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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/07/15 22:04:34 UTC
[incubator-tvm-site] branch asf-site updated: Docs build at Wed Jul
15 15:02:36 PDT 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 cd377c8 Docs build at Wed Jul 15 15:02:36 PDT 2020
cd377c8 is described below
commit cd377c856e2fb48956ff765360eb5b0a2c242fec
Author: tqchen <tq...@octoml.ai>
AuthorDate: Wed Jul 15 15:02:36 2020 -0700
Docs build at Wed Jul 15 15:02:36 PDT 2020
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660 files changed, 35100 insertions(+), 28578 deletions(-)
diff --git a/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py b/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
index 35a308c..c0b2a03 100644
--- a/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
+++ b/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
@@ -136,7 +136,7 @@ mod, params = relay.frontend.from_tflite(tflite_model,
# Build the module against to x86 CPU
target = "llvm"
with transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
+ lib = relay.build(mod, target, params=params)
######################################################################
# Execute on TVM
@@ -146,14 +146,11 @@ from tvm import te
from tvm.contrib import graph_runtime as runtime
# Create a runtime executor module
-module = runtime.create(graph, lib, tvm.cpu())
+module = runtime.GraphModule(lib['default'](tvm.cpu()))
# Feed input data
module.set_input(input_tensor, tvm.nd.array(image_data))
-# Feed related params
-module.set_input(**params)
-
# Run
module.run()
diff --git a/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb b/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
index ff25080..1860f7d 100644
--- a/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
+++ b/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "# TFLite input tensor name, shape and type\ninput_tensor = \"input\"\ninput_shape = (1, 224, 224, 3)\ninput_dtype = \"float32\"\n\n# Parse TFLite model and convert it to a Relay module\nfrom tvm import relay, transform\nmod, params = relay.frontend.from_tflite(tflite_model,\n shape_dict={input_tensor: input_shape},\n dtype_dict={input_tensor: input_dtype})\n\n# Build the module against to x86 CPU\nta [...]
+ "# TFLite input tensor name, shape and type\ninput_tensor = \"input\"\ninput_shape = (1, 224, 224, 3)\ninput_dtype = \"float32\"\n\n# Parse TFLite model and convert it to a Relay module\nfrom tvm import relay, transform\nmod, params = relay.frontend.from_tflite(tflite_model,\n shape_dict={input_tensor: input_shape},\n dtype_dict={input_tensor: input_dtype})\n\n# Build the module against to x86 CPU\nta [...]
]
},
{
@@ -105,7 +105,7 @@
},
"outputs": [],
"source": [
- "import tvm\nfrom tvm import te\nfrom tvm.contrib import graph_runtime as runtime\n\n# Create a runtime executor module\nmodule = runtime.create(graph, lib, tvm.cpu())\n\n# Feed input data\nmodule.set_input(input_tensor, tvm.nd.array(image_data))\n\n# Feed related params\nmodule.set_input(**params)\n\n# Run\nmodule.run()\n\n# Get output\ntvm_output = module.get_output(0).asnumpy()"
+ "import tvm\nfrom tvm import te\nfrom tvm.contrib import graph_runtime as runtime\n\n# Create a runtime executor module\nmodule = runtime.GraphModule(lib['default'](tvm.cpu()))\n\n# Feed input data\nmodule.set_input(input_tensor, tvm.nd.array(image_data))\n\n# Run\nmodule.run()\n\n# Get output\ntvm_output = module.get_output(0).asnumpy()"
]
},
{
diff --git a/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb b/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
index 3c67a09..bacd7a6 100644
--- a/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
+++ b/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
@@ -155,7 +155,7 @@
},
"outputs": [],
"source": [
- "def run_tvm(graph, lib, params):\n from tvm.contrib import graph_runtime\n rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))\n rt_mod.set_input(**params)\n rt_mod.set_input('input', data)\n rt_mod.run()\n tvm_res = rt_mod.get_output(0).asnumpy()\n tvm_pred = np.squeeze(tvm_res).argsort()[-5:][::-1]\n return tvm_pred, rt_mod"
+ "def run_tvm(lib):\n from tvm.contrib import graph_runtime\n rt_mod = graph_runtime.GraphModule(lib['default'](tvm.cpu(0)))\n rt_mod.set_input('input', data)\n rt_mod.run()\n tvm_res = rt_mod.get_output(0).asnumpy()\n tvm_pred = np.squeeze(tvm_res).argsort()[-5:][::-1]\n return tvm_pred, rt_mod"
]
},
{
@@ -223,7 +223,7 @@
},
"outputs": [],
"source": [
- "target = 'llvm'\nwith tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build_module.build(mod, target=target,\n params=params)"
+ "target = 'llvm'\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build_module.build(mod, target=target, params=params)"
]
},
{
@@ -241,7 +241,7 @@
},
"outputs": [],
"source": [
- "tvm_pred, rt_mod = run_tvm(graph, lib, params)"
+ "tvm_pred, rt_mod = run_tvm(lib)"
]
},
{
diff --git a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
index a6fe45b..6a995af 100644
--- a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
+++ b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
@@ -128,11 +128,7 @@ tuning_option = {
'measure_option': autotvm.measure_option(
builder=autotvm.LocalBuilder(timeout=10),
- #runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
- runner=autotvm.RPCRunner(
- '1080ti', # change the device key to your key
- '0.0.0.0', 9190,
- number=20, repeat=3, timeout=4, min_repeat_ms=150)
+ runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
),
}
diff --git a/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb b/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
index bc8691a..c76d6d5 100644
--- a/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
+++ b/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nHow to Use Relay Pass Infra\n===========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes.\n\nThe optimizations of a Relay program could be applied at various granularity,\nnamely function-level and module-level using :py:class:`tvm.r [...]
+ "\n\nHow to Use Relay Pass Infra\n===========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes.\n\nThe optimizations of a Relay program could be applied at various granularity,\nnamely function-level and module-level using :py:class:`tvm.r [...]
]
},
{
diff --git a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
index eacfead..07fb0ba 100644
--- a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
+++ b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
@@ -83,7 +83,7 @@
},
"outputs": [],
"source": [
- "#### DEVICE CONFIG ####\n\ntarget = tvm.target.create('opencl -device=mali')\n\n# Replace \"aarch64-linux-gnu\" with the correct target of your board.\n# This target host is used for cross compilation. You can query it by :code:`gcc -v` on your device.\ntarget_host = 'llvm -target=aarch64-linux-gnu'\n\n# Also replace this with the device key in your tracker\ndevice_key = 'rk3399'\n\n# Set this to True if you use android phone\nuse_android = False\n\n#### TUNING OPTION ####\nnetw [...]
+ "#### DEVICE CONFIG ####\n\ntarget = tvm.target.create('opencl -device=mali')\n\n# Replace \"aarch64-linux-gnu\" with the correct target of your board.\n# This target host is used for cross compilation. You can query it by :code:`gcc -v` on your device.\ntarget_host = 'llvm -mtriple=aarch64-linux-gnu'\n\n# Also replace this with the device key in your tracker\ndevice_key = 'rk3399'\n\n# Set this to True if you use android phone\nuse_android = False\n\n#### TUNING OPTION ####\nnet [...]
]
},
{
diff --git a/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb b/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb
index abb835c..957c3ac 100644
--- a/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb
+++ b/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "target = 'llvm'\nshape_dict = {'image': x.shape}\n\n# Parse CoreML model and convert into Relay computation graph\nmod, params = relay.frontend.from_coreml(mlmodel, shape_dict)\n\nwith tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(mod,\n target,\n params=params)"
+ "target = 'llvm'\nshape_dict = {'image': x.shape}\n\n# Parse CoreML model and convert into Relay computation graph\nmod, params = relay.frontend.from_coreml(mlmodel, shape_dict)\n\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target, params=params)"
]
},
{
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib import graph_runtime\nctx = tvm.cpu(0)\ndtype = 'float32'\nm = graph_runtime.create(graph, lib, ctx)\n# set inputs\nm.set_input('image', tvm.nd.array(x.astype(dtype)))\nm.set_input(**params)\n# execute\nm.run()\n# get outputs\ntvm_output = m.get_output(0)\ntop1 = np.argmax(tvm_output.asnumpy()[0])"
+ "from tvm.contrib import graph_runtime\nctx = tvm.cpu(0)\ndtype = 'float32'\nm = graph_runtime.GraphModule(lib['default'](ctx))\n# set inputs\nm.set_input('image', tvm.nd.array(x.astype(dtype)))\n# execute\nm.run()\n# get outputs\ntvm_output = m.get_output(0)\ntop1 = np.argmax(tvm_output.asnumpy()[0])"
]
},
{
diff --git a/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb b/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb
index 03cd570..a097a19 100644
--- a/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb
+++ b/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb
@@ -69,7 +69,7 @@
},
"outputs": [],
"source": [
- "# Caffe2 input tensor name, shape and type\ninput_name = resnet50.predict_net.op[0].input[0]\nshape_dict = {input_name: data.shape}\ndtype_dict = {input_name: data.dtype}\n\n# parse Caffe2 model and convert into Relay computation graph\nfrom tvm import relay, transform\nmod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict)\n\n# compile the model\n# target x86 CPU\ntarget = 'llvm'\nwith transform.PassContext(opt_level=3):\n g [...]
+ "# Caffe2 input tensor name, shape and type\ninput_name = resnet50.predict_net.op[0].input[0]\nshape_dict = {input_name: data.shape}\ndtype_dict = {input_name: data.dtype}\n\n# parse Caffe2 model and convert into Relay computation graph\nfrom tvm import relay, transform\nmod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict)\n\n# compile the model\n# target x86 CPU\ntarget = 'llvm'\nwith transform.PassContext(opt_level=3):\n l [...]
]
},
{
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "import tvm\nfrom tvm import te\nfrom tvm.contrib import graph_runtime\n# context x86 CPU, use tvm.gpu(0) if you run on GPU\nctx = tvm.cpu(0)\n# create a runtime executor module\nm = graph_runtime.create(graph, lib, ctx)\n# set inputs\nm.set_input(input_name, tvm.nd.array(data.astype('float32')))\n# set related params\nm.set_input(**params)\n# execute\nm.run()\n# get outputs\ntvm_out = m.get_output(0)\ntop1_tvm = np.argmax(tvm_out.asnumpy()[0])"
+ "import tvm\nfrom tvm import te\nfrom tvm.contrib import graph_runtime\n# context x86 CPU, use tvm.gpu(0) if you run on GPU\nctx = tvm.cpu(0)\n# create a runtime executor module\nm = graph_runtime.GraphModule(lib['default'](ctx))\n# set inputs\nm.set_input(input_name, tvm.nd.array(data.astype('float32')))\n# execute\nm.run()\n# get outputs\ntvm_out = m.get_output(0)\ntop1_tvm = np.argmax(tvm_out.asnumpy()[0])"
]
},
{
diff --git a/docs/_downloads/33a19782c8aaf9fc62e565c57df5caca/deploy_sparse.py b/docs/_downloads/33a19782c8aaf9fc62e565c57df5caca/deploy_sparse.py
index 87d5e45..11cd63c 100644
--- a/docs/_downloads/33a19782c8aaf9fc62e565c57df5caca/deploy_sparse.py
+++ b/docs/_downloads/33a19782c8aaf9fc62e565c57df5caca/deploy_sparse.py
@@ -216,15 +216,14 @@ def import_graphdef(
# tensors instead of sparse aware kernels.
def run_relay_graph(mod, params, shape_dict, target, ctx):
with relay.build_config(opt_level=3):
- graph, lib, params = relay.build(mod, target=target, params=params)
+ lib = relay.build(mod, target=target, params=params)
input_shape = shape_dict["input_1"]
dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype(
"int32"
)
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
m.set_input(0, dummy_data)
- m.set_input(**params)
m.run()
tvm_output = m.get_output(0)
diff --git a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
index 5dfb414..6ee8c87 100644
--- a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
+++ b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
@@ -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 -target=%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':\n target_h [...]
+ "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':\n target_ [...]
]
},
{
@@ -137,7 +137,7 @@
},
"outputs": [],
"source": [
- "tracker_host = os.environ.get('TVM_TRACKER_HOST', '0.0.0.0')\ntracker_port = int(os.environ.get('TVM_TRACKER_PORT', 9190))\nkey = 'android'\n\nif local_demo:\n remote = rpc.LocalSession()\nelse:\n tracker = rpc.connect_tracker(tracker_host, tracker_port)\n # When running a heavy model, we should increase the `session_timeout`\n remote = tracker.request(key, priority=0,\n session_timeout=60)\n\nif local_demo:\n ctx = remote.cpu(0)\nelif [...]
+ "tracker_host = os.environ.get('TVM_TRACKER_HOST', '0.0.0.0')\ntracker_port = int(os.environ.get('TVM_TRACKER_PORT', 9190))\nkey = 'android'\n\nif local_demo:\n remote = rpc.LocalSession()\nelse:\n tracker = rpc.connect_tracker(tracker_host, tracker_port)\n # When running a heavy model, we should increase the `session_timeout`\n remote = tracker.request(key, priority=0,\n session_timeout=60)\n\nif local_demo:\n ctx = remote.cpu(0)\nelif [...]
]
},
{
@@ -155,7 +155,7 @@
},
"outputs": [],
"source": [
- "# set parameter (upload params to the remote device. This may take a while)\nmodule.set_input(**params)\n# set input data\nmodule.set_input(input_name, tvm.nd.array(x.astype(dtype)))\n# run\nmodule.run()\n# get output\nout = module.get_output(0)\n\n# get top1 result\ntop1 = np.argmax(out.asnumpy())\nprint('TVM prediction top-1: {}'.format(synset[top1]))\n\nprint('Evaluate inference time cost...')\nftimer = module.module.time_evaluator('run', ctx, number=1, repeat=10)\nprof_res = [...]
+ "# set input data\nmodule.set_input(input_name, tvm.nd.array(x.astype(dtype)))\n# run\nmodule.run()\n# get output\nout = module.get_output(0)\n\n# get top1 result\ntop1 = np.argmax(out.asnumpy())\nprint('TVM prediction top-1: {}'.format(synset[top1]))\n\nprint('Evaluate inference time cost...')\nftimer = module.module.time_evaluator('run', ctx, number=1, repeat=10)\nprof_res = np.array(ftimer().results) * 1000 # convert to millisecond\nprint('Mean inference time (std dev): %.2f [...]
]
},
{
diff --git a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
index bc5b523..55eb3aa 100644
--- a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
+++ b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
@@ -246,7 +246,7 @@ test_target = 'cpu'
# Change target configuration.
# Run `adb shell cat /proc/cpuinfo` to find the arch.
arch = 'arm64'
-target = 'llvm -target=%s-linux-android' % arch
+target = 'llvm -mtriple=%s-linux-android' % arch
target_host = None
if local_demo:
@@ -264,8 +264,8 @@ shape_dict = {input_name: x.shape}
mod, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target=target,
- target_host=target_host, params=params)
+ lib = relay.build(mod, target=target,
+ target_host=target_host, params=params)
# After `relay.build`, you will get three return values: graph,
# library and the new parameter, since we do some optimization that will
@@ -309,14 +309,12 @@ remote.upload(lib_fname)
rlib = remote.load_module('net.so')
# create the remote runtime module
-module = runtime.create(graph, rlib, ctx)
+module = runtime.GraphModule(rlib['default'](ctx))
######################################################################
# Execute on TVM
# --------------
-# set parameter (upload params to the remote device. This may take a while)
-module.set_input(**params)
# set input data
module.set_input(input_name, tvm.nd.array(x.astype(dtype)))
# run
diff --git a/docs/_downloads/48779ddff800bd9d4b8b3bd7ef8f054c/using_external_lib.py b/docs/_downloads/48779ddff800bd9d4b8b3bd7ef8f054c/using_external_lib.py
index 7063c0e..bc47023 100644
--- a/docs/_downloads/48779ddff800bd9d4b8b3bd7ef8f054c/using_external_lib.py
+++ b/docs/_downloads/48779ddff800bd9d4b8b3bd7ef8f054c/using_external_lib.py
@@ -71,13 +71,11 @@ import logging
logging.basicConfig(level=logging.DEBUG) # to dump TVM IR after fusion
target = "cuda"
-graph, lib, params = relay.build_module.build(
- net, target, params=params)
+lib = relay.build_module.build(net, target, params=params)
ctx = tvm.context(target, 0)
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
-module = runtime.create(graph, lib, ctx)
-module.set_input(**params)
+module = runtime.GraphModule(lib['default'](ctx))
module.set_input("data", data)
module.run()
out_shape = (batch_size, out_channels, 224, 224)
@@ -494,13 +492,11 @@ out_cuda = out.asnumpy()
# To do that, all we need to do is to append the option " -libs=cudnn" to the target string.
net, params = testing.create_workload(simple_net)
target = "cuda -libs=cudnn" # use cudnn for convolution
-graph, lib, params = relay.build_module.build(
- net, target, params=params)
+lib = relay.build_module.build(net, target, params=params)
ctx = tvm.context(target, 0)
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
-module = runtime.create(graph, lib, ctx)
-module.set_input(**params)
+module = runtime.GraphModule(lib['default'](ctx))
module.set_input("data", data)
module.run()
out_shape = (batch_size, out_channels, 224, 224)
diff --git a/docs/_downloads/4cb9cb94f36033c7820ba70d890df4a1/cross_compilation_and_rpc.py b/docs/_downloads/4cb9cb94f36033c7820ba70d890df4a1/cross_compilation_and_rpc.py
index 553d77d..eaf6f03 100644
--- a/docs/_downloads/4cb9cb94f36033c7820ba70d890df4a1/cross_compilation_and_rpc.py
+++ b/docs/_downloads/4cb9cb94f36033c7820ba70d890df4a1/cross_compilation_and_rpc.py
@@ -107,7 +107,7 @@ s = te.create_schedule(B.op)
######################################################################
# Then we cross compile the kernel.
-# The target should be 'llvm -target=armv7l-linux-gnueabihf' for
+# The target should be 'llvm -mtriple=armv7l-linux-gnueabihf' for
# Raspberry Pi 3B, but we use 'llvm' here to make this tutorial runnable
# on our webpage building server. See the detailed note in the following block.
@@ -116,7 +116,7 @@ local_demo = True
if local_demo:
target = 'llvm'
else:
- target = 'llvm -target=armv7l-linux-gnueabihf'
+ target = 'llvm -mtriple=armv7l-linux-gnueabihf'
func = tvm.build(s, [A, B], target=target, name='add_one')
# save the lib at a local temp folder
@@ -131,14 +131,14 @@ func.export_library(path)
# to False and replace :code:`target` in :code:`build` with the appropriate
# target triple for your device. The target triple which might be
# different for different devices. For example, it is
-# :code:`'llvm -target=armv7l-linux-gnueabihf'` for Raspberry Pi 3B and
-# :code:`'llvm -target=aarch64-linux-gnu'` for RK3399.
