(temp.relpath("gemm.o"))
remote.upload(temp.relpath("gemm.o"))
f = remote.load_module("gemm.o")
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 7148875..cdcb6a0 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
=================
-**00:04.160** total execution time for **vta_tutorials_optimize** files:
+**00:03.930** total execution time for **vta_tutorials_optimize** files:
-- **00:03.485**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.675**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:03.263**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.667**: :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 9e46d60..1d044af 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
=================
-**00:01.238** total execution time for **vta_tutorials** files:
+**00:00.819** total execution time for **vta_tutorials** files:
-- **00:00.619**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.619**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.413**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.406**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/vta/tutorials/vta_get_started.rst.txt b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
index e9a74b9..af21ce8 100644
--- a/docs/_sources/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
@@ -80,7 +80,7 @@ the board with a VTA bitstream.
# We'll need the TVM RPC module and the VTA simulator module
from tvm import rpc
- from tvm.contrib import util
+ from tvm.contrib import utils
from vta.testing import simulator
# We read the Pynq RPC host IP address and port number from the OS environment
@@ -133,7 +133,7 @@ add :code:`A_buf` to :code:`B_buf` to produce :code:`C_buf`.
The last operation is a cast and copy back to DRAM, into results tensor
:code:`C`.
-.. image:: https://raw.githubusercontent.com/uwsaml/web-data/main/vta/tutorial/vadd_dataflow.png
+.. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/vadd_dataflow.png
:align: center
Input Placeholders
@@ -301,8 +301,8 @@ After we construct the schedule, by default the schedule computes
#[version = "0.0.5"]
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
- C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [A_buf: Pointer(int32)] "storage_scope" = "global";
@@ -453,8 +453,8 @@ with an :code:`env.alu` pragma.
#[version = "0.0.5"]
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
- C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [A_buf: Pointer(int32)] "storage_scope" = "local.acc_buffer" {
@@ -547,7 +547,7 @@ execution.
# Write the compiled module into an object file.
- temp = util.tempdir()
+ temp = utils.tempdir()
my_vadd.save(temp.relpath("vadd.o"))
# Send the executable over RPC
diff --git a/docs/_static/css/gallery.css b/docs/_static/css/gallery.css
new file mode 100644
index 0000000..91f489d
--- /dev/null
+++ b/docs/_static/css/gallery.css
@@ -0,0 +1,192 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+.sphx-glr-thumbcontainer {
+ background: #fff;
+ box-shadow: none;
+ float: left;
+ margin: 12px;
+ min-height: 310px;
+ padding-top: 5px;
+ position: relative;
+ background: url(../img/pattern.svg) no-repeat bottom, #005077;
+ transition:0.1s all;
+ background-size: cover;
+ transform:scale(1);
+}
+@media (min-width:1025px){
+ .sphx-glr-thumbcontainer:hover {
+ transform:scale(1.05);
+ box-shadow: 0px 20px 32px rgba(3, 109, 162, 0.15);
+ }
+}
+ .sphx-glr-thumbcontainer a.internal {
+ bottom: 0;
+ display: flex;
+ align-items: flex-end;
+ left: 0;
+ padding: 0 25px 30px;
+ position: absolute;
+ text-decoration: none;
+ right: 0;
+ top: 0;
+ font-size:16px;
+ color:#ffffff;
+ font-style: normal;
+ }
+ /* Next one is to avoid Sphinx traditional theme to cover all the
+ thumbnail with its default link Background color */
+ .sphx-glr-thumbcontainer a.internal:hover {
+ background-color: transparent;
+ }
+
+ .sphx-glr-thumbcontainer p {
+ margin: 0 0 .1em 0;
+ }
+ .sphx-glr-thumbcontainer .figure {
+ margin: 10px;
+ width: 235px;
+ }
+ .sphx-glr-thumbcontainer img {
+ /* display: inline; */
+ max-height: 160px;
+ width: 160px;
+ display:none;
+ }
+ /* .sphx-glr-thumbcontainer[tooltip]:hover:after {
+ background: rgba(0, 0, 0, 0.8);
+ -webkit-border-radius: 5px;
+ -moz-border-radius: 5px;
+ border-radius: 5px;
+ color: #fff;
+ content: attr(tooltip);
+ left: 95%;
+ padding: 5px 15px;
+ position: absolute;
+ z-index: 98;
+ width: 220px;
+ bottom: 52%;
+ }
+ .sphx-glr-thumbcontainer[tooltip]:hover:before {