+# :code:`'llvm -mtriple=armv7l-linux-gnueabihf'` for Raspberry Pi 3B and
+# :code:`'llvm -mtriple=aarch64-linux-gnu'` for RK3399.
#
# Usually, you can query the target by running :code:`gcc -v` on your
# device, and looking for the line starting with :code:`Target:`
# (Though it may still be a loose configuration.)
#
-# Besides :code:`-target`, you can also set other compilation options
+# Besides :code:`-mtriple`, you can also set other compilation options
# like:
#
# * -mcpu=<cpuname>
@@ -224,7 +224,7 @@ print('%g secs/op' % cost)
def run_opencl():
# NOTE: This is the setting for my rk3399 board. You need to modify
# them according to your environment.
- target_host = "llvm -target=aarch64-linux-gnu"
+ target_host = "llvm -mtriple=aarch64-linux-gnu"
opencl_device_host = '10.77.1.145'
opencl_device_port = 9090
diff --git a/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py b/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py
index b7b3d69..10d505c 100644
--- a/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py
+++ b/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py
@@ -145,10 +145,7 @@ print("Tensorflow protobuf imported to relay frontend.")
# lib: target library which can be deployed on target with TVM runtime.
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target=target,
- target_host=target_host,
- params=params)
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
######################################################################
# Execute the portable graph on TVM
@@ -157,10 +154,9 @@ with tvm.transform.PassContext(opt_level=3):
from tvm.contrib import graph_runtime
dtype = 'uint8'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))
-m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py b/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
index df40733..ae7f544 100644
--- a/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
+++ b/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
@@ -28,7 +28,7 @@ introduced an infrastructure to manage the optimization passes.
The optimizations of a Relay program could be applied at various granularity,
namely function-level and module-level using :py:class:`tvm.relay.transform.FunctionPass`
-and py:class:`tvm.relay.transform.ModulePass`
+and :py:class:`tvm.relay.transform.ModulePass`
respectively. Or users can rely on py:class:`tvm.transform.Sequential` to apply a sequence of passes
on a Relay program where the dependencies between passes can be resolved by the
pass infra. For more details about each type of these passes, please refer to
diff --git a/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py b/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py
index ecd283a..0e5f9af 100644
--- a/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py
+++ b/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py
@@ -157,10 +157,9 @@ def run_tflite_model(tflite_model_buf, input_data):
###############################################################################
# Lets run TVM compiled pre-quantized model inference and get the TVM prediction.
-def run_tvm(graph, lib, params):
+def run_tvm(lib):
from tvm.contrib import graph_runtime
- rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
- rt_mod.set_input(**params)
+ rt_mod = graph_runtime.GraphModule(lib['default'](tvm.cpu(0)))
rt_mod.set_input('input', data)
rt_mod.run()
tvm_res = rt_mod.get_output(0).asnumpy()
@@ -199,12 +198,11 @@ mod, params = relay.frontend.from_tflite(tflite_model,
# target platform that you are interested in.
target = 'llvm'
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build_module.build(mod, target=target,
- params=params)
+ lib = relay.build_module.build(mod, target=target, params=params)
###############################################################################
# Finally, lets call inference on the TVM compiled module.
-tvm_pred, rt_mod = run_tvm(graph, lib, params)
+tvm_pred, rt_mod = run_tvm(lib)
###############################################################################
# Accuracy comparison
diff --git a/docs/_downloads/6be1519353297beeea03fe17712dc16f/using_external_lib.ipynb b/docs/_downloads/6be1519353297beeea03fe17712dc16f/using_external_lib.ipynb
index 9527972..fe5e4f9 100644
--- a/docs/_downloads/6be1519353297beeea03fe17712dc16f/using_external_lib.ipynb
+++ b/docs/_downloads/6be1519353297beeea03fe17712dc16f/using_external_lib.ipynb
@@ -62,7 +62,7 @@
},
"outputs": [],
"source": [
- "import logging\nlogging.basicConfig(level=logging.DEBUG) # to dump TVM IR after fusion\n\ntarget = \"cuda\"\ngraph, lib, params = relay.build_module.build(\n net, target, params=params)\n\nctx = tvm.context(target, 0)\ndata = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\nmodule = runtime.create(graph, lib, ctx)\nmodule.set_input(**params)\nmodule.set_input(\"data\", data)\nmodule.run()\nout_shape = (batch_size, out_channels, 224, 224)\nout = module.get_output [...]
+ "import logging\nlogging.basicConfig(level=logging.DEBUG) # to dump TVM IR after fusion\n\ntarget = \"cuda\"\nlib = relay.build_module.build(net, target, params=params)\n\nctx = tvm.context(target, 0)\ndata = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\nmodule = runtime.GraphModule(lib['default'](ctx))\nmodule.set_input(\"data\", data)\nmodule.run()\nout_shape = (batch_size, out_channels, 224, 224)\nout = module.get_output(0, tvm.nd.empty(out_shape))\nout_cuda = [...]
]
},
{
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "net, params = testing.create_workload(simple_net)\ntarget = \"cuda -libs=cudnn\" # use cudnn for convolution\ngraph, lib, params = relay.build_module.build(\n net, target, params=params)\n\nctx = tvm.context(target, 0)\ndata = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\nmodule = runtime.create(graph, lib, ctx)\nmodule.set_input(**params)\nmodule.set_input(\"data\", data)\nmodule.run()\nout_shape = (batch_size, out_channels, 224, 224)\nout = module.get_o [...]
+ "net, params = testing.create_workload(simple_net)\ntarget = \"cuda -libs=cudnn\" # use cudnn for convolution\nlib = relay.build_module.build(net, target, params=params)\n\nctx = tvm.context(target, 0)\ndata = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\nmodule = runtime.GraphModule(lib['default'](ctx))\nmodule.set_input(\"data\", data)\nmodule.run()\nout_shape = (batch_size, out_channels, 224, 224)\nout = module.get_output(0, tvm.nd.empty(out_shape))\nout_cudnn [...]
]
},
{
diff --git a/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb b/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
index 30ff482..f333463 100644
--- a/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
+++ b/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\nwith tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(mod,\n target=target,\n target_host=target_host,\n params=params)"
+ "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target=target, target_host=target_host, params=params)"
]
},
{
@@ -116,7 +116,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib import graph_runtime\ndtype = 'float32'\nm = graph_runtime.create(graph, lib, ctx)\n# Set inputs\nm.set_input(input_name, tvm.nd.array(img.astype(dtype)))\nm.set_input(**params)\n# Execute\nm.run()\n# Get outputs\ntvm_output = m.get_output(0)"
+ "from tvm.contrib import graph_runtime\ndtype = 'float32'\nm = graph_runtime.GraphModule(lib['default'](ctx))\n# Set inputs\nm.set_input(input_name, tvm.nd.array(img.astype(dtype)))\n# Execute\nm.run()\n# Get outputs\ntvm_output = m.get_output(0)"
]
},
{
diff --git a/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py b/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py
index 19719a5..3f4efeb 100644
--- a/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py
+++ b/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py
@@ -337,12 +337,11 @@ mod = tvm.IRModule()
mod["main"] = func
# Build with Relay
with tvm.transform.PassContext(opt_level=0): # Currently only support opt_level=0
- graph, lib, params = relay.build(mod, target, params=params)
+ lib = relay.build(mod, target, params=params)
# Generate graph runtime
ctx = tvm.context(target, 0)
-m = graph_runtime.create(graph, lib, ctx)
-m.set_input(**params)
+m = graph_runtime.GraphModule(lib['default'](ctx))
######################################################################
# Run the TVM model, test for accuracy and verify with DGL
diff --git a/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py b/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py
index 5988525..66ea0bb 100644
--- a/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py
+++ b/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py
@@ -89,7 +89,7 @@ mod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net
# target x86 CPU
target = 'llvm'
with transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
+ lib = relay.build(mod, target, params=params)
######################################################################
# Execute on TVM
@@ -101,11 +101,9 @@ from tvm.contrib import graph_runtime
# context x86 CPU, use tvm.gpu(0) if you run on GPU
ctx = tvm.cpu(0)
# create a runtime executor module
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input(input_name, tvm.nd.array(data.astype('float32')))
-# set related params
-m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
index f9d16a8..2ddcec4 100644
--- a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
+++ b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
@@ -69,7 +69,7 @@
},
"outputs": [],
"source": [
- "#### DEVICE CONFIG ####\ntarget = tvm.target.cuda()\n\n#### TUNING OPTION ####\nnetwork = 'resnet-18'\nlog_file = \"%s.log\" % network\ndtype = 'float32'\n\ntuning_option = {\n 'log_filename': log_file,\n\n 'tuner': 'xgb',\n 'n_trial': 2000,\n 'early_stopping': 600,\n\n 'measure_option': autotvm.measure_option(\n builder=autotvm.LocalBuilder(timeout=10),\n #runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),\n runner [...]
+ "#### DEVICE CONFIG ####\ntarget = tvm.target.cuda()\n\n#### TUNING OPTION ####\nnetwork = 'resnet-18'\nlog_file = \"%s.log\" % network\ndtype = 'float32'\n\ntuning_option = {\n 'log_filename': log_file,\n\n 'tuner': 'xgb',\n 'n_trial': 2000,\n 'early_stopping': 600,\n\n 'measure_option': autotvm.measure_option(\n builder=autotvm.LocalBuilder(timeout=10),\n runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),\n ),\n}"
]
},
{
diff --git a/docs/_downloads/7630fb4eead0706f214a455190118d17/cross_compilation_and_rpc.ipynb b/docs/_downloads/7630fb4eead0706f214a455190118d17/cross_compilation_and_rpc.ipynb
index 9abb6b9..4b9c4df 100644
--- a/docs/_downloads/7630fb4eead0706f214a455190118d17/cross_compilation_and_rpc.ipynb
+++ b/docs/_downloads/7630fb4eead0706f214a455190118d17/cross_compilation_and_rpc.ipynb
@@ -54,7 +54,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Then we cross compile the kernel.\nThe target should be 'llvm -target=armv7l-linux-gnueabihf' for\nRaspberry Pi 3B, but we use 'llvm' here to make this tutorial runnable\non our webpage building server. See the detailed note in the following block.\n\n"
+ "Then we cross compile the kernel.\nThe target should be 'llvm -mtriple=armv7l-linux-gnueabihf' for\nRaspberry Pi 3B, but we use 'llvm' here to make this tutorial runnable\non our webpage building server. See the detailed note in the following block.\n\n"
]
},
{
@@ -65,14 +65,14 @@
},
"outputs": [],
"source": [
- "local_demo = True\n\nif local_demo:\n target = 'llvm'\nelse:\n target = 'llvm -target=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 = util.tempdir()\npath = temp.relpath('lib.tar')\nfunc.export_library(path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>To run this tutorial with a real remote device, change :code:`local_demo`\n to False and replace :code:`target` in :code:`build` with the appropriate\n target triple for your device. The target triple which might be\n different for different devices. For example, it is\n :code:`'llvm -target=armv7l-linux-gnueabihf'` for Raspberry Pi 3B and\n :code:`'llvm -target=aarch64-linux-gnu'` for RK3399.\n\n Usually, you can query the t [...]
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>To run this tutorial with a real remote device, change :code:`local_demo`\n to False and replace :code:`target` in :code:`build` with the appropriate\n target triple for your device. The target triple which might be\n different for different devices. For example, it is\n :code:`'llvm -mtriple=armv7l-linux-gnueabihf'` for Raspberry Pi 3B and\n :code:`'llvm -mtriple=aarch64-linux-gnu'` for RK3399.\n\n Usually, you can query the [...]
]
},
{
@@ -144,7 +144,7 @@
},
"outputs": [],
"source": [
- "def run_opencl():\n # NOTE: This is the setting for my rk3399 board. You need to modify\n # them according to your environment.\n target_host = \"llvm -target=aarch64-linux-gnu\"\n opencl_device_host = '10.77.1.145'\n opencl_device_port = 9090\n\n # create schedule for the above \"add one\" compute declaration\n s = te.create_schedule(B.op)\n xo, xi = s[B].split(B.op.axis[0], factor=32)\n s[B].bind(xo, te.thread_axis(\"blockIdx.x\"))\n s[B].bind(xi, [...]
+ "def run_opencl():\n # NOTE: This is the setting for my rk3399 board. You need to modify\n # them according to your environment.\n target_host = \"llvm -mtriple=aarch64-linux-gnu\"\n opencl_device_host = '10.77.1.145'\n opencl_device_port = 9090\n\n # create schedule for the above \"add one\" compute declaration\n s = te.create_schedule(B.op)\n xo, xi = s[B].split(B.op.axis[0], factor=32)\n s[B].bind(xo, te.thread_axis(\"blockIdx.x\"))\n s[B].bind(xi [...]
]
},
{
diff --git a/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb b/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
index 4608363..0244980 100644
--- a/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
+++ b/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
@@ -51,7 +51,7 @@
},
"outputs": [],
"source": [
- "def get_transform():\n import torchvision.transforms as transforms\n normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n return transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n normalize,\n ])\n\n\ndef get_real_image(im_height, im_width):\n img_url = 'https://github.com/dmlc/mxnet.js/blob [...]
+ "def get_transform():\n import torchvision.transforms as transforms\n normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n return transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n normalize,\n ])\n\n\ndef get_real_image(im_height, im_width):\n img_url = 'https://github.com/dmlc/mxnet.js/blob [...]
]
},
{
diff --git a/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb b/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
index 11356f1..8c1219f 100644
--- a/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
+++ b/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "block = model_zoo.get_model(model_name, pretrained=True)\n\ndef build(target):\n mod, params = relay.frontend.from_mxnet(block, {\"data\": dshape})\n with tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(mod, target, params=params)\n return graph, lib, params"
+ "block = model_zoo.get_model(model_name, pretrained=True)\n\ndef build(target):\n mod, params = relay.frontend.from_mxnet(block, {\"data\": dshape})\n with tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target, params=params)\n return lib"
]
},
{
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "def run(graph, lib, params, ctx):\n # Build TVM runtime\n m = graph_runtime.create(graph, lib, ctx)\n tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)\n m.set_input('data', tvm_input)\n m.set_input(**params)\n # execute\n m.run()\n # get outputs\n class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)\n return class_IDs, scores, bounding_boxs\n\nfor target, ctx in target_list:\n graph, lib, params = build(target)\n [...]
+ "def run(lib, ctx):\n # Build TVM runtime\n m = graph_runtime.GraphModule(lib['default'](ctx))\n tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)\n m.set_input('data', tvm_input)\n # execute\n m.run()\n # get outputs\n class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)\n return class_IDs, scores, bounding_boxs\n\nfor target, ctx in target_list:\n lib = build(target)\n class_IDs, scores, bounding_boxs = run(lib, ctx)"
]
},
{
diff --git a/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb b/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb
index cc1892e..9353b28 100644
--- a/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb
+++ b/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\ndata = np.empty([batch_size, net.c, net.h, net.w], dtype)\nshape = {'data': data.shape}\nprint(\"Compiling the model...\")\nwith tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(mod,\n target=target,\n target_host=target_host,\n params=params)\n\n[neth, netw] = shape['data'][2:] # Current [...]
+ "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\ndata = np.empty([batch_size, net.c, net.h, net.w], dtype)\nshape = {'data': data.shape}\nprint(\"Compiling the model...\")\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target=target, target_host=target_host, params=params)\n\n[neth, netw] = shape['data'][2:] # Current image shape is 608x608"
]
},
{
@@ -116,7 +116,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib import graph_runtime\n\nm = graph_runtime.create(graph, lib, ctx)\n\n# set inputs\nm.set_input('data', tvm.nd.array(data.astype(dtype)))\nm.set_input(**params)\n# execute\nprint(\"Running the test image...\")\n\n# detection\n# thresholds\nthresh = 0.5\nnms_thresh = 0.45\n\nm.run()\n# get outputs\ntvm_out = []\nif MODEL_NAME == 'yolov2':\n layer_out = {}\n layer_out['type'] = 'Region'\n # Get the region layer attributes (n, out_c, out_h, out_w, classes, [...]
+ "from tvm.contrib import graph_runtime\n\nm = graph_runtime.GraphModule(lib['default'](ctx))\n\n# set inputs\nm.set_input('data', tvm.nd.array(data.astype(dtype)))\n# execute\nprint(\"Running the test image...\")\n\n# detection\n# thresholds\nthresh = 0.5\nnms_thresh = 0.45\n\nm.run()\n# get outputs\ntvm_out = []\nif MODEL_NAME == 'yolov2':\n layer_out = {}\n layer_out['type'] = 'Region'\n # Get the region layer attributes (n, out_c, out_h, out_w, classes, coords, backgr [...]