+ border: solid;
+ border-color: #333 transparent;
+ border-width: 18px 0 0 20px;
+ bottom: 58%;
+ content: '';
+ left: 85%;
+ position: absolute;
+ z-index: 99;
+ } */
+
+ .highlight-pytb pre {
+ background-color: #ffe4e4;
+ border: 1px solid #f66;
+ margin-top: 10px;
+ padding: 7px;
+ }
+
+ .sphx-glr-script-out {
+ color: #888;
+ margin: 0;
+ }
+ p.sphx-glr-script-out {
+ padding-top: 0.7em;
+ }
+ .sphx-glr-script-out .highlight {
+ background-color: transparent;
+ margin-left: 2.5em;
+ margin-top: -2.1em;
+ }
+ .sphx-glr-script-out .highlight pre {
+ background-color: #fafae2;
+ border: 0;
+ max-height: 30em;
+ overflow: auto;
+ padding-left: 1ex;
+ margin: 0px;
+ word-break: break-word;
+ }
+ .sphx-glr-script-out + p {
+ margin-top: 1.8em;
+ }
+ blockquote.sphx-glr-script-out {
+ margin-left: 0pt;
+ }
+
+ div.sphx-glr-footer {
+ text-align: center;
+ }
+
+ div.binder-badge {
+ margin: 1em auto;
+ vertical-align: middle;
+ }
+
+ div.sphx-glr-download {
+ margin: 1em auto;
+ vertical-align: middle;
+ }
+
+ div.sphx-glr-download a {
+ background-color: #ffc;
+ background-image: linear-gradient(to bottom, #FFC, #d5d57e);
+ border-radius: 4px;
+ border: 1px solid #c2c22d;
+ color: #000;
+ display: inline-block;
+ font-weight: bold;
+ padding: 1ex;
+ text-align: center;
+ }
+
+
+ div.sphx-glr-download a:hover {
+ box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 5px rgba(0,0,0,.25);
+ text-decoration: none;
+ background-image: none;
+ background-color: #d5d57e;
+ }
+
+ .sphx-glr-example-title > :target::before {
+ display: block;
+ content: "";
+ margin-top: -50px;
+ height: 50px;
+ visibility: hidden;
+ }
+
+ ul.sphx-glr-horizontal {
+ list-style: none;
+ padding: 0;
+ }
+ ul.sphx-glr-horizontal li {
+ display: inline;
+ }
+ ul.sphx-glr-horizontal img {
+ height: auto !important;
+ }
+
+ .sphx-glr-single-img {
+ margin: auto;
+ display: block;
+ max-width: 100%;
+ }
+
+ .sphx-glr-multi-img {
+ max-width: 42%;
+ height: auto;
+ }
+
+ p.sphx-glr-signature a.reference.external {
+ -moz-border-radius: 5px;
+ -webkit-border-radius: 5px;
+ border-radius: 5px;
+ padding: 3px;
+ font-size: 75%;
+ text-align: right;
+ margin-left: auto;
+ display: table;
+ }
+
\ No newline at end of file
diff --git a/docs/_static/css/tlcpack_theme.css b/docs/_static/css/tlcpack_theme.css
new file mode 100644
index 0000000..f15293d
--- /dev/null
+++ b/docs/_static/css/tlcpack_theme.css
@@ -0,0 +1,1054 @@
+@charset "UTF-8";
+@import url("https://fonts.googleapis.com/css2?family=PT+Sans+Caption:wght@400;700&display=swap");
+@import url("https://fonts.googleapis.com/css2?family=PT+Sans:wght@400;700&display=swap");
+@import url("https://fonts.googleapis.com/css2?family=Ubuntu:ital,wght@0,300;0,400;0,500;0,700;1,300;1,400;1,700&display=swap");
+@import url("https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@300;400;500;600;700&display=swap");
+/** Theme Defualt Color **/
+/* Media Quires */
+*, :after, :before {
+ box-sizing: border-box;
+ -webkit-font-smoothing: antialiased;
+ -moz-osx-font-smoothing: grayscale;
+}
+
+body {
+ margin: 0;
+ font-family: "PT Sans", sans-serif;
+ font-weight: 400;
+ font-size: 16px;
+ line-height: 24px;
+ background: #ffffff;
+}
+body.scroll-hide {
+ overflow: hidden;
+}
+
+p {
+ font-size: 14px;
+ line-height: 25px;
+ color: #3c3c3c;
+ margin-bottom: 15px;
+ font-family: "PT Sans", sans-serif;
+ font-weight: 400;
+}
+p a {
+ color: #0379b6;
+}
+p a:visited {
+ color: #0379b6;
+}
+
+hr {
+ margin: 19px 0;
+}
+
+a:visited {
+ color: #0379b6;
+}
+
+h1, h2, h3 {
+ font-family: "PT Sans Caption", sans-serif;
+ font-weight: 700;
+ margin-bottom: 15px;
+}
+@media only screen and (max-width : 991px) {
+ h1 a.headerlink, h2 a.headerlink, h3 a.headerlink {
+ display: none !important;
+ }
+}
+
+h1 {
+ font-size: 30px;
+ line-height: 30px;
+ color: #3c3c3c;
+}
+
+h2 {
+ font-size: 25px;
+ line-height: 30px;
+ color: #303030;
+}
+
+h3 {
+ font-size: 20px;
+ line-height: 25px;
+ color: #3a3a3a;
+}
+
+.dropdown-menu {
+ background: #ffffff;
+ border-radius: 0px;
+ padding: 9px 8px;
+ max-width: 158px;
+ width: 100%;
+ min-width: 158px;
+ margin-top: 12px;
+ border: 0px;
+ left: 15px !important;
+ box-shadow: 0 0 4px rgba(0, 0, 0, 0.5);
+}
+.dropdown-menu::after {
+ content: "";
+ position: absolute;
+ top: -5px;
+ right: 15px;
+ border-radius: 3px;
+ width: 12px;
+ height: 12px;
+ background: #ffffff;
+ transform: rotate(45deg);
+}
+.dropdown-menu ul {
+ margin: 0;
+ padding: 0;
+ list-style: none;
+}
+.dropdown-menu ul li {
+ padding: 5px 0;
+ border-bottom: 1px solid rgba(37, 45, 90, 0.2);
+}
+.dropdown-menu ul li:first-child {
+ padding-top: 0px;
+}
+.dropdown-menu ul li:last-child {
+ border-bottom: 0px;
+ padding-bottom: 0px;
+}
+.dropdown-menu ul li a {
+ color: #505d68;
+ font-weight: 400;
+ font-size: 14px;
+ line-height: 19px;
+ font-family: "Ubuntu", sans-serif;
+ display: block;
+}
+
+.highlight .c1 {
+ color: #27AE60;
+ font-family: "IBM Plex Mono", monospace;
+ font-weight: 400;
+}
+.highlight .k, .highlight .kn, .highlight .nb {
+ color: #27AE60;
+}
+
+.section {
+ padding-bottom: 10px;
+}
+.section .section h3 {
+ color: #3a3a3a;
+ font-size: 17px;
+ line-height: 20px;
+}
+
+.rst-content code.literal, .rst-content tt.literal, code.literal {
+ white-space: nowrap;
+}
+
+.rst-content .sphx-glr-thumbcontainer {
+ background: #fff;
+ box-shadow: none;
+ float: left;
+ margin: 12px;
+ min-height: 310px;
+ padding-top: 35px;
+ position: relative;
+ background: url(../img/pattern.svg) no-repeat bottom, #005077;
+ transition: 0.1s all;
+ background-size: cover;
+ transform: scale(1);
+ border-radius: 0;
+}
+@media (min-width: 1025px) {
+ .rst-content .sphx-glr-thumbcontainer:hover {
+ transform: scale(1.05);
+ box-shadow: 0px 20px 32px rgba(3, 109, 162, 0.15);
+ border: none;
+ }
+}
+.rst-content .sphx-glr-thumbcontainer a.internal {
+ bottom: 0;
+ display: flex;
+ align-items: flex-end;
+ left: 0;
+ padding: 0 25px 30px;
+ position: absolute;
+ text-decoration: none;
+ right: 0;
+ top: 0;
+ font-size: 16px;
+ color: #ffffff;
+ font-style: normal;
+}
+.rst-content .sphx-glr-thumbcontainer .figure {
+ margin: 0;
+ width: 235px;
+}
+.rst-content div.sphx-glr-download a {
+ background-color:#0379B6;
+ background-image: none;
+ border-radius: 0;
+ padding:0;
+ border:none;
+ line-height: 0;
+}
+.rst-content div.sphx-glr-download a code.download{
+ color:#ffffff;
+ position: relative;
+ padding: 8px 25px 8px 45px;
+ line-height: 25px;
+}
+.rst-content div.sphx-glr-download a code.download span:first-child:before{
+ content:'';
+ height:14px;
+ width:14px;
+ background:url(../img/dwonloaddoc.svg) no-repeat;
+ position: absolute;
+ left:25px;
+ top:50%;
+ transform: translateY(-50%);
+ margin:0;
+}
+.rst-content div.sphx-glr-download a:hover {
+ background-color:#0379B6;
+ box-shadow:none;
+}
+.rst-content .sphx-glr-thumbcontainer .figure .headerlink {
+ display:none;
+}
+.rst-content .sphx-glr-thumbcontainer img {
+ max-height: 160px;
+ width: 160px;
+ display: block;
+ margin: 0 auto;
+}
+.rst-content .sphx-glr-thumbcontainer[tooltip]:hover:after, .rst-content .sphx-glr-thumbcontainer[tooltip]:hover:before {
+ content: none;
+}
+.rst-content .note {
+ background: #F2F8FB;
+ padding: 20px;
+}
+.rst-content .note .admonition-title {
+ background: #0379b6 !important;
+}
+.rst-content .admonition-title, .rst-content .wy-alert-title {
+ padding: 7px 20px 7px 38px;
+ font-weight: 700;
+ font-family: "PT Sans", sans-serif;
+ font-size: 14px;
+ line-height: 25px;
+ position: relative;
+ margin: -20px -20px 20px;
+}
+.rst-content .admonition-title::before, .rst-content .wy-alert-title::before {
+ content: "";
+ height: 12px;
+ width: 12px;
+ background: url(../img/note.svg) no-repeat;
+ position: absolute;
+ left: 20px;
+ top: 13px;
+}
+.rst-content blockquote {
+ margin-left: 0;
+ margin-bottom: 15px;
+}
+.rst-content div[class^=highlight], .rst-content pre.literal-block {
+ border: none;
+ background: #F1F4F8;
+ color: #252d5a;
+ margin-bottom: 20px;
+}
+.rst-content div[class^=highlight] pre, .rst-content pre.literal-block pre {
+ font-size: 11px;
+ line-height: 25px;
+ padding: 20px;
+ font-family: "IBM Plex Mono", monospace;
+ font-weight: 400;
+}
+
+.rst-content .section ol, .rst-content ol.arabic, .wy-plain-list-decimal, article ol {
+ margin-bottom: 15px;
+}
+.rst-content .section ol li, .rst-content ol.arabic li, .wy-plain-list-decimal li, article ol li {
+ font-size: 14px;
+ line-height: 25px;
+ color: #252d5a;
+ font-family: "PT Sans", sans-serif;
+ font-weight: 400;
+ margin-left: 16px;
+ list-style: disc;
+}
+
+.rst-content .section ul, .rst-content .toctree-wrapper ul, .wy-plain-list-disc, article ul {
+ font-size: 14px;
+ color: #252d5a;
+ line-height: 25px;
+ margin-bottom: 15px;
+ list-style: none;
+}
+.rst-content .section ul li, .rst-content .toctree-wrapper ul li, .wy-plain-list-disc li, article ul li {