]
}
],
diff --git a/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py b/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
index 25df341..c9174ad 100644
--- a/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
+++ b/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
@@ -177,10 +177,10 @@ if local_demo:
else:
target = tvm.target.arm_cpu('rasp3b')
# The above line is a simple form of
- # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')
+ # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -mtriple=armv7l-linux-gnueabihf -mattr=+neon')
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(func, target, params=params)
+ lib = relay.build(func, target, params=params)
# After `relay.build`, you will get three return values: graph,
# library and the new parameter, since we do some optimization that will
@@ -212,9 +212,7 @@ rlib = remote.load_module('net.tar')
# create the remote runtime module
ctx = remote.cpu(0)
-module = runtime.create(graph, rlib, ctx)
-# set parameter (upload params to the remote device. This may take a while)
-module.set_input(**params)
+module = runtime.GraphModule(rlib['default'](ctx))
# set input data
module.set_input('data', tvm.nd.array(x.astype('float32')))
# run
diff --git a/docs/_downloads/87b9e8307245d848689e4cdc3e6fa9bf/deploy_sparse.ipynb b/docs/_downloads/87b9e8307245d848689e4cdc3e6fa9bf/deploy_sparse.ipynb
index d6157fe..a6d012c 100644
--- a/docs/_downloads/87b9e8307245d848689e4cdc3e6fa9bf/deploy_sparse.ipynb
+++ b/docs/_downloads/87b9e8307245d848689e4cdc3e6fa9bf/deploy_sparse.ipynb
@@ -105,7 +105,7 @@
},
"outputs": [],
"source": [
- "def run_relay_graph(mod, params, shape_dict, target, ctx):\n with relay.build_config(opt_level=3):\n graph, lib, params = relay.build(mod, target=target, params=params)\n input_shape = shape_dict[\"input_1\"]\n dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype(\n \"int32\"\n )\n\n m = graph_runtime.create(graph, lib, ctx)\n m.set_input(0, dummy_data)\n m.set_input(**params)\n m.run()\n tvm_output = m.get_ [...]
+ "def run_relay_graph(mod, params, shape_dict, target, ctx):\n with relay.build_config(opt_level=3):\n lib = relay.build(mod, target=target, params=params)\n input_shape = shape_dict[\"input_1\"]\n dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype(\n \"int32\"\n )\n\n m = graph_runtime.GraphModule(lib['default'](ctx))\n m.set_input(0, dummy_data)\n m.run()\n tvm_output = m.get_output(0)\n\n ftimer = m.modul [...]
]
},
{
diff --git a/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py b/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
index d6183d6..ca741b3 100644
--- a/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
+++ b/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
@@ -82,10 +82,9 @@ def get_synset():
def run_tvm_model(mod, params, input_name, inp, target="llvm"):
with tvm.transform.PassContext(opt_level=3):
- json, lib, params = relay.build(mod, target=target, params=params)
+ lib = relay.build(mod, target=target, params=params)
- runtime = tvm.contrib.graph_runtime.create(json, lib, tvm.context(target, 0))
- runtime.set_input(**params)
+ runtime = tvm.contrib.graph_runtime.GraphModule(lib['default'](tvm.context(target, 0)))
runtime.set_input(input_name, inp)
runtime.run()
diff --git a/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py b/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py
index 6d84463..c49fc8b 100644
--- a/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py
+++ b/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py
@@ -101,10 +101,7 @@ data = np.empty([batch_size, net.c, net.h, net.w], dtype)
shape = {'data': data.shape}
print("Compiling the model...")
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target=target,
- target_host=target_host,
- params=params)
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
[neth, netw] = shape['data'][2:] # Current image shape is 608x608
######################################################################
@@ -122,11 +119,10 @@ data = tvm.relay.testing.darknet.load_image(img_path, netw, neth)
# The process is no different from other examples.
from tvm.contrib import graph_runtime
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('data', tvm.nd.array(data.astype(dtype)))
-m.set_input(**params)
# execute
print("Running the test image...")
diff --git a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
index 3b07097..d7529b2 100644
--- a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
+++ b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
@@ -190,7 +190,7 @@ def get_network(name, batch_size):
# Replace "aarch64-linux-gnu" with the correct target of your board.
# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
-target = tvm.target.create('llvm -device=arm_cpu -target=aarch64-linux-gnu')
+target = tvm.target.create('llvm -device=arm_cpu -mtriple=aarch64-linux-gnu')
# Also replace this with the device key in your tracker
device_key = 'rk3399'
diff --git a/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb b/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
index f22dcd8..d152c98 100644
--- a/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
+++ b/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
@@ -155,7 +155,7 @@
},
"outputs": [],
"source": [
- "local_demo = True\n\nif local_demo:\n target = tvm.target.create('llvm')\nelse:\n target = tvm.target.arm_cpu('rasp3b')\n # The above line is a simple form of\n # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')\n\nwith tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(func, target, params=params)\n\n# After `relay.build`, you will get three return values: graph,\n# library and t [...]
+ "local_demo = True\n\nif local_demo:\n target = tvm.target.create('llvm')\nelse:\n target = tvm.target.arm_cpu('rasp3b')\n # The above line is a simple form of\n # target = tvm.target.create('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 paramet [...]
]
},
{
@@ -173,7 +173,7 @@
},
"outputs": [],
"source": [
- "# obtain an RPC session from remote device.\nif local_demo:\n remote = rpc.LocalSession()\nelse:\n # The following is my environment, change this to the IP address of your target device\n host = '10.77.1.162'\n port = 9090\n remote = rpc.connect(host, port)\n\n# upload the library to remote device and load it\nremote.upload(lib_fname)\nrlib = remote.load_module('net.tar')\n\n# create the remote runtime module\nctx = remote.cpu(0)\nmodule = runtime.create(graph, rl [...]
+ "# obtain an RPC session from remote device.\nif local_demo:\n remote = rpc.LocalSession()\nelse:\n # The following is my environment, change this to the IP address of your target device\n host = '10.77.1.162'\n port = 9090\n remote = rpc.connect(host, port)\n\n# upload the library to remote device and load it\nremote.upload(lib_fname)\nrlib = remote.load_module('net.tar')\n\n# create the remote runtime module\nctx = remote.cpu(0)\nmodule = runtime.GraphModule(rlib [...]
]
}
],
diff --git a/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb b/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb
index ed1884c..efd45fa 100644
--- a/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb
+++ b/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb
@@ -177,7 +177,7 @@
},
"outputs": [],
"source": [
- "model_params = {}\nfor param_tensor in torch_model.state_dict():\n model_params[param_tensor] = torch_model.state_dict()[param_tensor].numpy()\n\nfor i in range(num_layers+1):\n params[\"layers.%d.weight\"%(i)] = model_params[\"layers.%d.weight\"%(i)]\n params[\"layers.%d.bias\"%(i)] = model_params[\"layers.%d.bias\"%(i)]\n\n# Set the TVM build target\ntarget = 'llvm' # Currently only support `llvm` as target\n\nfunc = relay.Function(relay.analysis.free_vars(output), ou [...]
+ "model_params = {}\nfor param_tensor in torch_model.state_dict():\n model_params[param_tensor] = torch_model.state_dict()[param_tensor].numpy()\n\nfor i in range(num_layers+1):\n params[\"layers.%d.weight\"%(i)] = model_params[\"layers.%d.weight\"%(i)]\n params[\"layers.%d.bias\"%(i)] = model_params[\"layers.%d.bias\"%(i)]\n\n# Set the TVM build target\ntarget = 'llvm' # Currently only support `llvm` as target\n\nfunc = relay.Function(relay.analysis.free_vars(output), ou [...]
]
},
{
diff --git a/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py b/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
index e2fc3c5..46162e1 100644
--- a/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
+++ b/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
@@ -88,18 +88,17 @@ block = model_zoo.get_model(model_name, pretrained=True)
def build(target):
mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
- return graph, lib, params
+ lib = relay.build(mod, target, params=params)
+ return lib
######################################################################
# Create TVM runtime and do inference
-def run(graph, lib, params, ctx):
+def run(lib, ctx):
# Build TVM runtime
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)
m.set_input('data', tvm_input)
- m.set_input(**params)
# execute
m.run()
# get outputs
@@ -107,8 +106,8 @@ def run(graph, lib, params, ctx):
return class_IDs, scores, bounding_boxs
for target, ctx in target_list:
- graph, lib, params = build(target)
- class_IDs, scores, bounding_boxs = run(graph, lib, params, ctx)
+ lib = build(target)
+ class_IDs, scores, bounding_boxs = run(lib, ctx)
######################################################################
# Display result
diff --git a/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb b/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb
index 23ee2ad..6e90654 100644
--- a/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb
+++ b/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb
@@ -134,7 +134,7 @@
},
"outputs": [],
"source": [
- "with tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(mod,\n target=target,\n target_host=target_host,\n params=params)"
+ "with tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target=target, target_host=target_host, params=params)"
]
},
{
@@ -152,7 +152,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib import graph_runtime\ndtype = 'uint8'\nm = graph_runtime.create(graph, lib, ctx)\n# set inputs\nm.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))\nm.set_input(**params)\n# execute\nm.run()\n# get outputs\ntvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))"
+ "from tvm.contrib import graph_runtime\ndtype = 'uint8'\nm = graph_runtime.GraphModule(lib['default'](ctx))\n# set inputs\nm.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))\n# execute\nm.run()\n# get outputs\ntvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))"
]
},
{
diff --git a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
index 4748f41..5f5e523 100644
--- a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
+++ b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
@@ -191,7 +191,7 @@ target = tvm.target.create('opencl -device=mali')
# Replace "aarch64-linux-gnu" with the correct target of your board.
# This target host is used for cross compilation. You can query it by :code:`gcc -v` on your device.
-target_host = 'llvm -target=aarch64-linux-gnu'
+target_host = 'llvm -mtriple=aarch64-linux-gnu'
# Also replace this with the device key in your tracker
device_key = 'rk3399'
diff --git a/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py b/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py
index beac483..f5db0f5 100644
--- a/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py
+++ b/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py
@@ -75,9 +75,7 @@ shape_dict = {'image': x.shape}
mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target,
- params=params)
+ lib = relay.build(mod, target, params=params)
######################################################################
# Execute on TVM
@@ -86,10 +84,9 @@ with tvm.transform.PassContext(opt_level=3):
from tvm.contrib import graph_runtime
ctx = tvm.cpu(0)
dtype = 'float32'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('image', tvm.nd.array(x.astype(dtype)))
-m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py b/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py
index 6e6b2d7..d75ec00 100644
--- a/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py
+++ b/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py
@@ -91,7 +91,7 @@ func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_
# now compile the graph
target = 'cuda'
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(func, target, params=params)
+ lib = relay.build(func, target, params=params)
######################################################################
# Execute the portable graph on TVM
@@ -100,10 +100,9 @@ with tvm.transform.PassContext(opt_level=3):
from tvm.contrib import graph_runtime
ctx = tvm.gpu(0)
dtype = 'float32'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('data', tvm.nd.array(x.astype(dtype)))
-m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb b/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb
index 7e5d275..732dc40 100644
--- a/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb
+++ b/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "target = 'cuda'\nwith tvm.transform.PassContext(opt_level=3):\n graph, lib, params = relay.build(func, target, params=params)"
+ "target = 'cuda'\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(func, target, params=params)"
]
},
{
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib import graph_runtime\nctx = tvm.gpu(0)\ndtype = 'float32'\nm = graph_runtime.create(graph, lib, ctx)\n# set inputs\nm.set_input('data', tvm.nd.array(x.astype(dtype)))\nm.set_input(**params)\n# execute\nm.run()\n# get outputs\ntvm_output = m.get_output(0)\ntop1 = np.argmax(tvm_output.asnumpy()[0])\nprint('TVM prediction top-1:', top1, synset[top1])"
+ "from tvm.contrib import graph_runtime\nctx = tvm.gpu(0)\ndtype = 'float32'\nm = graph_runtime.GraphModule(lib['default'](ctx))\n# set inputs\nm.set_input('data', tvm.nd.array(x.astype(dtype)))\n# execute\nm.run()\n# get outputs\ntvm_output = m.get_output(0)\ntop1 = np.argmax(tvm_output.asnumpy()[0])\nprint('TVM prediction top-1:', top1, synset[top1])"
]
},
{
diff --git a/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py b/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
index 53d29a9..b0639f5 100644
--- a/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
+++ b/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
@@ -102,10 +102,7 @@ target = 'llvm'
target_host = 'llvm'
ctx = tvm.cpu(0)
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target=target,
- target_host=target_host,
- params=params)
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
######################################################################
# Execute the portable graph on TVM
@@ -113,10 +110,9 @@ with tvm.transform.PassContext(opt_level=3):
# Now we can try deploying the compiled model on target.
from tvm.contrib import graph_runtime
dtype = 'float32'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
# Set inputs
m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
-m.set_input(**params)
# Execute
m.run()
# Get outputs
diff --git a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
index 2fa3551..e93e90d 100644
--- a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
+++ b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
@@ -83,7 +83,7 @@
},
"outputs": [],
"source": [
- "#### DEVICE CONFIG ####\n\n# Replace \"aarch64-linux-gnu\" with the correct target of your board.\n# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.\ntarget = tvm.target.create('llvm -device=arm_cpu -target=aarch64-linux-gnu')\n\n# Also replace this with the device key in your tracker\ndevice_key = 'rk3399'\n\n# Set this to True if you use android phone\nuse_android = False\n\n#### TUNING OPTION ####\nnetwork = 'resnet-18'\nlog_file [...]
+ "#### DEVICE CONFIG ####\n\n# Replace \"aarch64-linux-gnu\" with the correct target of your board.\n# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.\ntarget = tvm.target.create('llvm -device=arm_cpu -mtriple=aarch64-linux-gnu')\n\n# Also replace this with the device key in your tracker\ndevice_key = 'rk3399'\n\n# Set this to True if you use android phone\nuse_android = False\n\n#### TUNING OPTION ####\nnetwork = 'resnet-18'\nlog_file [...]
]
},
{
diff --git a/docs/_sources/dev/codebase_walkthrough.rst.txt b/docs/_sources/dev/codebase_walkthrough.rst.txt
index 8674c8e..7a00339 100644
--- a/docs/_sources/dev/codebase_walkthrough.rst.txt
+++ b/docs/_sources/dev/codebase_walkthrough.rst.txt
@@ -73,11 +73,11 @@ You can also checkout `FFI Navigator <https://github.com/tqchen/ffi-navigator>`_
A ``Tensor`` object has an ``Operation`` object associated with it, defined in ``python/tvm/te/tensor.py``, ``include/tvm/te/operation.h``, and ``src/tvm/te/operation`` subdirectory. A ``Tensor`` is an output of its ``Operation`` object. Each ``Operation`` object has in turn ``input_tensors()`` method, which returns a list of input ``Tensor`` to it. This way we can keep track of dependencies between ``Operation``.
-We pass the operation corresponding to the output tensor ``C`` to ``tvm.create_schedule()`` function in ``python/tvm/te/schedule.py``.
+We pass the operation corresponding to the output tensor ``C`` to ``tvm.te.create_schedule()`` function in ``python/tvm/te/schedule.py``.
::
- s = tvm.create_schedule(C.op)
+ s = tvm.te.create_schedule(C.op)
This function is mapped to the C++ function in ``include/tvm/schedule.h``.
diff --git a/docs/_sources/dev/relay_add_pass.rst.txt b/docs/_sources/dev/relay_add_pass.rst.txt
index a82ae4f..fc26559 100644
--- a/docs/_sources/dev/relay_add_pass.rst.txt
+++ b/docs/_sources/dev/relay_add_pass.rst.txt
@@ -181,7 +181,7 @@ Example: Constant Folding
-------------------------
In order to better understand the process of writing a pass, we will look at
-the constant folding pass (found in `src/relay/pass/fold_constant.cc`_)
+the constant folding pass (found in `src/relay/transforms/fold_constant.cc`_)
as a guide, because it is a relatively simple pass that incorporates
both types of traversals.
@@ -329,7 +329,7 @@ Now, we construct a more convenient interface ``FoldConstant`` for our constant
folder. ``FoldConstant`` is a standalone function outside of the ``ConstantFolder``
class that takes an expression and internally creates and uses a
``ConstantFolder`` instance (the full definition can be found in
-`src/relay/pass/fold_constant.cc`_).
+`src/relay/transforms/fold_constant.cc`_).
Registering a Pass with the Pass Manager
@@ -403,4 +403,4 @@ in `src/relay/pass/`_.
.. _src/relay/pass/: https://github.com/apache/incubator-tvm/tree/master/src/relay/pass
-.. _src/relay/pass/fold_constant.cc: https://github.com/apache/incubator-tvm/blob/master/src/relay/pass/fold_constant.cc
+.. _src/relay/transforms/fold_constant.cc: https://github.com/apache/incubator-tvm/blob/master/src/relay/transforms/fold_constant.cc
diff --git a/docs/_sources/dev/virtual_machine.rst.txt b/docs/_sources/dev/virtual_machine.rst.txt
index 5bb5ade..5878003 100644
--- a/docs/_sources/dev/virtual_machine.rst.txt
+++ b/docs/_sources/dev/virtual_machine.rst.txt
@@ -38,8 +38,8 @@ them on the runtime. Graph runtime provides a fast execution experience but only
subset of Relay programs.
An alternative but not-standard approach is Relay's ahead-of-time compiler,
-which compiles a Relay program into a shared library containing an ahead-
-of-time implementation. The ahead-of-time compiler provides compelling performance
+which compiles a Relay program into a shared library containing an ahead-of-time
+implementation. The ahead-of-time compiler provides compelling performance
but is difficult to extend and instrument, which can only be done by modifying the
code generation and optimization mechanisms.