+ margin-left: 16px;
+ position: relative;
+ list-style: none;
+}
+.rst-content .section ul li:before, .rst-content .toctree-wrapper ul li:before, .wy-plain-list-disc li:before, article ul li:before {
+ content: "•";
+ color: #0379b6;
+ font-weight: bold;
+ display: inline-block;
+ width: 1em;
+ margin-left: -16px;
+ position: absolute;
+}
+.rst-content .section ul li li, .rst-content .toctree-wrapper ul li li, .wy-plain-list-disc li li, article ul li li {
+ position: relative;
+ list-style: none;
+}
+.rst-content .section ul li li:before, .rst-content .toctree-wrapper ul li li:before, .wy-plain-list-disc li li:before, article ul li li:before {
+ content: none;
+}
+.rst-content .section ul li li:after, .rst-content .toctree-wrapper ul li li:after, .wy-plain-list-disc li li:after, article ul li li:after {
+ content: "";
+ height: 4px;
+ width: 4px;
+ background: #EFA867;
+ display: inline-block;
+ position: absolute;
+ transform: rotate(45deg);
+ top: 10px;
+ left: -16px;
+}
+.rst-content .section ul li li li, .rst-content .toctree-wrapper ul li li li, .wy-plain-list-disc li li li, article ul li li li {
+ position: relative;
+ list-style: none;
+}
+.rst-content .section ul li li li:before, .rst-content .toctree-wrapper ul li li li:before, .wy-plain-list-disc li li li:before, article ul li li li:before {
+ content: none;
+}
+.rst-content .section ul li li li:after, .rst-content .toctree-wrapper ul li li li:after, .wy-plain-list-disc li li li:after, article ul li li li:after {
+ content: "";
+ height: 4px;
+ width: 4px;
+ background: #252d5a;
+ display: inline-block;
+ position: absolute;
+ transform: rotate(45deg);
+ top: 10px;
+ left: -16px;
+}
+
+.docutils dt {
+ display: inline-block;
+ padding: 5px 15px;
+ background: rgba(3, 121, 182, 0.1);
+ border-left: 2px solid #0379b6;
+ font-weight: 700;
+ font-size: 14px;
+ font-family: "PT Sans", sans-serif;
+ color: #0379b6;
+}
+
+footer .btn {
+ min-width: 168px;
+ font-size: 12px;
+ color: #ffffff;
+ box-shadow: none;
+ text-transform: uppercase;
+ font-weight: 700;
+ border: 1px solid #3c88b5;
+ color: #ffffff !important;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ padding: 0 10px;
+ height: 40px;
+ background: transparent !important;
+ border-radius: 0;
+}
+@media only screen and (max-width : 991px) {
+ footer .btn {
+ min-width: 126px;
+ }
+}
+footer .btn:visited {
+ color: #ffffff !important;
+}
+footer .btn:hover {
+ background: transparent !important;
+}
+footer .btn:active {
+ padding: 0 10px;
+ box-shadow: none;
+}
+footer .btn .fa {
+ display: none;
+}
+footer .btn.float-left {
+ color: #3c88b5 !important;
+}
+footer .btn.float-left:before {
+ content: "";
+ display: inline-block;
+ width: 11px;
+ height: 8px;
+ margin-right: 10px;
+ background: url(../img/prevarrow.svg) no-repeat;
+ margin-top: 1px;
+}
+footer .btn.float-right {
+ background: #0379b6 !important;
+}
+footer .btn.float-right:after {
+ content: "";
+ display: inline-block;
+ width: 11px;
+ height: 8px;
+ margin-left: 10px;
+ background: url(../img/nextarrow.svg) no-repeat;
+ margin-top: 1px;
+}
+
+/** Header Css Start **/
+@media (min-width: 992px) {
+ .fixed {
+ top: 61px;
+ }
+}
+.header {
+ padding: 18px 0;
+ transition: all 0.5s;
+ background: linear-gradient(141.15deg, rgba(0, 0, 0, 0.5) 1.1%, #008DD2 99.38%), #252D5A;
+}
+@media (min-width: 992px) {
+ .header .innercontainer {
+ max-width: 1278px;
+ margin-left: 60px;
+ padding: 0 50px;
+ }
+}
+.header.fixed {
+ position: fixed;
+ top: 0;
+ left: 0;
+ width: 100%;
+ background: linear-gradient(141.15deg, rgba(0, 0, 0, 0.5) 1.1%, #008dd2 99.38%), #252d5a;
+ animation: smoothScroll 0.5s forwards;
+ box-shadow: 0 10px 40px 0 rgba(0, 0, 0, 0.3);
+ z-index: 999;
+}
+@media only screen and (max-width : 991px) {
+ .header {
+ padding: 30px 30px;
+ position: fixed;
+ width: 100%;
+ top: 0;
+ z-index: 9999;
+ left: 0;
+ }
+}
+.header .headerInner .headerLogo img {
+ display: block;
+}
+@media only screen and (max-width : 767px) {
+ .header .headerInner .headerLogo img {
+ max-width: 75px;
+ }
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .headerNav {
+ position: fixed;
+ right: 0;
+ top: 0;
+ height: 100vh;
+ width: 249px;
+ background-color: #ffffff;
+ transform: scaleX(0);
+ transform-origin: right;
+ transition: all 0.3s ease-in-out;
+ padding: 76px 49px;
+ overflow: auto;
+ }
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .headerNav .nav {
+ display: flex;
+ flex-direction: column;
+ margin-bottom: 9px;
+ }
+}
+.header .headerInner .headerNav .nav .nav-item {
+ padding: 0 15px;
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .headerNav .nav .nav-item {
+ padding: 0;
+ margin-bottom: 35px;
+ }
+}
+@media only screen and (max-width : 1024px) {
+ .header .headerInner .headerNav .nav .nav-item {
+ padding: 0 10px;
+ }
+}
+.header .headerInner .headerNav .nav .nav-item .nav-link {
+ color: #ffffff;
+ text-transform: uppercase;
+ font-weight: 400;
+ font-size: 14px;
+ line-height: 18px;
+ letter-spacing: 1px;
+ padding: 0 4px;
+ position: relative;
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .headerNav .nav .nav-item .nav-link {
+ font-size: 16px;
+ line-height: 21px;
+ letter-spacing: 1px;
+ color: #252d5a;
+ padding: 0;
+ }
+}
+.header .headerInner .headerNav .nav .nav-item .nav-link::after {
+ content: "";
+ left: auto;
+ right: 0;
+ bottom: -5px;
+ height: 2px;
+ width: 0%;
+ position: absolute;
+ border-radius: 50px;
+ background-color: #ffffff;
+ transition: 0.3s all ease-in-out;
+}
+.header .headerInner .headerNav .nav .nav-item .nav-link:hover::after {
+ width: 100%;
+ right: auto;
+ left: 0;
+}
+.header .headerInner .headerNav.opne {
+ transform: scaleX(1);
+}
+.header .headerInner .headerNav .navCloseBtn {
+ display: none;
+ background-color: transparent;
+ position: absolute;
+ top: 15px;
+ right: 15px;
+ border: 0;
+ padding: 0;
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .headerNav .navCloseBtn {
+ display: block;
+ }
+}
+.header .headerInner .headerNav .navCloseBtn img {
+ display: block;
+}
+.header .headerInner .headerNav .responsivetlcdropdown {
+ display: none;
+}
+.header .headerInner .headerNav .responsivetlcdropdown .btn-link {
+ background-color: transparent;
+ border: 0px;
+ font-size: 16px;
+ line-height: 21px;
+ letter-spacing: 1px;
+ color: #252d5a;
+ padding: 0;
+ outline: 0;
+ text-decoration: none;
+ cursor: pointer;
+}
+.header .headerInner .headerNav .responsivetlcdropdown ul {
+ padding: 29px 0 0 12px;
+ margin: 0;
+}
+.header .headerInner .headerNav .responsivetlcdropdown ul li {
+ list-style: none;
+ margin-bottom: 10px;
+}
+.header .headerInner .headerNav .responsivetlcdropdown ul li a {
+ font-size: 14px;
+ line-height: 21px;
+ color: #505d68;
+ display: block;
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .headerNav .responsivetlcdropdown {
+ display: block;
+ }
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .tlcDropdown {
+ display: none;
+ }
+}
+.header .headerInner .tlcDropdown .dropdown {
+ position: relative;
+}
+.header .headerInner .tlcDropdown .dropdown .btn-link {
+ padding: 0 18px 0px 1px;
+ background-color: transparent;
+ font-size: 14px;
+ line-height: 15px;
+ text-align: center;
+ letter-spacing: 1.16667px;
+ text-transform: uppercase;
+ color: #ffffff;
+ position: relative;
+ border: 0;
+ border-bottom: 1px solid #ffffff;
+ border-radius: 0px;
+ border-color: #ffffff !important;
+ box-shadow: inherit;
+ outline: 0;
+ text-decoration: none;
+ cursor: pointer;
+}
+.header .headerInner .tlcDropdown .dropdown .btn-link:hover {
+ color: #ffffff !important;
+}
+.header .headerInner .tlcDropdown .dropdown .btn-link::after {
+ content: "";
+ position: absolute;
+ right: 3px;
+ top: 50%;
+ width: 8px;
+ height: 5px;
+ border: 0px;
+ margin: 0;
+ transform: translate(0, -50%);
+ background: url(../img/dropdown-icon.svg) no-repeat center;
+}
+.header .headerInner .responsiveMenuIcon {
+ display: none;
+}
+@media only screen and (max-width : 991px) {
+ .header .headerInner .responsiveMenuIcon {
+ display: block;
+ }
+}
+.header .headerInner .responsiveMenuIcon .btn-menu {
+ padding: 0;
+ border: 0px;
+ background-color: transparent;
+}
+
+.wy-breadcrumbs .br-arrow {
+ font-family: "PT Sans", sans-serif;
+ font-size: 14px;
+ margin-right: 2px;
+ color: #0379b6;
+}
+
+.wy-nav-side {
+ background: #F8F9FA;
+}
+@media (min-width: 1100px) {
+ .wy-nav-side {
+ width: 400px;
+ }
+}
+.wy-nav-side .wy-side-scroll {
+ padding-top: 45px;
+}
+@media (min-width: 1100px) {
+ .wy-nav-side .wy-side-scroll {
+ width: 420px;
+ }
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-side .wy-side-scroll {
+ padding: 0 30px 144px;
+ }
+}
+.wy-nav-side .wy-side-nav-search {
+ width: 245px;
+ background: transparent;
+ padding: 0;
+ margin-left: auto;
+ margin-right: 50px;
+ text-align: center;
+}
+