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index 09f15f8..f0ceb69 100644
--- a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
Computation times
=================
-**00:47.023** total execution time for **tutorials_autotvm** files:
-
-- **00:26.532**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:19.876**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.180**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.148**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.145**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.142**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+**00:48.125** total execution time for **tutorials_autotvm** files:
+
+- **00:26.526**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:20.990**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.156**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.154**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.154**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.145**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index 5bdc8b8..b8ef167 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -234,26 +234,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- 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) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\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) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\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) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 7 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 8 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 9 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 10 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 11 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 12 GFLOPS: 71.32/71.32 result: MeasureResult(costs=(0.0032459606750000003,), error_no=0, all_cost=1.6292922496795654, timestamp=1593745237.4247222) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
- No: 13 GFLOPS: 0.00/71.32 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 14 GFLOPS: 0.00/71.32 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 15 GFLOPS: 0.00/71.32 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 16 GFLOPS: 0.00/71.32 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 17 GFLOPS: 0.00/71.32 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 18 GFLOPS: 0.00/71.32 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (3) /workspace/build/libtvm.so(+0x4ed7d7) [0x7f16248497d7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f1624849276]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 19 GFLOPS: 793.41/793.41 result: MeasureResult(costs=(0.0002917792207505519,), error_no=0, all_cost=2.0187714099884033, timestamp=1593745241.1444745) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
- No: 20 GFLOPS: 6.99/793.41 result: MeasureResult(costs=(0.033099542,), error_no=0, all_cost=1.5997724533081055, timestamp=1593745241.9657192) [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
+ 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) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\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) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\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) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 7 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 8 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 9 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 10 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 11 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 12 GFLOPS: 65.50/65.50 result: MeasureResult(costs=(0.0035342738749999996,), error_no=0, all_cost=1.604149341583252, timestamp=1594838916.341495) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
+ No: 13 GFLOPS: 0.00/65.50 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 14 GFLOPS: 0.00/65.50 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 15 GFLOPS: 0.00/65.50 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 16 GFLOPS: 0.00/65.50 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 17 GFLOPS: 0.00/65.50 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 18 GFLOPS: 0.00/65.50 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (3) /workspace/build/libtvm.so(+0x56bde7) [0x7f3eca036de7]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f3eca036886]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 19 GFLOPS: 757.03/757.03 result: MeasureResult(costs=(0.0003058017539503386,), error_no=0, all_cost=1.966531753540039, timestamp=1594838920.344363) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
+ No: 20 GFLOPS: 6.96/757.03 result: MeasureResult(costs=(0.033247296,), error_no=0, all_cost=1.6596012115478516, timestamp=1594838921.1786706) [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
@@ -307,7 +307,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
- Time cost of this operator: 0.000355
+ Time cost of this operator: 0.000356
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
index 1f7addb..897d8e7 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
@@ -200,7 +200,7 @@ set :code:`use_android` to True if you use android phone.
# Replace "aarch64-linux-gnu" with the correct target of your board.
# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
- target = tvm.target.create('llvm -device=arm_cpu -target=aarch64-linux-gnu')
+ target = tvm.target.create('llvm -device=arm_cpu -mtriple=aarch64-linux-gnu')
# Also replace this with the device key in your tracker
device_key = 'rk3399'
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
index 8f83263..4f96af3 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
@@ -140,11 +140,7 @@ Before tuning, we apply some configurations.
'measure_option': autotvm.measure_option(
builder=autotvm.LocalBuilder(timeout=10),
- #runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
- runner=autotvm.RPCRunner(
- '1080ti', # change the device key to your key
- '0.0.0.0', 9190,
- number=20, repeat=3, timeout=4, min_repeat_ms=150)
+ runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
),
}
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 f343f9f..73cdd7c 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
@@ -201,7 +201,7 @@ set :code:`use_android` to True if you use android phone.
# Replace "aarch64-linux-gnu" with the correct target of your board.
# This target host is used for cross compilation. You can query it by :code:`gcc -v` on your device.
- target_host = 'llvm -target=aarch64-linux-gnu'
+ target_host = 'llvm -mtriple=aarch64-linux-gnu'
# Also replace this with the device key in your tracker
device_key = 'rk3399'
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index cba0e45..2b981b0 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -361,16 +361,16 @@ used to get the best config later.
.. code-block:: none
Get devices for measurement successfully!
- No: 1 GFLOPS: 6.78/6.78 result: MeasureResult(costs=(0.039591604,), error_no=0, all_cost=1.0488479137420654, timestamp=1593745207.5358753) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
- No: 2 GFLOPS: 2.24/6.78 result: MeasureResult(costs=(0.1196258708,), error_no=0, all_cost=2.235098361968994, timestamp=1593745209.7621841) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
- No: 3 GFLOPS: 7.30/7.30 result: MeasureResult(costs=(0.0367613626,), error_no=0, all_cost=1.3152332305908203, timestamp=1593745210.7636383) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 4 GFLOPS: 4.46/7.30 result: MeasureResult(costs=(0.060166502600000005,), error_no=0, all_cost=1.3689393997192383, timestamp=1593745212.0622144) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
- No: 5 GFLOPS: 5.89/7.30 result: MeasureResult(costs=(0.0455629548,), error_no=0, all_cost=1.023970127105713, timestamp=1593745213.1131737) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
- No: 6 GFLOPS: 6.05/7.30 result: MeasureResult(costs=(0.0443622662,), error_no=0, all_cost=1.5819377899169922, timestamp=1593745214.1444423) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
- No: 7 GFLOPS: 25.76/25.76 result: MeasureResult(costs=(0.010421797600000001,), error_no=0, all_cost=0.6426005363464355, timestamp=1593745214.6381452) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
- No: 8 GFLOPS: 21.44/25.76 result: MeasureResult(costs=(0.0125213934,), error_no=0, all_cost=0.611748218536377, timestamp=1593745215.1676252) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
- No: 9 GFLOPS: 0.88/25.76 result: MeasureResult(costs=(0.3052182662,), error_no=0, all_cost=5.2150187492370605, timestamp=1593745220.3343065) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
- No: 10 GFLOPS: 0.00/25.76 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f16252121a1]\n [bt] (4) /workspace/build/libtvm.so(+0xee55a2) [0x7f16252415a2]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7f162524491b]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::CallF [...]
+ No: 1 GFLOPS: 6.81/6.81 result: MeasureResult(costs=(0.039431393599999996,), error_no=0, all_cost=0.8926143646240234, timestamp=1594838886.2628007) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+ No: 2 GFLOPS: 2.24/6.81 result: MeasureResult(costs=(0.11989471519999999,), error_no=0, all_cost=2.2794392108917236, timestamp=1594838888.51563) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+ No: 3 GFLOPS: 7.30/7.30 result: MeasureResult(costs=(0.0367874284,), error_no=0, all_cost=1.3788068294525146, timestamp=1594838889.4407125) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 4 GFLOPS: 4.25/7.30 result: MeasureResult(costs=(0.06320074319999999,), error_no=0, all_cost=1.3663620948791504, timestamp=1594838890.7862232) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+ No: 5 GFLOPS: 6.02/7.30 result: MeasureResult(costs=(0.044613433,), error_no=0, all_cost=1.0256812572479248, timestamp=1594838891.8365152) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+ No: 6 GFLOPS: 6.09/7.30 result: MeasureResult(costs=(0.0441040612,), error_no=0, all_cost=1.0365724563598633, timestamp=1594838892.8824277) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
+ No: 7 GFLOPS: 24.79/24.79 result: MeasureResult(costs=(0.0108270606,), error_no=0, all_cost=0.6016161441802979, timestamp=1594838893.3945599) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
+ No: 8 GFLOPS: 20.88/24.79 result: MeasureResult(costs=(0.012858057400000001,), error_no=0, all_cost=0.719602108001709, timestamp=1594838893.936547) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
+ No: 9 GFLOPS: 0.92/24.79 result: MeasureResult(costs=(0.292011726,), error_no=0, all_cost=5.070920705795288, timestamp=1594838898.9400105) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+ No: 10 GFLOPS: 0.00/24.79 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f3ecaa32ad1]\n [bt] (4) /workspace/build/libtvm.so(+0xf96ed2) [0x7f3ecaa61ed2]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7f3ecaa6524b]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::CallF [...]
diff --git a/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
index a8d1709..96f3a01 100644
--- a/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
@@ -104,7 +104,7 @@ Here we will declare a simple kernel on the local machine:
Then we cross compile the kernel.
-The target should be 'llvm -target=armv7l-linux-gnueabihf' for
+The target should be 'llvm -mtriple=armv7l-linux-gnueabihf' for
Raspberry Pi 3B, but we use 'llvm' here to make this tutorial runnable
on our webpage building server. See the detailed note in the following block.
@@ -117,7 +117,7 @@ on our webpage building server. See the detailed note in the following block.
if local_demo:
target = 'llvm'
else:
- target = 'llvm -target=armv7l-linux-gnueabihf'
+ target = 'llvm -mtriple=armv7l-linux-gnueabihf'
func = tvm.build(s, [A, B], target=target, name='add_one')
# save the lib at a local temp folder
@@ -137,14 +137,14 @@ on our webpage building server. See the detailed note in the following block.
to False and replace :code:`target` in :code:`build` with the appropriate
target triple for your device. The target triple which might be
different for different devices. For example, it is
- :code:`'llvm -target=armv7l-linux-gnueabihf'` for Raspberry Pi 3B and
- :code:`'llvm -target=aarch64-linux-gnu'` for RK3399.
+ :code:`'llvm -mtriple=armv7l-linux-gnueabihf'` for Raspberry Pi 3B and
+ :code:`'llvm -mtriple=aarch64-linux-gnu'` for RK3399.
Usually, you can query the target by running :code:`gcc -v` on your
device, and looking for the line starting with :code:`Target:`
(Though it may still be a loose configuration.)
- Besides :code:`-target`, you can also set other compilation options
+ Besides :code:`-mtriple`, you can also set other compilation options
like:
* -mcpu=<cpuname>
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.693e-07 secs/op
+ 1.754e-07 secs/op
@@ -268,7 +268,7 @@ The following function shows how we run an OpenCL kernel remotely
def run_opencl():
# NOTE: This is the setting for my rk3399 board. You need to modify
# them according to your environment.
- target_host = "llvm -target=aarch64-linux-gnu"
+ target_host = "llvm -mtriple=aarch64-linux-gnu"
opencl_device_host = '10.77.1.145'
opencl_device_port = 9090
diff --git a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt b/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
index b758cc8..f813330 100644
--- a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
+++ b/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
@@ -19,7 +19,7 @@ introduced an infrastructure to manage the optimization passes.
The optimizations of a Relay program could be applied at various granularity,
namely function-level and module-level using :py:class:`tvm.relay.transform.FunctionPass`
-and py:class:`tvm.relay.transform.ModulePass`
+and :py:class:`tvm.relay.transform.ModulePass`
respectively. Or users can rely on py:class:`tvm.transform.Sequential` to apply a sequence of passes
on a Relay program where the dependencies between passes can be resolved by the
pass infra. For more details about each type of these passes, please refer to
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index e2fb71b..63b9694 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.458** total execution time for **tutorials_dev** files:
+**00:00.479** total execution time for **tutorials_dev** files:
-- **00:00.314**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
-- **00:00.143**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:00.331**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
+- **00:00.147**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/frontend/build_gcn.rst.txt b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
index d364c9c..8d9e4fd 100644
--- a/docs/_sources/tutorials/frontend/build_gcn.rst.txt
+++ b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
@@ -431,12 +431,11 @@ Export the weigths from PyTorch model to Python Dict
mod["main"] = func
# Build with Relay
with tvm.transform.PassContext(opt_level=0): # Currently only support opt_level=0
- graph, lib, params = relay.build(mod, target, params=params)
+ lib = relay.build(mod, target, params=params)
# Generate graph runtime
ctx = tvm.context(target, 0)
- m = graph_runtime.create(graph, lib, ctx)
- m.set_input(**params)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
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 1ad02a0..87abfa3 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -297,7 +297,7 @@ to run this tutorial with a real device.
# Change target configuration.
# Run `adb shell cat /proc/cpuinfo` to find the arch.
arch = 'arm64'
- target = 'llvm -target=%s-linux-android' % arch
+ target = 'llvm -mtriple=%s-linux-android' % arch
target_host = None
if local_demo:
@@ -315,8 +315,8 @@ to run this tutorial with a real device.
mod, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target=target,
- target_host=target_host, params=params)
+ lib = relay.build(mod, target=target,
+ target_host=target_host, params=params)
# After `relay.build`, you will get three return values: graph,
# library and the new parameter, since we do some optimization that will
@@ -369,7 +369,7 @@ to the remote android device.
rlib = remote.load_module('net.so')
# create the remote runtime module
- module = runtime.create(graph, rlib, ctx)
+ module = runtime.GraphModule(rlib['default'](ctx))
@@ -384,8 +384,6 @@ Execute on TVM
.. code-block:: default
- # set parameter (upload params to the remote device. This may take a while)
- module.set_input(**params)
# set input data
module.set_input(input_name, tvm.nd.array(x.astype(dtype)))
# run
@@ -415,7 +413,7 @@ Execute on TVM
TVM prediction top-1: tiger cat
Evaluate inference time cost...
- Mean inference time (std dev): 4.31 ms (0.46 ms)
+ Mean inference time (std dev): 5.22 ms (0.02 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 e5c3dea..b027aed 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
@@ -238,10 +238,10 @@ to run this tutorial with a real device.
else:
target = tvm.target.arm_cpu('rasp3b')
# The above line is a simple form of
- # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')
+ # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -mtriple=armv7l-linux-gnueabihf -mattr=+neon')
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(func, target, params=params)
+ lib = relay.build(func, target, params=params)
# After `relay.build`, you will get three return values: graph,
# library and the new parameter, since we do some optimization that will
@@ -282,9 +282,7 @@ to the remote device.
# create the remote runtime module
ctx = remote.cpu(0)
- module = runtime.create(graph, rlib, ctx)
- # set parameter (upload params to the remote device. This may take a while)
- module.set_input(**params)
+ module = runtime.GraphModule(rlib['default'](ctx))
# set input data
module.set_input('data', tvm.nd.array(x.astype('float32')))
# run
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
index 8e07388..96c575a 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -85,10 +85,9 @@ Helper functions to run the demo
def run_tvm_model(mod, params, input_name, inp, target="llvm"):
with tvm.transform.PassContext(opt_level=3):
- json, lib, params = relay.build(mod, target=target, params=params)
+ lib = relay.build(mod, target=target, params=params)
- runtime = tvm.contrib.graph_runtime.create(json, lib, tvm.context(target, 0))
- runtime.set_input(**params)
+ runtime = tvm.contrib.graph_runtime.GraphModule(lib['default'](tvm.context(target, 0)))
runtime.set_input(input_name, inp)
runtime.run()
@@ -352,7 +351,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 13.114505149999998
+ Elapsed average ms: 19.127417110000003
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index f84690e..732df94 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
@@ -219,10 +219,9 @@ Lets run TVM compiled pre-quantized model inference and get the TVM prediction.
.. code-block:: default
- def run_tvm(graph, lib, params):
+ def run_tvm(lib):
from tvm.contrib import graph_runtime
- rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
- rt_mod.set_input(**params)
+ rt_mod = graph_runtime.GraphModule(lib['default'](tvm.cpu(0)))
rt_mod.set_input('input', data)
rt_mod.run()
tvm_res = rt_mod.get_output(0).asnumpy()
@@ -286,8 +285,7 @@ target platform that you are interested in.
target = 'llvm'
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build_module.build(mod, target=target,
- params=params)
+ lib = relay.build_module.build(mod, target=target, params=params)
@@ -300,7 +298,7 @@ Finally, lets call inference on the TVM compiled module.
.. code-block:: default
- tvm_pred, rt_mod = run_tvm(graph, lib, params)
+ tvm_pred, rt_mod = run_tvm(lib)
@@ -361,7 +359,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 33.84403376999999
+ Elapsed average ms: 35.17905105
@@ -394,7 +392,7 @@ 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 4.482 seconds)
+ **Total running time of the script:** ( 2 minutes 6.650 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_sparse.rst.txt b/docs/_sources/tutorials/frontend/deploy_sparse.rst.txt
index c5d2b43..b6e778f 100644
--- a/docs/_sources/tutorials/frontend/deploy_sparse.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_sparse.rst.txt
@@ -246,15 +246,14 @@ tensors instead of sparse aware kernels.
def run_relay_graph(mod, params, shape_dict, target, ctx):
with relay.build_config(opt_level=3):
- graph, lib, params = relay.build(mod, target=target, params=params)
+ lib = relay.build(mod, target=target, params=params)
input_shape = shape_dict["input_1"]
dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype(
"int32"
)
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
m.set_input(0, dummy_data)
- m.set_input(**params)
m.run()
tvm_output = m.get_output(0)
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index e340eae..b121041 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -118,8 +118,8 @@ Convert and compile model for CPU.
def build(target):
mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
- return graph, lib, params
+ lib = relay.build(mod, target, params=params)
+ return lib
@@ -143,12 +143,11 @@ Create TVM runtime and do inference
.. code-block:: default
- def run(graph, lib, params, ctx):
+ def run(lib, ctx):
# Build TVM runtime
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)
m.set_input('data', tvm_input)
- m.set_input(**params)
# execute
m.run()
# get outputs
@@ -156,8 +155,8 @@ Create TVM runtime and do inference
return class_IDs, scores, bounding_boxs
for target, ctx in target_list:
- graph, lib, params = build(target)
- class_IDs, scores, bounding_boxs = run(graph, lib, params, ctx)
+ lib = build(target)
+ class_IDs, scores, bounding_boxs = run(lib, ctx)
@@ -169,123 +168,123 @@ Create TVM runtime and do inference
.. code-block:: none
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 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, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32, 4), 'float32'), ('TENSOR', (12, 256, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 32, 32, 8), 'float32'), ('TENSOR', (128, 32, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 32, 32, 8), 'float32'), ('TENSOR', (32, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 32, 32, 8), 'float32'), ('TENSOR', (32, 128, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 64, 64, 8), 'float32'), ('TENSOR', (32, 64, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 16, 64, 64, 8), 'float32'), ('TENSOR', (64, 16, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 16, 64, 64, 8), 'float32'), ('TENSOR', (16, 16, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 64, 64, 8), 'float32'), ('TENSOR', (16, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 128, 128, 8), 'float32'), ('TENSOR', (16, 32, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 8, 128, 128, 8), 'float32'), ('TENSOR', (32, 8, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 8, 128, 128, 8), 'float32'), ('TENSOR', (8, 8, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 128, 128, 8), 'float32'), ('TENSOR', (8, 32, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 8, 128, 128, 8), 'float32'), ('TENSOR', (8, 8, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1, 512, 512, 3), 'float32'), ('TENSOR', (8, 1, 7, 7, 3, 8), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'NCHW3c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 128, 128, 8), 'float32'), ('TENSOR', (64, 32, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 64, 64, 8), 'float32'), ('TENSOR', (128, 64, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16, 4), 'float32'), ('TENSOR', (18, 512, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 16, 16, 8), 'float32'), ('TENSOR', (256, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 16, 16, 8), 'float32'), ('TENSOR', (64, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 16, 16, 8), 'float32'), ('TENSOR', (64, 256, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 32, 32, 8), 'float32'), ('TENSOR', (64, 128, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 32, 32, 8), 'float32'), ('TENSOR', (256, 128, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 8, 8, 4), 'float32'), ('TENSOR', (18, 128, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 16, 16, 8), 'float32'), ('TENSOR', (64, 64, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 4, 4, 4), 'float32'), ('TENSOR', (18, 128, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 8, 8, 8), 'float32'), ('TENSOR', (64, 64, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 8, 8, 8), 'float32'), ('TENSOR', (64, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 2, 2, 4), 'float32'), ('TENSOR', (12, 64, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 4, 4, 8), 'float32'), ('TENSOR', (32, 32, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 4, 4, 8), 'float32'), ('TENSOR', (32, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 1, 1, 4), 'float32'), ('TENSOR', (12, 64, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 2, 2, 8), 'float32'), ('TENSOR', (32, 32, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 2, 2, 8), 'float32'), ('TENSOR', (32, 32, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 32, 32, 8), 'float32'), ('TENSOR', (2, 128, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 16, 16, 8), 'float32'), ('TENSOR', (3, 256, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 8, 8, 8), 'float32'), ('TENSOR', (3, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 4, 4, 8), 'float32'), ('TENSOR', (3, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 2, 2, 8), 'float32'), ('TENSOR', (2, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 1, 1, 8), 'float32'), ('TENSOR', (2, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), '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, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (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, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 128, 128), 'float32'), ('TENSOR', (64, 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, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (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, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 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, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (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, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (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, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 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, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (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, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 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, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (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, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 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, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (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, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 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, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (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, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 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, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (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, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 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, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (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, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 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, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (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, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (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, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 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, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 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, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 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, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 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, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 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, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 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, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 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, 256, 32, 32, 4), 'float32'), ('TENSOR', (12, 256, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', '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, 32, 32, 32, 8), 'float32'), ('TENSOR', (128, 32, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 32, 32, 32, 8), 'float32'), ('TENSOR', (32, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 128, 32, 32, 8), 'float32'), ('TENSOR', (32, 128, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 64, 64, 8), 'float32'), ('TENSOR', (32, 64, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 16, 64, 64, 8), 'float32'), ('TENSOR', (64, 16, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 16, 64, 64, 8), 'float32'), ('TENSOR', (16, 16, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 64, 64, 8), 'float32'), ('TENSOR', (16, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 32, 128, 128, 8), 'float32'), ('TENSOR', (16, 32, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 8, 128, 128, 8), 'float32'), ('TENSOR', (32, 8, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 8, 128, 128, 8), 'float32'), ('TENSOR', (8, 8, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 32, 128, 128, 8), 'float32'), ('TENSOR', (8, 32, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 8, 128, 128, 8), 'float32'), ('TENSOR', (8, 8, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 512, 512, 3), 'float32'), ('TENSOR', (8, 1, 7, 7, 3, 8), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'NCHW3c', 'NCHW8c', '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, 32, 128, 128, 8), 'float32'), ('TENSOR', (64, 32, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 64, 64, 8), 'float32'), ('TENSOR', (128, 64, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 512, 16, 16, 4), 'float32'), ('TENSOR', (18, 512, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', '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, 16, 16, 8), 'float32'), ('TENSOR', (256, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 16, 16, 8), 'float32'), ('TENSOR', (64, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 256, 16, 16, 8), 'float32'), ('TENSOR', (64, 256, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 128, 32, 32, 8), 'float32'), ('TENSOR', (64, 128, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 128, 32, 32, 8), 'float32'), ('TENSOR', (256, 128, 1, 1, 8, 8), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 128, 8, 8, 4), 'float32'), ('TENSOR', (18, 128, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', '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, 16, 16, 8), 'float32'), ('TENSOR', (64, 64, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 128, 4, 4, 4), 'float32'), ('TENSOR', (18, 128, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', '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, 8, 8, 8), 'float32'), ('TENSOR', (64, 64, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 8, 8, 8), 'float32'), ('TENSOR', (64, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 2, 2, 4), 'float32'), ('TENSOR', (12, 64, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', '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, 32, 4, 4, 8), 'float32'), ('TENSOR', (32, 32, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 4, 4, 8), 'float32'), ('TENSOR', (32, 64, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 1, 1, 4), 'float32'), ('TENSOR', (12, 64, 3, 3, 4, 7), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW4c', 'NCHW7c', '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, 32, 2, 2, 8), 'float32'), ('TENSOR', (32, 32, 3, 3, 8, 8), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 32, 2, 2, 8), 'float32'), ('TENSOR', (32, 32, 1, 1, 8, 8), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW8c', 'NCHW8c', '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, 128, 32, 32, 8), 'float32'), ('TENSOR', (2, 128, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 256, 16, 16, 8), 'float32'), ('TENSOR', (3, 256, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 8, 8, 8), 'float32'), ('TENSOR', (3, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 4, 4, 8), 'float32'), ('TENSOR', (3, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 32, 2, 2, 8), 'float32'), ('TENSOR', (2, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', '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, 32, 1, 1, 8), 'float32'), ('TENSOR', (2, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/frontend/from_caffe2.rst.txt b/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
index ddb9a53..1ea013f 100644
--- a/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
@@ -112,7 +112,7 @@ Compile the model on Relay
# target x86 CPU
target = 'llvm'
with transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
+ lib = relay.build(mod, target, params=params)
@@ -133,11 +133,9 @@ The process is no different from other examples.
# context x86 CPU, use tvm.gpu(0) if you run on GPU
ctx = tvm.cpu(0)
# create a runtime executor module
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input(input_name, tvm.nd.array(data.astype('float32')))
- # set related params
- m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_sources/tutorials/frontend/from_coreml.rst.txt b/docs/_sources/tutorials/frontend/from_coreml.rst.txt
index eb48cb6..93a11fe 100644
--- a/docs/_sources/tutorials/frontend/from_coreml.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_coreml.rst.txt
@@ -111,9 +111,7 @@ We should be familiar with the process right now.
mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target,
- params=params)
+ lib = relay.build(mod, target, params=params)
@@ -131,10 +129,9 @@ The process is no different from other example
from tvm.contrib import graph_runtime
ctx = tvm.cpu(0)
dtype = 'float32'
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('image', tvm.nd.array(x.astype(dtype)))
- m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_sources/tutorials/frontend/from_darknet.rst.txt b/docs/_sources/tutorials/frontend/from_darknet.rst.txt
index af3f0c7..e645351 100644
--- a/docs/_sources/tutorials/frontend/from_darknet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_darknet.rst.txt
@@ -134,10 +134,7 @@ compile the model
shape = {'data': data.shape}
print("Compiling the model...")
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target=target,
- target_host=target_host,
- params=params)
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
[neth, netw] = shape['data'][2:] # Current image shape is 608x608
@@ -190,11 +187,10 @@ The process is no different from other examples.
from tvm.contrib import graph_runtime
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('data', tvm.nd.array(data.astype(dtype)))
- m.set_input(**params)
# execute
print("Running the test image...")
diff --git a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
index b24a761..efb8f3b 100644
--- a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
@@ -125,7 +125,7 @@ now compile the graph
target = 'cuda'
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(func, target, params=params)
+ lib = relay.build(func, target, params=params)
@@ -143,10 +143,9 @@ Now, we would like to reproduce the same forward computation using TVM.
from tvm.contrib import graph_runtime
ctx = tvm.gpu(0)
dtype = 'float32'
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('data', tvm.nd.array(x.astype(dtype)))
- m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index c6c05d6..da49da2 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -125,7 +125,7 @@ Compile the model with relay
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/onnx.py:2287: UserWarning: Mismatched attribute type in ' : kernel_shape'
+ /workspace/docs/../python/tvm/relay/frontend/onnx.py:2409: 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))
@@ -151,10 +151,10 @@ Execute on TVM
.. code-block:: none
-
...47%, 0.01 MB, 47 KB/s, 0 seconds passed
...94%, 0.02 MB, 94 KB/s, 0 seconds passed
...100%, 0.02 MB, 141 KB/s, 0 seconds passed
- Cannot find config for target=llvm, 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, 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, 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.
+
...47%, 0.01 MB, 823 KB/s, 0 seconds passed
...94%, 0.02 MB, 1522 KB/s, 0 seconds passed
...100%, 0.02 MB, 2253 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.
diff --git a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
index dbd520b..0b360f6 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -150,10 +150,7 @@ Compile the graph to llvm target with given input specification.
target_host = 'llvm'
ctx = tvm.cpu(0)
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target=target,
- target_host=target_host,
- params=params)
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
@@ -170,10 +167,9 @@ Now we can try deploying the compiled model on target.
from tvm.contrib import graph_runtime
dtype = 'float32'
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
# Set inputs
m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
- m.set_input(**params)
# Execute
m.run()
# Get outputs
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index 64ef6e7..5ffa6a4 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -219,10 +219,7 @@ Results:
with tvm.transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod,
- target=target,
- target_host=target_host,
- params=params)
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
@@ -240,10 +237,9 @@ Now we can try deploying the compiled model on target.
from tvm.contrib import graph_runtime
dtype = 'uint8'
- m = graph_runtime.create(graph, lib, ctx)
+ m = graph_runtime.GraphModule(lib['default'](ctx))
# set inputs
m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))
- m.set_input(**params)
# execute
m.run()
# get outputs
diff --git a/docs/_sources/tutorials/frontend/from_tflite.rst.txt b/docs/_sources/tutorials/frontend/from_tflite.rst.txt
index a23bc38..6a003d6 100644
--- a/docs/_sources/tutorials/frontend/from_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tflite.rst.txt
@@ -178,7 +178,7 @@ Compile the model with relay
# Build the module against to x86 CPU
target = "llvm"
with transform.PassContext(opt_level=3):
- graph, lib, params = relay.build(mod, target, params=params)
+ lib = relay.build(mod, target, params=params)
@@ -197,14 +197,11 @@ Execute on TVM
from tvm.contrib import graph_runtime as runtime
# Create a runtime executor module
- module = runtime.create(graph, lib, tvm.cpu())
+ module = runtime.GraphModule(lib['default'](tvm.cpu()))
# Feed input data
module.set_input(input_tensor, tvm.nd.array(image_data))
- # Feed related params
- module.set_input(**params)
-
# Run
module.run()
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index 051dbd5..c79d117 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,23 +5,23 @@
Computation times
=================
-**05:27.339** total execution time for **tutorials_frontend** files:
+**05:40.551** total execution time for **tutorials_frontend** files:
-- **02:04.482**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **00:29.629**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:29.048**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:26.466**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:20.677**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:18.378**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:16.755**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:10.554**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:09.594**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:08.537**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:06.934**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:06.808**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:06.118**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:04.935**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:04.635**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:02.086**: :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``)
-- **00:00.152**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
+- **02:06.650**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **00:31.429**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:30.305**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:27.478**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:21.411**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:19.452**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:17.016**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:11.026**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:10.302**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:09.392**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:09.021**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:07.264**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:06.329**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:05.102**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:04.732**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:02.053**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.433**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:00.157**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/tutorials/frontend/using_external_lib.rst.txt b/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
index 1cd717a..6cf0bff 100644
--- a/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
+++ b/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
@@ -84,13 +84,11 @@ By setting the logging level to DEBUG, the result of Relay graph compilation wil
logging.basicConfig(level=logging.DEBUG) # to dump TVM IR after fusion
target = "cuda"
- graph, lib, params = relay.build_module.build(
- net, target, params=params)
+ lib = relay.build_module.build(net, target, params=params)
ctx = tvm.context(target, 0)
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
- module = runtime.create(graph, lib, ctx)
- module.set_input(**params)
+ module = runtime.GraphModule(lib['default'](ctx))
module.set_input("data", data)
module.run()
out_shape = (batch_size, out_channels, 224, 224)
@@ -113,7 +111,7 @@ By setting the logging level to DEBUG, the result of Relay graph compilation wil
INFO:compile_engine:Use implementation injective.cpu for op multiply
INFO:compile_engine:Use implementation injective.cpu for op negative
INFO:compile_engine:Use implementation injective.cpu for op add
- WARNING:autotvm:Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 224, 224), 'float32'), ('TENSOR', (16, 3, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 224, 224), 'float32'), ('TENSOR', (16, 3, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
INFO:compile_engine:Use implementation conv2d_nchw.cuda for op nn.conv2d
INFO:compile_engine:Use implementation injective.cuda for op multiply
INFO:compile_engine:Use implementation injective.cuda for op add
@@ -534,13 +532,11 @@ To do that, all we need to do is to append the option " -libs=cudnn" to the targ
net, params = testing.create_workload(simple_net)
target = "cuda -libs=cudnn" # use cudnn for convolution
- graph, lib, params = relay.build_module.build(
- net, target, params=params)
+ lib = relay.build_module.build(net, target, params=params)
ctx = tvm.context(target, 0)
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
- module = runtime.create(graph, lib, ctx)
- module.set_input(**params)
+ module = runtime.GraphModule(lib['default'](ctx))
module.set_input("data", data)
module.run()
out_shape = (batch_size, out_channels, 224, 224)
diff --git a/docs/_sources/tutorials/index.rst.txt b/docs/_sources/tutorials/index.rst.txt
index ec1dc46..d77c32c 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -246,13 +246,13 @@ Compile Deep Learning Models
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This is an example of using Relay to compile a ResNet model and deploy it on Raspberry Pi. ">
+ <div class="sphx-glr-thumbcontainer" tooltip="This article is an introductory tutorial to deploy PyTorch models with Relay.">
.. only:: html
- .. figure:: /tutorials/frontend/images/thumb/sphx_glr_deploy_model_on_rasp_thumb.png
+ .. figure:: /tutorials/frontend/images/thumb/sphx_glr_from_pytorch_thumb.png
- :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py`
+ :ref:`sphx_glr_tutorials_frontend_from_pytorch.py`
.. raw:: html
@@ -262,17 +262,17 @@ Compile Deep Learning Models
.. toctree::
:hidden:
- /tutorials/frontend/deploy_model_on_rasp
+ /tutorials/frontend/from_pytorch
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This article is an introductory tutorial to deploy PyTorch models with Relay.">
+ <div class="sphx-glr-thumbcontainer" tooltip="This is an example of using Relay to compile a ResNet model and deploy it on Raspberry Pi. ">
.. only:: html
- .. figure:: /tutorials/frontend/images/thumb/sphx_glr_from_pytorch_thumb.png
+ .. figure:: /tutorials/frontend/images/thumb/sphx_glr_deploy_model_on_rasp_thumb.png
- :ref:`sphx_glr_tutorials_frontend_from_pytorch.py`
+ :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py`
.. raw:: html
@@ -282,7 +282,7 @@ Compile Deep Learning Models
.. toctree::
:hidden:
- /tutorials/frontend/from_pytorch
+ /tutorials/frontend/deploy_model_on_rasp
.. raw:: html
@@ -764,13 +764,13 @@ 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="Auto-tuning for specific devices and workloads is critical for getting the best performance. Th...">
.. only:: html
- .. figure:: /tutorials/autotvm/images/thumb/sphx_glr_tune_relay_x86_thumb.png
+ .. figure:: /tutorials/autotvm/images/thumb/sphx_glr_tune_relay_cuda_thumb.png
- :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py`
+ :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py`
.. raw:: html
@@ -780,17 +780,17 @@ Auto tuning
.. toctree::
:hidden:
- /tutorials/autotvm/tune_relay_x86
+ /tutorials/autotvm/tune_relay_cuda
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="Auto-tuning for specific devices and workloads is critical for getting the best performance. Th...">
+ <div class="sphx-glr-thumbcontainer" tooltip="This is a tutorial about how to tune convolution neural network for x86 CPU. ">
.. only:: html
- .. figure:: /tutorials/autotvm/images/thumb/sphx_glr_tune_relay_cuda_thumb.png
+ .. figure:: /tutorials/autotvm/images/thumb/sphx_glr_tune_relay_x86_thumb.png
- :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py`
+ :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py`
.. raw:: html
@@ -800,7 +800,7 @@ Auto tuning
.. toctree::
:hidden:
- /tutorials/autotvm/tune_relay_cuda
+ /tutorials/autotvm/tune_relay_x86
.. raw:: html
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index 52f46b2..d677ac1 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -449,13 +449,13 @@ of computation of `C`.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
+ buffers = {B: Buffer(B_2: handle, float32, [m: int32], [stride: int32], type="auto"),
+ C: Buffer(C_2: handle, float32, [m], [stride_1: int32], type="auto"),
A: Buffer(A_2: handle, 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)
- C_2[(i*stride)] = ((float32*)B_2[(i*stride_1)]*2f32)
+ B_2[(i*stride)] = ((float32*)A_2[(i*stride_2)] + 1f32)
+ C_2[(i*stride_1)] = ((float32*)B_2[(i*stride)]*2f32)
}
}
@@ -492,12 +492,12 @@ tensor is required.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- C: Buffer(C_2: handle, float32, [m], [stride_1: int32], type="auto"),
+ buffers = {C: Buffer(C_2: handle, float32, [m: int32], [stride: int32], type="auto"),
+ B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
A: Buffer(A_2: handle, float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
- C_2[(i*stride_1)] = (((float32*)A_2[(i*stride_2)] + 1f32)*2f32)
+ C_2[(i*stride)] = (((float32*)A_2[(i*stride_2)] + 1f32)*2f32)
}
}
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index faff215..0457fd1 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
=================
-**00:03.276** total execution time for **tutorials_language** files:
+**00:03.541** total execution time for **tutorials_language** files:
-- **00:01.150**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.555**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.450**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.413**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.226**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.165**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.164**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:01.258**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.591**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.489**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.452**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.247**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.180**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.170**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
- **00:00.154**: :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 ed11121..d7bdf99 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -236,8 +236,8 @@ such placeholder can be put to let TVM automatically bind the inferred value for
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 512], []),
- B: Buffer(B_2: handle, float32, [512, 64], []),
+ buffers = {B: Buffer(B_2: handle, float32, [512, 64], []),
+ C: Buffer(C_2: handle, float32, [1024, 512], []),
A: Buffer(A_2: handle, float32, [1024, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, 1024) {
@@ -310,8 +310,8 @@ The importing needs to happen before the tensorized GEMV being executed.