+.wy-side-nav-search>div.version {
+ align-content: center;
+ margin-top: -.4045em;
+ margin-bottom: .809em;
+ font-weight: normal;
+ margin-left: auto;
+ text-align: center;
+ color: #303030;
+}
+
+@media only screen and (max-width : 991px) {
+ .wy-nav-side .wy-side-nav-search {
+ width: 100%;
+ max-width: 300px;
+ margin-right: auto;
+ margin-left: 0;
+ }
+}
+.wy-nav-side .wy-side-nav-search input[type=text] {
+ box-shadow: none;
+ border: none;
+ padding: 8px 12px;
+ font-size: 14px;
+ line-height: 25px;
+ border-radius: 0;
+ font-family: "PT Sans", sans-serif;
+ font-weight: 400;
+ color: #252d5a;
+}
+.wy-nav-side .wy-side-nav-search a {
+ font-size: 18px;
+ line-height: 30px;
+ font-family: "PT Sans Caption", sans-serif;
+ color: #252d5a;
+}
+.wy-nav-side .wy-menu-vertical {
+ width: 245px;
+ margin-left: auto;
+ margin-right: 50px;
+}
+.wy-nav-side .wy-menu-vertical .caption {
+ padding-left: 0;
+ font-size: 100%;
+}
+.wy-nav-side .wy-menu-vertical a {
+ font-size: 15px;
+ color: #252d5a;
+ line-height: 30px;
+ font-family: "PT Sans Caption", sans-serif;
+ font-weight: 500;
+ padding: 10px 0;
+ transition: 0.2s all;
+}
+.wy-nav-side .wy-menu-vertical a:hover {
+ background: transparent;
+ color: #0379b6 !important;
+}
+.wy-nav-side .wy-menu-vertical li.current {
+ background: transparent;
+}
+.wy-nav-side .wy-menu-vertical li.current > a {
+ color: #252d5a;
+ position: relative;
+}
+.wy-nav-side .wy-menu-vertical li.current > a span.toctree-expand:before {
+ content: none;
+ font-size: 24px;
+}
+.wy-nav-side .wy-menu-vertical li.current > a span.toctree-expand:after {
+ background: url(../img/hide.svg) no-repeat;
+}
+.wy-nav-side .wy-menu-vertical li span.toctree-expand:before {
+ content: none;
+}
+.wy-nav-side .wy-menu-vertical li span.toctree-expand:after {
+ content: "";
+ position: absolute;
+ left: -15px;
+ top: 20px;
+ background: url(../img/show.svg) no-repeat;
+ height: 8px;
+ width: 8px;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2 span.toctree-expand:after {
+ left: 3px;
+ top: 10px;
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-side .wy-menu-vertical li.toctree-l2 span.toctree-expand:after {
+ top: 15px;
+ }
+}
+.wy-nav-side .wy-menu-vertical li.on a {
+ padding: 0;
+ color: #252d5a;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l1.current > a {
+ padding: 10px 0;
+ border: none;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l1.current > a .toctree-expand {
+ /*display: none;*/
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l1.current a {
+ border: none;
+ background: transparent !important;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2 a {
+ padding: 0 0 0 18px;
+ color: #252d5a;
+ font-size: 14px;
+ font-family: "PT Sans", sans-serif;
+ margin-bottom: 5px;
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-side .wy-menu-vertical li.toctree-l2 a {
+ padding: 5px 0 5px 18px;
+ }
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current > a {
+ background: transparent;
+ color: #0379b6;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3.current > a {
+ color: #0379b6;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3 a {
+ padding: 0 0 0 38px;
+ font-size: 14px;
+ font-family: "PT Sans", sans-serif;
+ font-weight: 400;
+ margin-bottom: 5px;
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3 a {
+ padding: 5px 0 5px 38px;
+ }
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3 a span.toctree-expand:after {
+ left: 22px;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3 .toctree-l4 {
+ margin-bottom: 5px;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3 .toctree-l4.current > a {
+ color: #0379b6;
+}
+.wy-nav-side .wy-menu-vertical li.toctree-l2.current .toctree-l3 .toctree-l4 a {
+ padding: 0 0 0 48px;
+}
+
+.wy-nav-content-wrap {
+ padding-top: 86px;
+}
+@media (min-width: 1100px) {
+ .wy-nav-content-wrap {
+ margin-left: 400px;
+ }
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-content-wrap {
+ padding-top: 30px;
+ }
+}
+.wy-nav-content-wrap .wy-nav-content {
+ padding: 0;
+ max-width: 100%;
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-content-wrap .wy-nav-content {
+ margin-top: 144px;
+ }
+}
+.wy-nav-content-wrap .wy-nav-content .document, .wy-nav-content-wrap .wy-nav-content div[role~=navigation] {
+ max-width: 944px;
+ padding: 0 50px;
+ width: 100%;
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-content-wrap .wy-nav-content .document, .wy-nav-content-wrap .wy-nav-content div[role~=navigation] {