B: Buffer(B_2: handle, float32, [512, 64], []),
A: Buffer(A_2: handle, 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/tmpmel936f0/input0.cc'
- source_filename = "/tmp/tmpmel936f0/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpu5z0lc8e/input0.cc'
+ source_filename = "/tmp/tmpu5z0lc8e/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 0ed5d9c..b74c9b6 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -64,15 +64,15 @@ together in the next schedule procedure.
primfn(A0_1: handle, A1_1: handle, B.v0_1: handle, B.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B.v0: Buffer(B.v0_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A1: Buffer(A1_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- B.v1: Buffer(B.v1_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+ buffers = {B.v1: Buffer(B.v1_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B.v0: Buffer(B.v0_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A1: Buffer(A1_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
A0: Buffer(A0_2: handle, 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) + (j*stride_1))] = ((float32*)A0_2[((i*stride_6) + (j*stride_7))] + 2f32)
- B.v1_2[((i*stride_4) + (j*stride_5))] = ((float32*)A1_2[((i*stride_2) + (j*stride_3))]*3f32)
+ 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)
}
}
}
@@ -133,17 +133,17 @@ with :py:func:`te.comm_reducer` as below:
primfn(idx_1: handle, val_1: handle, T.v0_1: handle, T.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {T.v1: Buffer(T.v1_2: handle, int32, [m: int32], [stride: int32], type="auto"),
- T.v0: Buffer(T.v0_2: handle, int32, [m], [stride_1: int32], type="auto"),
- val: Buffer(val_2: handle, int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {T.v0: Buffer(T.v0_2: handle, int32, [m: int32], [stride: int32], type="auto"),
+ val: Buffer(val_2: handle, int32, [m, n: int32], [stride_1: int32, stride_2: int32], type="auto"),
+ T.v1: Buffer(T.v1_2: handle, int32, [m], [stride_3: int32], type="auto"),
idx: Buffer(idx_2: handle, int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {idx_1: idx, val_1: val, T.v0_1: T.v0, T.v1_1: T.v1} {
for (i: int32, 0, m) {
- T.v0_2[(i*stride_1)] = -1
- T.v1_2[(i*stride)] = -2147483648
+ T.v0_2[(i*stride)] = -1
+ T.v1_2[(i*stride_3)] = -2147483648
for (k: int32, 0, n) {
- T.v0_2[(i*stride_1)] = @tir.if_then_else(((int32*)val_2[((i*stride_2) + (k*stride_3))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v0_2[(i*stride_1)], (int32*)idx_2[((i*stride_4) + (k*stride_5))], dtype=int32)
- T.v1_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_2) + (k*stride_3))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v1_2[(i*stride)], (int32*)val_2[((i*stride_2) + (k*stride_3))], dtype=int32)
+ T.v0_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride_3)]), (int32*)T.v0_2[(i*stride)], (int32*)idx_2[((i*stride_4) + (k*stride_5))], dtype=int32)
+ T.v1_2[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride_3)]), (int32*)T.v1_2[(i*stride_3)], (int32*)val_2[((i*stride_1) + (k*stride_2))], dtype=int32)
}
}
}
@@ -191,8 +191,8 @@ in terms of operation.
primfn(A0_1: handle, A1_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A1: Buffer(A1_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {A1: Buffer(A1_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ C: Buffer(C_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
A0: Buffer(A0_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, C_1: C} {
attr [B.v0: handle] "storage_scope" = "global";
@@ -205,7 +205,7 @@ in terms of operation.
B.v1[j] = ((float32*)A0_2[((i*stride_4) + (j*stride_5))]*3f32)
}
for (j_1: int32, 0, n) {
- C_2[((i*stride) + (j_1*stride_1))] = ((float32*)A1_2[((i*stride_2) + (j_1*stride_3))] + (float32*)B.v0[j_1])
+ C_2[((i*stride_2) + (j_1*stride_3))] = ((float32*)A1_2[((i*stride) + (j_1*stride_1))] + (float32*)B.v0[j_1])
}
}
}
diff --git a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
index b7fde98..0252f31 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
=================
-**00:00.155** total execution time for **tutorials_micro** files:
+**00:00.158** total execution time for **tutorials_micro** files:
-- **00:00.155**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.158**: :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 e04350e..b886529 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -293,7 +293,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 19.549104 ms
+ Convolution: 19.571479 ms
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index 795f5c0..d4dee5d 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -563,7 +563,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 6.305341 ms
+ conv2d with tensor core: 6.307450 ms
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index 6d12cd1..030310a 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: 0.007645
- Baseline: 5.973757
+ Numpy running time: 0.006930
+ Baseline: 5.950333
@@ -206,7 +206,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.109398
+ Opt1: 0.127129
@@ -300,7 +300,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.118614
+ Opt2: 0.141108
@@ -389,7 +389,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.062121
+ Opt3: 0.070495
@@ -413,8 +413,8 @@ Here is the generated IR after loop permutation.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
+ buffers = {B: Buffer(B_2: handle, float32, [1024, 1024], []),
+ C: Buffer(C_2: handle, float32, [1024, 1024], []),
A: Buffer(A_2: handle, float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x.outer: int32, 0, 32) {
@@ -497,7 +497,7 @@ the corresponding value from the packed array.
.. code-block:: none
- Opt4: 0.063075
+ Opt4: 0.068150
@@ -521,8 +521,8 @@ Here is the generated IR after array packing.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [1024, 1024], []),
- C: Buffer(C_2: handle, float32, [1024, 1024], []),
+ buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
+ B: Buffer(B_2: handle, float32, [1024, 1024], []),
A: Buffer(A_2: handle, float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [packedB: handle] "storage_scope" = "global";
@@ -607,7 +607,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.065756
+ Opt5: 0.061840
@@ -723,7 +723,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.016878
+ Opt6: 0.017352
@@ -747,8 +747,8 @@ Here is the generated IR after parallelization.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
+ buffers = {B: Buffer(B_2: handle, float32, [1024, 1024], []),
+ C: Buffer(C_2: handle, float32, [1024, 1024], []),
A: Buffer(A_2: handle, float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [packedB: handle] "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 8245160..ad44083 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
=================
-**00:27.059** total execution time for **tutorials_optimize** files:
+**00:27.636** total execution time for **tutorials_optimize** files:
-- **00:25.353**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:00.888**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:00.664**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.155**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:25.842**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:00.962**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.669**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.163**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
diff --git a/docs/_sources/tutorials/relay_quick_start.rst.txt b/docs/_sources/tutorials/relay_quick_start.rst.txt
index 310518f..c3d79b7 100644
--- a/docs/_sources/tutorials/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/relay_quick_start.rst.txt
@@ -223,8 +223,8 @@ in this example. Then the machine code will be generated as the module library.
.. code-block:: none
-
...1%, 0.01 MB, 37 KB/s, 0 seconds passed
...3%, 0.02 MB, 75 KB/s, 0 seconds passed
...5%, 0.02 MB, 113 KB/s, 0 seconds passed
...7%, 0.03 MB, 150 KB/s, 0 seconds passed
...9%, 0.04 MB, 187 KB/s, 0 seconds passed
...11%, 0.05 MB, 224 KB/s, 0 seconds passed
...13%, 0.05 MB, 261 KB/s, 0 seconds passed
...15%, 0.06 MB, 298 KB/s, 0 seconds passed
...17%, 0.07 MB, 335 KB/s, 0 seconds passed
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...23%, 0.09 MB, 445 KB/s, 0 seconds passed
...25%, 0.10 MB, 481 KB/s, 0 seconds passed
...27%, 0.11 MB, 518 KB/s, 0 seconds passed
...29%, 0.12 MB, 554 KB/s, 0 seconds passed
...31%, 0.12 MB, 590 KB/s, 0 seconds passed
...33%, 0.13 MB, 626 KB/s, 0 seconds passed
...35%, 0.14 MB, 662 KB/s, 0 seconds passed
...37%, 0.15 MB, 698 KB/s, 0 seconds passed
...39%, 0.16 MB, 734 KB/s, 0 seconds passed
...41%, 0.16 MB, 770 KB/s, 0 seconds pa
ssed
...43%, 0.17 MB, 804 KB/s, 0 seconds passed
...45%, 0.18 MB, 840 KB/s, 0 seconds passed
...47%, 0.19 MB, 876 KB/s, 0 seconds passed
...49%, 0.20 MB, 912 KB/s, 0 seconds passed
...51%, 0.20 MB, 948 KB/s, 0 seconds passed
...53%, 0.21 MB, 981 KB/s, 0 seconds passed
...55%, 0.22 MB, 1017 KB/s, 0 seconds passed
...57%, 0.23 MB, 1052 KB/s, 0 seconds passed
...59%, 0.23 MB, 1087 KB/s, 0 seconds passed
...61%, 0.24 MB, 1121 KB/s, 0 seconds passed
...63%, 0.25 MB, 1157 KB/s, 0 seconds passed
...65%, 0.26 MB, 1192 KB/s, 0 seconds passed
...67%, 0.27 MB, 1228 KB/s, 0 seconds passed
...69%, 0.27 MB, 1262 KB/s, 0 seconds passed
...71%, 0.28 MB, 1297 KB/s, 0 seconds passed
...73%, 0.29 MB, 1332 KB/s, 0 seconds passed
...75%, 0.30 MB, 1367 KB/s, 0 seconds passed
...77%, 0.30 MB, 1399 KB/s, 0 seconds passed
...79%, 0.31 MB, 1434 KB/s, 0 seconds passed
...81%, 0.32 MB, 1469 KB/s, 0 seconds passed
...83%, 0.33 M
B, 1504 KB/s, 0 seconds passed
...85%, 0.34 MB, 1539 KB/s, 0 seconds passed
...87%, 0.34 MB, 1575 KB/s, 0 seconds passed
...89%, 0.35 MB, 1606 KB/s, 0 seconds passed
...91%, 0.36 MB, 1641 KB/s, 0 seconds passed
...93%, 0.37 MB, 1676 KB/s, 0 seconds passed
...95%, 0.38 MB, 1711 KB/s, 0 seconds passed
...97%, 0.38 MB, 1745 KB/s, 0 seconds passed
...99%, 0.39 MB, 1780 KB/s, 0 seconds passed
...100%, 0.40 MB, 1814 KB/s, 0 seconds passed
- Cannot find config for target=cuda -model=unknown, 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.
+
...1%, 0.01 MB, 21 KB/s, 0 seconds passed
...3%, 0.02 MB, 42 KB/s, 0 seconds passed
...5%, 0.02 MB, 63 KB/s, 0 seconds passed
...7%, 0.03 MB, 84 KB/s, 0 seconds passed
...9%, 0.04 MB, 105 KB/s, 0 seconds passed
...11%, 0.05 MB, 125 KB/s, 0 seconds passed
...13%, 0.05 MB, 146 KB/s, 0 seconds passed
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...19%, 0.08 MB, 208 KB/s, 0 seconds passed
...21%, 0.09 MB, 229 KB/s, 0 seconds passed
...23%, 0.09 MB, 250 KB/s, 0 seconds passed
...25%, 0.10 MB, 269 KB/s, 0 seconds passed
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...37%, 0.15 MB, 392 KB/s, 0 seconds passed
...39%, 0.16 MB, 412 KB/s, 0 seconds passed
...41%, 0.16 MB, 433 KB/s, 0 seconds pass
ed
...43%, 0.17 MB, 453 KB/s, 0 seconds passed
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...59%, 0.23 MB, 612 KB/s, 0 seconds passed
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...75%, 0.30 MB, 770 KB/s, 0 seconds passed
...77%, 0.30 MB, 790 KB/s, 0 seconds passed
...79%, 0.31 MB, 810 KB/s, 0 seconds passed
...81%, 0.32 MB, 828 KB/s, 0 seconds passed
...83%, 0.33 MB, 848 KB/s, 0 s
econds passed
...85%, 0.34 MB, 867 KB/s, 0 seconds passed
...87%, 0.34 MB, 887 KB/s, 0 seconds passed
...89%, 0.35 MB, 907 KB/s, 0 seconds passed
...91%, 0.36 MB, 926 KB/s, 0 seconds passed
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...97%, 0.38 MB, 983 KB/s, 0 seconds passed
...99%, 0.39 MB, 1003 KB/s, 0 seconds passed
...100%, 0.40 MB, 1022 KB/s, 0 seconds passed
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=('dense_small_batch.cuda', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/sg_execution_times.rst.txt b/docs/_sources/tutorials/sg_execution_times.rst.txt
index c8197f8..b7ceee9 100644
--- a/docs/_sources/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:09.684** total execution time for **tutorials** files:
+**00:10.247** total execution time for **tutorials** files:
-- **00:09.247**: :ref:`sphx_glr_tutorials_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.309**: :ref:`sphx_glr_tutorials_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.129**: :ref:`sphx_glr_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:09.779**: :ref:`sphx_glr_tutorials_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.336**: :ref:`sphx_glr_tutorials_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.132**: :ref:`sphx_glr_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index 1229000..4023bd1 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -230,7 +230,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, 0x1230f5830), stage(b, 0x14e175730), stage(T_add, 0x11b48e260), stage(T_multiply, 0xa6350d70), stage(T_elemwise_sum, 0x12868aef0), stage(T_divide, 0x14ac01630), stage(T_divide_red.rf, 0xc7c0ebb0), stage(T_divide_red, 0x128535490)]
+ [stage(a, 0x17c5dfaf0), stage(b, 0x1599bdfe0), stage(T_add, 0x18115f990), stage(T_multiply, 0x156b5edc0), stage(T_elemwise_sum, 0x1599bd5a0), stage(T_divide, 0x16551b0e0), stage(T_divide_red.rf, 0x1811612f0), stage(T_divide_red, 0x15a442880)]
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index c861977..e8da589 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
=================
-**00:00.491** total execution time for **tutorials_topi** files:
+**00:00.507** total execution time for **tutorials_topi** files:
-- **00:00.491**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.507**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
index e671bc9..836b040 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
=================
-**00:03.730** total execution time for **vta_tutorials_autotvm** files:
+**00:03.715** total execution time for **vta_tutorials_autotvm** files:
-- **00:03.730**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:03.715**: :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 847eab9..2e9c5d8 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -481,7 +481,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
.. code-block:: none
Extract tasks...
-
...1%, 0.01 MB, 37 KB/s, 0 seconds passed
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479 KB/s, 0 seconds passed
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...58%, 0.42 MB, 1882 KB/s, 0 seconds passed
...59%, 0.43 MB, 1917 KB/s, 0 seconds passed
...60%, 0.44 MB, 1949 KB/s, 0 seconds passed
...62%, 0.45 MB, 1983 KB/s, 0 seconds passed
...63%, 0.45 MB, 2015 KB/s, 0 seconds passed
...64%, 0.46 MB, 2049 KB/s, 0 seconds passed
...65%, 0.47 MB, 2080 KB/s, 0 seconds passed
...66%, 0.48 MB, 2113 KB/s, 0 seconds passed
...67%, 0.48 MB, 2146 KB/s, 0 secon
ds passed
...68%, 0.49 MB, 2180 KB/s, 0 seconds passed
...69%, 0.50 MB, 2211 KB/s, 0 seconds passed
...70%, 0.51 MB, 2244 KB/s, 0 seconds passed
...71%, 0.52 MB, 2272 KB/s, 0 seconds passed
...72%, 0.52 MB, 2305 KB/s, 0 seconds passed
...73%, 0.53 MB, 2338 KB/s, 0 seconds passed
...75%, 0.54 MB, 2372 KB/s, 0 seconds passed
...76%, 0.55 MB, 2405 KB/s, 0 seconds passed
...77%, 0.55 MB, 2438 KB/s, 0 seconds passed
...78%, 0.56 MB, 2468 KB/s, 0 seconds passed
...79%, 0.57 MB, 2502 KB/s, 0 seconds passed
...80%, 0.58 MB, 2535 KB/s, 0 seconds passed
...81%, 0.59 MB, 2568 KB/s, 0 seconds passed
...82%, 0.59 MB, 2601 KB/s, 0 seconds passed
...83%, 0.60 MB, 2634 KB/s, 0 seconds passed
...84%, 0.61 MB, 2662 KB/s, 0 seconds passed
...85%, 0.62 MB, 2695 KB/s, 0 seconds passed
...87%, 0.62 MB, 2727 KB/s, 0 seconds passed
...88%, 0.63 MB, 2760 KB/s, 0 seconds passed
...89%, 0.64 MB, 2793 KB/s, 0 seconds passed
...