+ padding: 0 30px;
+ }
+}
+.wy-nav-content-wrap .wy-nav-content .document {
+ min-height: 500px;
+}
+@media only screen and (max-width : 767px) {
+ .wy-nav-content-wrap .wy-nav-content .document {
+ min-height: auto;
+ }
+}
+
+@media only screen and (max-width : 991px) {
+ .wy-nav-content-wrap {
+ margin-left: 0;
+ }
+ .wy-nav-content-wrap.shift {
+ position: relative;
+ left: 0;
+ }
+}
+
+.wy-nav-top {
+ padding: 5px 25px 5px 30px;
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-top {
+ position: fixed;
+ top: 84px;
+ left: 0;
+ width: 100%;
+ display: flex;
+ flex-direction: row-reverse;
+ justify-content: space-between;
+ text-align: left;
+ z-index: 999;
+ align-items: center;
+ background: #F8F9FA;
+ }
+ .wy-nav-top .nav-content {
+ flex: 1 1 auto;
+ color: #252d5a;
+ font-family: "PT Sans Caption", sans-serif;
+ font-size: 15px;
+ font-weight: 700;
+ }
+ .wy-nav-top .togglemenu {
+ width: 30px;
+ height: 30px;
+ background: url(../img/rightangle.svg) no-repeat center;
+ transition: 0.1s all;
+ }
+}
+
+.shift .togglemenu {
+ transform: rotate(90deg);
+}
+
+@media only screen and (max-width : 991px) {
+ .wy-nav-side {
+ left: 0;
+ width: 100%;
+ transition: 0.3s all;
+ opacity: 0;
+ z-index: -1;
+ top: 144px;
+ transform: translateY(-60px);
+ }
+ .wy-nav-side.shift {
+ width: 100%;
+ opacity: 1;
+ z-index: 200;
+ width: 100%;
+ transform: translateY(0);
+ }
+}
+@media only screen and (max-width : 991px) {
+ .wy-nav-side .wy-menu.wy-menu-vertical, .wy-nav-side .wy-side-nav-search, .wy-nav-side .wy-side-scroll {
+ width: auto;
+ }
+}
+
+.rst-footer-buttons {
+ margin-bottom: 57px;
+}
+
+.footerSec {
+ background: #001B29;
+ font-family: "Ubuntu", sans-serif;
+ font-size: 14px;
+ line-height: 21px;
+ color: #ffffff;
+ padding-bottom: 43px;
+}
+@media only screen and (max-width : 991px) {
+ .footerSec {
+ padding: 0 30px;
+ }
+}
+.footerSec h5 {
+ font-size: 14px;
+ margin-bottom: 0;
+ font-weight: normal;
+ font-family: "Ubuntu", sans-serif;
+}
+@media only screen and (max-width : 767px) {
+ .footerSec h5 {
+ margin-top: 10px;
+ max-width: 240px;
+ }
+}
+.footerSec .footerHeader {
+ padding: 18px 0 18px 50px;
+ border-bottom: 1px solid #252d5a;
+}
+@media only screen and (max-width : 991px) {
+ .footerSec .footerHeader {
+ padding: 30px 0 20px;
+ }
+}
+.footerSec .footerHeader p {
+ margin-bottom: 0;
+}
+.footerSec .footernote {
+ padding: 30px 50px 0;
+ max-width: 944px;
+ width: 100%;
+}
+@media only screen and (max-width : 991px) {
+ .footerSec .footernote {
+ padding: 20px 0 30px;
+ }
+}
+
+.gallery-container {
+ display: flex;
+ flex-wrap: wrap;
+ margin-bottom: 30px;
+}
+@media only screen and (max-width : 991px) {
+ .gallery-container {
+ flex-wrap: nowrap;
+ overflow-x: auto;
+ }
+}
+
+@media screen and (min-width: 1100px) {
+ .wy-nav-content-wrap {
+ background: #ffffff;
+ }
+
+ .wy-nav-content {
+ background: #ffffff;
+ }
+}
+@media only screen and (max-width : 991px) {
+ .navigation-hide {
+ display: none;
+ }
+
+ .sphx-glr-thumbcontainer {
+ margin: 0;
+ margin-right: 15px;
+ width: 100%;
+ flex: 0 0 255px;
+ }
+ .sphx-glr-thumbcontainer .figure {
+ width: 100%;
+ }
+
+ .backtop {
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ background-color: #0379b6;
+ width: 45px;
+ height: 45px;
+ text-align: center;
+ position: fixed;
+ bottom: 30px;
+ right: 30px;
+ transition: background-color 0.3s, opacity 0.5s, visibility 0.5s;
+ opacity: 0;
+ visibility: hidden;
+ z-index: 100;
+ }
+ .backtop img {
+ transform: rotate(-90deg);
+ }
+ .backtop.show {
+ opacity: 1;
+ visibility: visible;
+ }
+}
+.rst-content .container{
+ padding:0;
+}
diff --git a/docs/_static/img/close-icon.svg b/docs/_static/img/close-icon.svg
new file mode 100644
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--- /dev/null
+++ b/docs/_static/img/close-icon.svg
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+<path fill-rule="evenodd" clip-rule="evenodd" d="M13.295 2.115C13.6844 1.72564 13.6844 1.09436 13.295 0.705C12.9056 0.315639 12.2744 0.315639 11.885 0.705L7 5.59L2.115 0.705C1.72564 0.315639 1.09436 0.315639 0.705 0.705C0.315639 1.09436 0.315639 1.72564 0.705 2.115L5.59 7L0.705 11.885C0.315639 12.2744 0.315639 12.9056 0.705 13.295C1.09436 13.6844 1.72564 13.6844 2.115 13.295L7 8.41L11.885 13.295C12.2744 13.6844 12.9056 13.6844 13.295 13.295C13.6844 12.9056 13.6844 12.2744 13.295 11.885L8 [...]