90%, 0.65 MB, 2826 KB/s, 0 seconds passed
...91%, 0.66 MB, 2858 KB/s, 0 seconds passed
...92%, 0.66 MB, 2891 KB/s, 0 seconds passed
...93%, 0.67 MB, 2920 KB/s, 0 seconds passed
...94%, 0.68 MB, 2953 KB/s, 0 seconds passed
...95%, 0.69 MB, 2986 KB/s, 0 seconds passed
...96%, 0.70 MB, 3018 KB/s, 0 seconds passed
...97%, 0.70 MB, 3049 KB/s, 0 seconds passed
...99%, 0.71 MB, 3081 KB/s, 0 seconds passed
...100%, 0.72 MB, 3112 KB/s, 0 seconds passed
+
...1%, 0.01 MB, 39 KB/s, 0 seconds passed
...2%, 0.02 MB, 78 KB/s, 0 seconds passed
...3%, 0.02 MB, 117 KB/s, 0 seconds passed
...4%, 0.03 MB, 156 KB/s, 0 seconds passed
...5%, 0.04 MB, 195 KB/s, 0 seconds passed
...6%, 0.05 MB, 233 KB/s, 0 seconds passed
...7%, 0.05 MB, 271 KB/s, 0 seconds passed
...8%, 0.06 MB, 310 KB/s, 0 seconds passed
...9%, 0.07 MB, 348 KB/s, 0 seconds passed
...10%, 0.08 MB, 386 KB/s, 0 seconds passed
...11%, 0.09 MB, 423 KB/s, 0 seconds passed
...13%, 0.09 MB, 462 KB/s, 0 seconds passed
...14%, 0.10 MB, 499 KB/s, 0 seconds passed
...15%, 0.11 MB, 537 KB/s, 0 seconds passed
...16%, 0.12 MB, 574 KB/s, 0 seconds passed
...17%, 0.12 MB, 612 KB/s, 0 seconds passed
...18%, 0.13 MB, 649 KB/s, 0 seconds passed
...19%, 0.14 MB, 687 KB/s, 0 seconds passed
...20%, 0.15 MB, 724 KB/s, 0 seconds passed
...21%, 0.16 MB, 761 KB/s, 0 seconds passed
...22%, 0.16 MB, 798 KB/s, 0 seconds passed
...23%, 0.17 MB, 835 KB/s, 0 seconds passed
...25%, 0.18 MB, 872 KB/s, 0 seconds passed
...26%, 0.19 MB, 909 KB/s, 0 seconds passed
...27%, 0.20 MB, 945 KB/s, 0 seconds passed
...28%, 0.20 MB, 982 KB/s, 0 seconds passed
...29%, 0.21 MB, 1018 KB/s, 0 seconds passed
...30%, 0.22 MB, 1055 KB/s, 0 seconds passed
...31%, 0.23 MB, 1091 KB/s, 0 seconds passed
...32%, 0.23 MB, 1127 KB/s, 0 seconds passed
...33%, 0.24 MB, 1164 KB/s, 0 seconds passed
...34%, 0.25 MB, 1199 KB/s, 0 seconds passed
...35%, 0.26 MB, 1236 KB/s, 0 seconds passed
...36%, 0.27 MB, 1272 KB/s, 0 seconds passed
...38%, 0.27 MB, 1309 KB/s, 0 seconds passed
...39%, 0.28 MB, 1342 KB/s, 0 seconds passed
...40%, 0.29 MB, 1379 KB/s, 0 seconds passed
...41%, 0.30 MB, 1415 KB/s, 0 seconds passed
...42%, 0.30 MB, 1452 KB/s, 0 seconds passed
...43%, 0.31 MB, 1485 KB/s, 0 seconds passed
...44%, 0.32 MB, 1521 KB/s, 0 seconds passed
...45%, 0.33 MB,
1557 KB/s, 0 seconds passed
...46%, 0.34 MB, 1594 KB/s, 0 seconds passed
...47%, 0.34 MB, 1629 KB/s, 0 seconds passed
...48%, 0.35 MB, 1665 KB/s, 0 seconds passed
...50%, 0.36 MB, 1697 KB/s, 0 seconds passed
...51%, 0.37 MB, 1733 KB/s, 0 seconds passed
...52%, 0.38 MB, 1768 KB/s, 0 seconds passed
...53%, 0.38 MB, 1804 KB/s, 0 seconds passed
...54%, 0.39 MB, 1839 KB/s, 0 seconds passed
...55%, 0.40 MB, 1875 KB/s, 0 seconds passed
...56%, 0.41 MB, 1907 KB/s, 0 seconds passed
...57%, 0.41 MB, 1942 KB/s, 0 seconds passed
...58%, 0.42 MB, 1978 KB/s, 0 seconds passed
...59%, 0.43 MB, 2013 KB/s, 0 seconds passed
...60%, 0.44 MB, 2049 KB/s, 0 seconds passed
...62%, 0.45 MB, 2085 KB/s, 0 seconds passed
...63%, 0.45 MB, 2117 KB/s, 0 seconds passed
...64%, 0.46 MB, 2153 KB/s, 0 seconds passed
...65%, 0.47 MB, 2187 KB/s, 0 seconds passed
...66%, 0.48 MB, 2222 KB/s, 0 seconds passed
...67%, 0.48 MB, 2257 KB/s, 0 seco
nds passed
...68%, 0.49 MB, 2292 KB/s, 0 seconds passed
...69%, 0.50 MB, 2323 KB/s, 0 seconds passed
...70%, 0.51 MB, 2358 KB/s, 0 seconds passed
...71%, 0.52 MB, 2393 KB/s, 0 seconds passed
...72%, 0.52 MB, 2428 KB/s, 0 seconds passed
...73%, 0.53 MB, 2462 KB/s, 0 seconds passed
...75%, 0.54 MB, 2497 KB/s, 0 seconds passed
...76%, 0.55 MB, 2530 KB/s, 0 seconds passed
...77%, 0.55 MB, 2565 KB/s, 0 seconds passed
...78%, 0.56 MB, 2599 KB/s, 0 seconds passed
...79%, 0.57 MB, 2634 KB/s, 0 seconds passed
...80%, 0.58 MB, 2667 KB/s, 0 seconds passed
...81%, 0.59 MB, 2702 KB/s, 0 seconds passed
...82%, 0.59 MB, 2733 KB/s, 0 seconds passed
...83%, 0.60 MB, 2767 KB/s, 0 seconds passed
...84%, 0.61 MB, 2797 KB/s, 0 seconds passed
...85%, 0.62 MB, 2832 KB/s, 0 seconds passed
...87%, 0.62 MB, 2866 KB/s, 0 seconds passed
...88%, 0.63 MB, 2900 KB/s, 0 seconds passed
...89%, 0.64 MB, 2935 KB/s, 0 seconds passed
..
.90%, 0.65 MB, 2969 KB/s, 0 seconds passed
...91%, 0.66 MB, 3003 KB/s, 0 seconds passed
...92%, 0.66 MB, 3038 KB/s, 0 seconds passed
...93%, 0.67 MB, 3038 KB/s, 0 seconds passed
...94%, 0.68 MB, 3071 KB/s, 0 seconds passed
...95%, 0.69 MB, 3105 KB/s, 0 seconds passed
...96%, 0.70 MB, 3139 KB/s, 0 seconds passed
...97%, 0.70 MB, 3168 KB/s, 0 seconds passed
...99%, 0.71 MB, 3202 KB/s, 0 seconds passed
...100%, 0.72 MB, 3233 KB/s, 0 seconds passed
Extracted 10 conv2d tasks:
(1, 14, 14, 256, 512, 1, 1, 0, 0, 2, 2)
(1, 28, 28, 128, 256, 1, 1, 0, 0, 2, 2)
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index dca7412..c4e5551 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -243,8 +243,8 @@ The compilation steps are:
.. code-block:: none
-
...12%, 0.01 MB, 41 KB/s, 0 seconds passed
...25%, 0.02 MB, 82 KB/s, 0 seconds passed
...38%, 0.02 MB, 123 KB/s, 0 seconds passed
...51%, 0.03 MB, 164 KB/s, 0 seconds passed
...64%, 0.04 MB, 205 KB/s, 0 seconds passed
...77%, 0.05 MB, 245 KB/s, 0 seconds passed
...89%, 0.05 MB, 286 KB/s, 0 seconds passed
...100%, 0.06 MB, 326 KB/s, 0 seconds passed
- resnet18_v1 inference graph built in 3.97s!
+
...12%, 0.01 MB, 47 KB/s, 0 seconds passed
...25%, 0.02 MB, 94 KB/s, 0 seconds passed
...38%, 0.02 MB, 141 KB/s, 0 seconds passed
...51%, 0.03 MB, 188 KB/s, 0 seconds passed
...64%, 0.04 MB, 235 KB/s, 0 seconds passed
...77%, 0.05 MB, 281 KB/s, 0 seconds passed
...89%, 0.05 MB, 328 KB/s, 0 seconds passed
...100%, 0.06 MB, 374 KB/s, 0 seconds passed
+ resnet18_v1 inference graph built in 4.12s!
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
index e995f53..def0002 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -315,7 +315,7 @@ The compilation steps are:
.. code-block:: none
- yolov3-tiny inference graph built in 5.40s!
+ yolov3-tiny inference graph built in 5.07s!
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 f4d2a0f..a378d70 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:53.024** total execution time for **vta_tutorials_frontend** files:
+**00:52.876** total execution time for **vta_tutorials_frontend** files:
-- **00:31.870**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:21.153**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:31.536**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:21.340**: :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 565e62e..cd0584a 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -304,8 +304,8 @@ After we construct the schedule, by default the schedule computes
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
- C: Buffer(C_2: handle, int8, [1, 16, 1, 16], []),
+ buffers = {C: Buffer(C_2: handle, int8, [1, 16, 1, 16], []),
+ B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
A: Buffer(A_2: handle, int8, [1, 16, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [A_buf: handle] "storage_scope" = "global";
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index 808f397..ce0e246 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -257,8 +257,8 @@ Those include:
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
- kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], []),
+ buffers = {kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], []),
+ res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
data: Buffer(data_2: handle, int8, [1, 16, 14, 14, 1, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
attr [data_buf: handle] "storage_scope" = "global";
diff --git a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index 0510a2b..5d93faa 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -364,8 +364,8 @@ below:
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
- res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
+ buffers = {res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
+ weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
data: Buffer(data_2: handle, int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
attr [data_buf: handle] "storage_scope" = "global";
@@ -507,8 +507,8 @@ and mapping the shift, and clipping computation to the vector ALU.
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
- weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
+ buffers = {weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
+ res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
data: Buffer(data_2: handle, int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
attr [res_gem: handle] "storage_scope" = "local.acc_buffer";
diff --git a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
index 6f79a02..f7e3241 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
=================
-**00:02.952** total execution time for **vta_tutorials_optimize** files:
+**00:02.967** total execution time for **vta_tutorials_optimize** files:
-- **00:02.600**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.352**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.605**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.361**: :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 8a25e95..6efd822 100644
--- a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.647** total execution time for **vta_tutorials** files:
+**00:00.651** total execution time for **vta_tutorials** files:
-- **00:00.326**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.320**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.338**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.312**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/api/doxygen/analyzer_8h.html b/docs/api/doxygen/analyzer_8h.html
index 6314c53..42ada3b 100644
--- a/docs/api/doxygen/analyzer_8h.html
+++ b/docs/api/doxygen/analyzer_8h.html
@@ -111,7 +111,7 @@ Include dependency graph for analyzer.h:</div>
</div><div class="textblock"><div class="dynheader">
This graph shows which files directly or indirectly include this file:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="analyzer_8h__dep__incl.svg" width="4788" height="648"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="analyzer_8h__dep__incl.svg" width="4788" height="663"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
</div>
diff --git a/docs/api/doxygen/analyzer_8h__dep__incl.svg b/docs/api/doxygen/analyzer_8h__dep__incl.svg
index ce57914..b1d942b 100644
--- a/docs/api/doxygen/analyzer_8h__dep__incl.svg
+++ b/docs/api/doxygen/analyzer_8h__dep__incl.svg
@@ -4,929 +4,943 @@
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+<!-- Node19->Node21 -->
+<g id="edge31" class="edge"><title>Node19->Node21</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="2585.7,-206.03 2596.26,-206.876 2588.53,-199.628 2585.7,-206.03"/>
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+<!-- Node19->Node25 -->
+<g id="edge30" class="edge"><title>Node19->Node25</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="2586,-206.838 2596.6,-206.869 2588.33,-200.237 2586,-206.838"/>
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<!-- Node20->Node21 -->
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-<!-- Node20->Node22 -->
-<g id="edge27" class="edge"><title>Node20->Node22</title>
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-<!-- Node20->Node23 -->
-<g id="edge28" class="edge"><title>Node20->Node23</title>
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-<!-- Node25->Node17 -->
-<g id="edge33" class="edge"><title>Node25->Node17</title>
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-<!-- Node25->Node18 -->
-<g id="edge32" class="edge"><title>Node25->Node18</title>
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-<!-- Node25->Node20 -->
-<g id="edge38" class="edge"><title>Node25->Node20</title>
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-<!-- Node25->Node23 -->
-<g id="edge43" class="edge"><title>Node25->Node23</title>
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-<!-- Node25->Node26 -->
-<g id="edge34" class="edge"><title>Node25->Node26</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="3212.49,-325.396 3215.99,-335.396 3219.49,-325.396 3212.49,-325.396"/>
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-<!-- Node25->Node27 -->
-<g id="edge35" class="edge"><title>Node25->Node27</title>
-<path fill="none" stroke="midnightblue" d="M3144.91,-334.984C2928.83,-305.671 2869.77,-330.992 2657.99,-299 2654.88,-298.531 2651.7,-297.99 2648.5,-297.401"/>
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-<!-- Node25->Node28 -->
-<g id="edge36" class="edge"><title>Node25->Node28</title>
-<path fill="none" stroke="midnightblue" d="M3253.39,-330.803C3278.31,-318.342 3309.88,-302.556 3328.98,-293.007"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3251.58,-327.793 3244.2,-335.396 3254.71,-334.054 3251.58,-327.793"/>
-</g>
-<!-- Node25->Node29 -->
-<g id="edge39" class="edge"><title>Node25->Node29</title>
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-</g>
-<!-- Node28->Node21 -->
-<g id="edge37" class="edge"><title>Node28->Node21</title>
-<path fill="none" stroke="midnightblue" d="M3334.24,-265.415C3300.48,-216.351 3203.69,-75.6578 3172.68,-30.5784"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="3331.56,-267.696 3340.11,-273.951 3337.33,-263.729 3331.56,-267.696"/>
-</g>
-<!-- Node29->Node20 -->
-<g id="edge41" class="edge"><title>Node29->Node20</title>
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-<!-- Node29->Node23 -->
-<g id="edge42" class="edge"><title>Node29->Node23</title>
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-<!-- Node29->Node24 -->
-<g id="edge40" class="edge"><title>Node29->Node24</title>
-<path fill="none" stroke="midnightblue" d="M2346,-265.983C2366.59,-239.857 2406.31,-189.441 2425.74,-164.783"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2343.25,-263.818 2339.82,-273.839 2348.75,-268.15 2343.25,-263.818"/>
-</g>
-<!-- Node30->Node3 -->
-<g id="edge45" class="edge"><title>Node30->Node3</title>
-<path fill="none" stroke="midnightblue" d="M1669.36,-331.544C1694.48,-321.143 1725.66,-308.238 1748.99,-298.577"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1667.96,-328.337 1660.06,-335.396 1670.64,-334.804 1667.96,-328.337"/>
-</g>
-<!-- Node30->Node6 -->
-<g id="edge46" class="edge"><title>Node30->Node6</title>
-<path fill="none" stroke="midnightblue" d="M1697.23,-334.217C1748.04,-323.318 1815.78,-308.787 1864.89,-298.251"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1696.35,-330.827 1687.31,-336.346 1697.82,-337.671 1696.35,-330.827"/>
+<path fill="none" stroke="midnightblue" d="M2598.7,-134.269C2578.51,-121.403 2549.44,-102.879 2532.57,-92.1273"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2596.96,-137.311 2607.27,-139.734 2600.72,-131.408 2596.96,-137.311"/>
+</g>
+<!-- Node21->Node22 -->
+<g id="edge27" class="edge"><title>Node21->Node22</title>
+<path fill="none" stroke="midnightblue" d="M2603.8,-72.9444C2738.4,-59.3588 2997.22,-33.2357 3109.21,-21.9327"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2603.13,-69.4936 2593.53,-73.9802 2603.84,-76.4582 2603.13,-69.4936"/>
+</g>
+<!-- Node21->Node23 -->
+<g id="edge28" class="edge"><title>Node21->Node23</title>
+<path fill="none" stroke="midnightblue" d="M2546.77,-68.6206C2570.45,-57.6252 2604.45,-41.8413 2628.82,-30.5281"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2545.22,-65.4828 2537.62,-72.8685 2548.17,-71.8319 2545.22,-65.4828"/>
+</g>
+<!-- Node21->Node24 -->
+<g id="edge29" class="edge"><title>Node21->Node24</title>
+<path fill="none" stroke="midnightblue" d="M2496.74,-67.1526C2479.83,-56.2748 2456.73,-41.4029 2439.92,-30.589"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2495.11,-70.2652 2505.41,-72.7338 2498.89,-64.3788 2495.11,-70.2652"/>
+</g>
+<!-- Node26->Node18 -->
+<g id="edge34" class="edge"><title>Node26->Node18</title>
+<path fill="none" stroke="midnightblue" d="M3144.84,-334.92C2992.81,-311.846 2951.38,-321.084 2803.03,-299 2798.57,-298.336 2793.97,-297.593 2789.35,-296.803"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3144.6,-338.424 3155.01,-336.487 3145.66,-331.506 3144.6,-338.424"/>
+</g>
+<!-- Node26->Node19 -->
+<g id="edge33" class="edge"><title>Node26->Node19</title>
+<path fill="none" stroke="midnightblue" d="M3145.06,-334.984C3009.23,-314.747 2965.26,-347.334 2841.03,-299 2819.07,-290.457 2819.23,-278.275 2798.03,-268 2755.76,-247.514 2703.67,-233.903 2666.98,-226.027"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3144.59,-338.451 3155.01,-336.534 3145.66,-331.535 3144.59,-338.451"/>
+</g>
+<!-- Node26->Node21 -->
+<g id="edge39" class="edge"><title>Node26->Node21</title>
+<path fill="none" stroke="midnightblue" d="M3144.77,-334.936C3025.71,-316.731 2984.15,-349.135 2879.03,-299 2830.79,-275.992 2750.65,-160.15 2704.03,-134 2664.98,-112.091 2615.89,-99.2642 2578.1,-92.0233"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3144.56,-338.448 3155,-336.594 3145.68,-331.538 3144.56,-338.448"/>
+</g>
+<!-- Node26->Node24 -->
+<g id="edge44" class="edge"><title>Node26->Node24</title>
+<path fill="none" stroke="midnightblue" d="M3180.48,-330.631C3087.53,-281.857 2828.79,-149.654 2602.03,-67 2561.73,-52.309 2515.11,-39.5585 2478.89,-30.5446"/>
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+</g>
+<!-- Node26->Node27 -->
+<g id="edge35" class="edge"><title>Node26->Node27</title>
+<path fill="none" stroke="midnightblue" d="M3216.03,-325.108C3216.03,-316.154 3216.03,-306.323 3216.03,-298.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3212.53,-325.396 3216.03,-335.396 3219.53,-325.396 3212.53,-325.396"/>
+</g>
+<!-- Node26->Node28 -->
+<g id="edge36" class="edge"><title>Node26->Node28</title>
+<path fill="none" stroke="midnightblue" d="M3144.95,-334.984C2928.87,-305.671 2869.81,-330.992 2658.03,-299 2654.92,-298.531 2651.74,-297.99 2648.54,-297.401"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3144.64,-338.475 3155.03,-336.375 3145.6,-331.54 3144.64,-338.475"/>
+</g>
+<!-- Node26->Node29 -->
+<g id="edge37" class="edge"><title>Node26->Node29</title>
+<path fill="none" stroke="midnightblue" d="M3253.43,-330.803C3278.35,-318.342 3309.92,-302.556 3329.02,-293.007"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3251.62,-327.793 3244.24,-335.396 3254.75,-334.054 3251.62,-327.793"/>
+</g>
+<!-- Node26->Node30 -->
+<g id="edge40" class="edge"><title>Node26->Node30</title>
+<path fill="none" stroke="midnightblue" d="M3144.51,-334.926C2873.29,-299.61 2801.47,-316.714 2532.03,-299 2491.42,-296.33 2446.16,-293.056 2409.08,-290.295"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3144.29,-338.427 3154.66,-336.26 3145.2,-331.486 3144.29,-338.427"/>
+</g>
+<!-- Node29->Node22 -->
+<g id="edge38" class="edge"><title>Node29->Node22</title>
+<path fill="none" stroke="midnightblue" d="M3334.28,-265.415C3300.52,-216.351 3203.73,-75.6578 3172.72,-30.5784"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3331.6,-267.696 3340.15,-273.951 3337.36,-263.729 3331.6,-267.696"/>
+</g>
+<!-- Node30->Node21 -->
+<g id="edge42" class="edge"><title>Node30->Node21</title>
+<path fill="none" stroke="midnightblue" d="M2332.92,-263.514C2333.78,-232.392 2339.99,-170.355 2374.03,-134 2395.17,-111.428 2426.92,-98.9432 2455.33,-92.0385"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2329.42,-263.772 2332.81,-273.81 2336.42,-263.849 2329.42,-263.772"/>
+</g>
+<!-- Node30->Node24 -->
+<g id="edge43" class="edge"><title>Node30->Node24</title>
+<path fill="none" stroke="midnightblue" d="M2331.22,-263.869C2329.99,-247.285 2329.05,-222.409 2332.03,-201 2341.57,-132.393 2386.91,-60.6902 2407.65,-30.8479"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2327.74,-264.217 2332.08,-273.881 2334.71,-263.618 2327.74,-264.217"/>
+</g>
+<!-- Node30->Node25 -->
+<g id="edge41" class="edge"><title>Node30->Node25</title>
+<path fill="none" stroke="midnightblue" d="M2346.04,-265.983C2366.63,-239.857 2406.35,-189.441 2425.78,-164.783"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2343.29,-263.818 2339.85,-273.839 2348.79,-268.15 2343.29,-263.818"/>
+</g>
+<!-- Node31->Node4 -->
+<g id="edge46" class="edge"><title>Node31->Node4</title>
+<path fill="none" stroke="midnightblue" d="M1669.4,-331.544C1694.52,-321.143 1725.69,-308.238 1749.03,-298.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1668,-328.337 1660.1,-335.396 1670.68,-334.804 1668,-328.337"/>
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<!-- Node31->Node7 -->
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-<!-- Node31->Node8 -->
-<g id="edge58" class="edge"><title>Node31->Node8</title>
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-<!-- Node31->Node10 -->
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-<!-- Node31->Node11 -->
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-<!-- Node31->Node12 -->
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-<!-- Node31->Node13 -->
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-<!-- Node31->Node14 -->
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-<!-- Node31->Node32 -->
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diff --git a/docs/api/doxygen/analyzer_8h_source.html b/docs/api/doxygen/analyzer_8h_source.html
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<div class="title">analyzer.h</div> </div>
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+<a href="analyzer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * or more co [...]