+</svg>
diff --git a/docs/_static/img/downangle.svg b/docs/_static/img/downangle.svg
new file mode 100644
index 0000000..196c418
--- /dev/null
+++ b/docs/_static/img/downangle.svg
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+<svg width="12" height="12" viewBox="0 0 12 12" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M9.84262 6.15683L5.84277 10.1567L1.84292 6.15683" stroke="#252D5A" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>
diff --git a/docs/_static/img/dropdown-icon.svg b/docs/_static/img/dropdown-icon.svg
new file mode 100644
index 0000000..71a65fe
--- /dev/null
+++ b/docs/_static/img/dropdown-icon.svg
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+<svg width="8" height="5" viewBox="0 0 8 5" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path fill-rule="evenodd" clip-rule="evenodd" d="M1.41421 0C0.523309 0 0.077142 1.07714 0.707107 1.70711L3.29289 4.29289C3.68342 4.68342 4.31658 4.68342 4.70711 4.29289L7.2929 1.70711C7.92286 1.07714 7.47669 0 6.58579 0H1.41421Z" fill="white"/>
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diff --git a/docs/_static/img/dwonloaddoc.svg b/docs/_static/img/dwonloaddoc.svg
new file mode 100644
index 0000000..6a61385
--- /dev/null
+++ b/docs/_static/img/dwonloaddoc.svg
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+<svg width="14" height="12" viewBox="0 0 14 12" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M1 9.26514V11.2651H13V9.26514" stroke="white"/>
+<path d="M7.15686 0L7.15686 7.5" stroke="white"/>
+<path d="M10 4.84338L7.00012 7.84326L4.00024 4.84338" stroke="white"/>
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diff --git a/docs/_static/img/hide.svg b/docs/_static/img/hide.svg
new file mode 100644
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--- /dev/null
+++ b/docs/_static/img/hide.svg
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+<svg width="8" height="8" viewBox="0 0 8 8" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M8 4L-4.17233e-07 4" stroke="#0379B6"/>
+</svg>
diff --git a/docs/_static/img/menu-icon.svg b/docs/_static/img/menu-icon.svg
new file mode 100644
index 0000000..c15a280
--- /dev/null
+++ b/docs/_static/img/menu-icon.svg
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+<svg width="29" height="19" viewBox="0 0 29 19" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M1 18H27.4444" stroke="white" stroke-width="2" stroke-linecap="round"/>
+<path d="M1 9.5H18" stroke="white" stroke-width="2" stroke-linecap="round"/>
+<path d="M1 1H27.4444" stroke="white" stroke-width="2" stroke-linecap="round"/>
+</svg>
diff --git a/docs/_static/img/nextarrow.svg b/docs/_static/img/nextarrow.svg
new file mode 100644
index 0000000..717127d
--- /dev/null
+++ b/docs/_static/img/nextarrow.svg
@@ -0,0 +1,4 @@
+<svg width="12" height="8" viewBox="0 0 12 8" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M0.156982 3.5H10.657" stroke="white"/>
+<path d="M8.00012 0.656854L11 3.65674L8.00012 6.65662" stroke="white"/>
+</svg>
diff --git a/docs/_static/img/note.svg b/docs/_static/img/note.svg
new file mode 100644
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--- /dev/null
+++ b/docs/_static/img/note.svg
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+<svg width="12" height="12" viewBox="0 0 12 12" fill="none" xmlns="http://www.w3.org/2000/svg">
+<circle cx="6" cy="6" r="6" fill="white"/>
+<path d="M6 3V7" stroke="#0379B6"/>
+<path d="M6 8V9" stroke="#0379B6"/>
+</svg>
diff --git a/docs/_static/img/pattern.svg b/docs/_static/img/pattern.svg
new file mode 100644
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