<div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a035f9270597638a44a55dd2552f4ad20"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a035f9270597638a44a55dd2552f4ad20">tvm::arith::ConstIntBoundNode::kPosInf</a></div><div class="ttdeci">static const constexpr int64_t kPosInf</div><div class="ttdoc">Number to represent +inf. </div><div class="ttdef"><b>Definition:</b> analyzer.h:76</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a7187224ffaf688dd1aa0f6069b393f92"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a7187224ffaf688dd1aa0f6069b393f92">tvm::arith::ConstIntBoundNode::max_value</a></div><div class="ttdeci">int64_t max_value</div><div class="ttdef"><b>Definition:</b> analyzer.h:64</div></div>
<div class="ttc" id="classtvm_1_1arith_1_1ModularSetNode_html_a5b5bf53e26881ec83777f2a3c41a4031"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a5b5bf53e26881ec83777f2a3c41a4031">tvm::arith::ModularSetNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> analyzer.h:180</div></div>
diff --git a/docs/api/doxygen/annotated.html b/docs/api/doxygen/annotated.html
index 2b91c5f..c69c566 100644
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<tr id="row_2_0_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html" target="_self">IntConstraintsNode</a></td><td class="desc">Represent integer constrains including (integer) variables, their ranges and the relations between them (either equations or inequalities) </td></tr>
<tr id="row_2_0_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html" target="_self">IntConstraintsTransform</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html" title="We can have different set of variables to represent the same constraints. For ex [...]
<tr id="row_2_0_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html" target="_self">IntConstraintsTransformNode</a></td><td class="desc">We can have different set of variables to represent the same constraints. For example, the following two systems are equivalent, {a + b = 0 | a >= 0, b >= 0} and {m - n [...]
-<tr id="row_2_0_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSet.html" target="_self">IntSet</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" title="Base class of all Integer set containers. represent a set of integers in one dimension. ">IntSetNode</a> </td></tr>
-<tr id="row_2_0_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html" target="_self">IntSetAnalyzer</a></td><td class="desc"><a class="el" href="classtvm_1_1Integer.html" title="Container of constant int that adds more constructors. ">Integer</a> set analyzer </td></tr>
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-<tr id="row_2_0_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSet.html" target="_self">ModularSet</a></td><td class="desc">Reference of <a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" title="Range of a linear integer function. Use to do specify the possible index values. ">ModularSetNode</a> </td></tr>
-<tr id="row_2_0_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html" target="_self">ModularSetAnalyzer</a></td><td class="desc"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html" title="Analyzer that contains bunch of sub-analyzers. ">Analyzer</a> to get modular information over expression </td></tr>
-<tr id="row_2_0_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" target="_self">ModularSetNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> of a linear integer function. Use to do specify the possible index values </td></tr>
-<tr id="row_2_0_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1RewriteSimplifier.html" target="_self">RewriteSimplifier</a></td><td class="desc">Rewrite-rule based simplifier </td></tr>
+<tr id="row_2_0_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html" target="_self">IntGroupBounds</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusive), upper bounds (inclusi [...]
+<tr id="row_2_0_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" target="_self">IntGroupBoundsNode</a></td><td class="desc">Represent integer grouped bounds which are classified into lower bounds (inclusive), upper bounds (inclusive) and equalities. It also contains coefficient as a multiplier for the bounds, i.e., [...]
+<tr id="row_2_0_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSet.html" target="_self">IntSet</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" title="Base class of all Integer set containers. represent a set of integers in one dimension. ">IntSetNode</a> </td></tr>
+<tr id="row_2_0_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html" target="_self">IntSetAnalyzer</a></td><td class="desc"><a class="el" href="classtvm_1_1Integer.html" title="Container of constant int that adds more constructors. ">Integer</a> set analyzer </td></tr>
+<tr id="row_2_0_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" target="_self">IntSetNode</a></td><td class="desc">Base class of all <a class="el" href="classtvm_1_1Integer.html" title="Container of constant int that adds more constructors. ">Integer</a> set containers. represent a set of integers in one dimension </td></tr>
+<tr id="row_2_0_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSet.html" target="_self">ModularSet</a></td><td class="desc">Reference of <a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" title="Range of a linear integer function. Use to do specify the possible index values. ">ModularSetNode</a> </td></tr>
+<tr id="row_2_0_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html" target="_self">ModularSetAnalyzer</a></td><td class="desc"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html" title="Analyzer that contains bunch of sub-analyzers. ">Analyzer</a> to get modular information over expression </td></tr>
+<tr id="row_2_0_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" target="_self">ModularSetNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> of a linear integer function. Use to do specify the possible index values </td></tr>
+<tr id="row_2_0_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1RewriteSimplifier.html" target="_self">RewriteSimplifier</a></td><td class="desc">Rewrite-rule based simplifier </td></tr>
<tr id="row_2_1_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_2_1_" class="arrow" onclick="toggleFolder('2_1_')">►</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_2_1_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html" target="_self">AttrDocEntry</a></td><td class="desc"></td></tr>
<tr id="row_2_1_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html" target="_self">AttrDocVisitor</a></td><td class="desc"></td></tr>
@@ -686,8 +688,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr id="row_2_85_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
<tr id="row_2_86_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetId.html" target="_self">TargetId</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetIdNode.html" title="Target Id, specifies the kind of the target. ">TargetIdNode</a> </td></tr>
<tr id="row_2_87_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetIdAttrMap.html" target="_self">TargetIdAttrMap</a></td><td class="desc">Map<TargetId, ValueType> used to store meta-information about <a class="el" href="classtvm_1_1TargetId.html" title="Managed reference class to TargetIdNode. ">TargetId</a> </td></tr>
-<tr id="row_2_88_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_2_88_" class="arrow" onclick="toggleFolder('2_88_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetIdNode.html" target="_self">TargetIdNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a> Id, specifies the kind of the [...]
-<tr id="row_2_88_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1TargetIdNode_1_1ValueTypeInfo.html" target="_self">ValueTypeInfo</a></td><td class="desc">Stores the required type_key and type_index of a specific attr of a target </td></tr>
+<tr id="row_2_88_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetIdNode.html" target="_self">TargetIdNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a> Id, specifies the kind of the target </td></tr>
<tr id="row_2_89_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetIdRegEntry.html" target="_self">TargetIdRegEntry</a></td><td class="desc">Helper structure to register <a class="el" href="classtvm_1_1TargetId.html" title="Managed reference class to TargetIdNode. ">TargetId</a> </td></tr>
<tr id="row_2_90_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetNode.html" target="_self">TargetNode</a></td><td class="desc">Compilation target </td></tr>
<tr id="row_2_91_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> </td></tr>
@@ -715,9 +716,16 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr id="row_2_113_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> parameter in functions </td></tr>
<tr id="row_2_114_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
<tr id="row_2_115_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
-<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
-<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
-<tr id="row_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
+<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
+<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
+<tr id="row_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
+<tr id="row_6_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMGraphRuntimeGraphAttr.html" target="_self">TVMGraphRuntimeGraphAttr</a></td><td class="desc"></td></tr>
+<tr id="row_7_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMModule.html" target="_self">TVMModule</a></td><td class="desc">Module container of TVM </td></tr>
+<tr id="row_8_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMMutableFuncRegistry.html" target="_self">TVMMutableFuncRegistry</a></td><td class="desc">A <a class="el" href="structTVMFuncRegistry.html" title="A data structure that facilitates function lookup by C-string name. ">TVMFuncRegistry</a> that supports adding and changing the functions </td></tr>
+<tr id="row_9_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMOpParam.html" target="_self">TVMOpParam</a></td><td class="desc">Operator attributes about tvm op </td></tr>
+<tr id="row_10_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMPackedFunc.html" target="_self">TVMPackedFunc</a></td><td class="desc"></td></tr>
+<tr id="row_11_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
+<tr id="row_12_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
</table>
</div><!-- directory -->
</div><!-- contents -->
diff --git a/docs/api/doxygen/attr__registry__map_8h.html b/docs/api/doxygen/attr__registry__map_8h.html
index 03e0a23..31d694a 100644
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+<div class="ttc" id="classtvm_1_1OpRegEntry_html"><div class="ttname"><a href="classtvm_1_1OpRegEntry.html">tvm::OpRegEntry</a></div><div class="ttdoc">Helper structure to register operators. </div><div class="ttdef"><b>Definition:</b> op.h:215</div></div>
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diff --git a/docs/api/doxygen/builtin_8h_source.html b/docs/api/doxygen/builtin_8h_source.html
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-<div class="ttc" id="classtvm_1_1Op_html"><div class="ttname"><a href="classtvm_1_1Op.html">tvm::Op</a></div><div class="ttdoc">Managed reference class to OpNode. </div><div class="ttdef"><b>Definition:</b> op.h:164</div></div>
+<div class="ttc" id="classtvm_1_1Op_html"><div class="ttname"><a href="classtvm_1_1Op.html">tvm::Op</a></div><div class="ttdoc">Managed reference class to OpNode. </div><div class="ttdef"><b>Definition:</b> op.h:165</div></div>
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diff --git a/docs/api/doxygen/c__backend__api_8h.html b/docs/api/doxygen/c__backend__api_8h.html
index f13c2b8..8384aa6 100644
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+++ b/docs/api/doxygen/c__backend__api_8h.html
@@ -103,6 +103,12 @@ Include dependency graph for c_backend_api.h:</div>
<div class="center"><iframe scrolling="no" frameborder="0" src="c__backend__api_8h__incl.svg" width="324" height="216"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
+</div><div class="textblock"><div class="dynheader">
+This graph shows which files directly or indirectly include this file:</div>
+<div class="dyncontent">
+<div class="center"><iframe scrolling="no" frameborder="0" src="c__backend__api_8h__dep__incl.svg" width="219" height="410"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</div>
+</div>
</div>
<p><a href="c__backend__api_8h_source.html">Go to the source code of this file.</a></p>
<table class="memberdecls">
@@ -114,9 +120,9 @@ Classes</h2></td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
-<tr class="memitem:a79a43e3f37e8f77316e8dd16d0ee9b47"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__backend__api_8h.html#a79a43e3f37e8f77316e8dd16d0ee9b47">TVMBackendPackedCFunc</a>) (<a class="el" href="unionTVMValue.html">TVMValue</a> *args, int *type_codes, int num_args, <a class="el" href="unionTVMValue.html">TVMValue</a> *out_ret_value, int *out_ret_tcode)</td></tr>
-<tr class="memdesc:a79a43e3f37e8f77316e8dd16d0ee9b47"><td class="mdescLeft"> </td><td class="mdescRight">Signature for backend functions exported as DLL. <a href="#a79a43e3f37e8f77316e8dd16d0ee9b47">More...</a><br /></td></tr>
-<tr class="separator:a79a43e3f37e8f77316e8dd16d0ee9b47"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a58f5af221f14f8e68bd57f83643b3cac"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__backend__api_8h.html#a58f5af221f14f8e68bd57f83643b3cac">TVMBackendPackedCFunc</a>) (<a class="el" href="unionTVMValue.html">TVMValue</a> *args, int *type_codes, int num_args, <a class="el" href="unionTVMValue.html">TVMValue</a> *out_ret_value, int *out_ret_tcode, void *resource_handle)</td></tr>
+<tr class="memdesc:a58f5af221f14f8e68bd57f83643b3cac"><td class="mdescLeft"> </td><td class="mdescRight">Signature for backend functions exported as DLL. <a href="#a58f5af221f14f8e68bd57f83643b3cac">More...</a><br /></td></tr>
+<tr class="separator:a58f5af221f14f8e68bd57f83643b3cac"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac1b48128210969500e27aad08a7fe51b"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__backend__api_8h.html#ac1b48128210969500e27aad08a7fe51b">FTVMParallelLambda</a>) (int task_id, <a class="el" href="structTVMParallelGroupEnv.html">TVMParallelGroupEnv</a> *penv, void *cdata)</td></tr>
<tr class="memdesc:ac1b48128210969500e27aad08a7fe51b"><td class="mdescLeft"> </td><td class="mdescRight">The callback function to execute a parallel lambda. <a href="#ac1b48128210969500e27aad08a7fe51b">More...</a><br /></td></tr>
<tr class="separator:ac1b48128210969500e27aad08a7fe51b"><td class="memSeparator" colspan="2"> </td></tr>
@@ -171,12 +177,12 @@ Functions</h2></td></tr>
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-<a class="anchor" id="a79a43e3f37e8f77316e8dd16d0ee9b47"></a>
+<a class="anchor" id="a58f5af221f14f8e68bd57f83643b3cac"></a>
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+ <td class="memname">typedef int(* TVMBackendPackedCFunc) (<a class="el" href="unionTVMValue.html">TVMValue</a> *args, int *type_codes, int num_args, <a class="el" href="unionTVMValue.html">TVMValue</a> *out_ret_value, int *out_ret_tcode, void *resource_handle)</td>
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@@ -188,7 +194,8 @@ Functions</h2></td></tr>
<tr><td class="paramname">type_codes</td><td>The type codes of the arguments </td></tr>
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<tr><td class="paramname">out_ret_value</td><td>The output value of the the return value. </td></tr>
- <tr><td class="paramname">out_ret_tcode</td><td>The output type code of the return value.</td></tr>
+ <tr><td class="paramname">out_ret_tcode</td><td>The output type code of the return value. </td></tr>
+ <tr><td class="paramname">resource_handle</td><td>Pointer to associated resource.</td></tr>
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<div class="qindex"><a class="qindex" href="#letter_A">A</a> | <a class="qindex" href="#letter_B">B</a> | <a class="qindex" href="#letter_C">C</a> | <a class="qindex" href="#letter_D">D</a> | <a class="qindex" href="#letter_E">E</a> | <a class="qindex" href="#letter_F">F</a> | <a class="qindex" href="#letter_G">G</a> | <a class="qindex" href="#letter_H">H</a> | <a class="qindex" href="#letter_I">I</a> |& [...]
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-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html">ConvGemmWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html">ConvWinogradWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterC [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Add.html">Add</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespac [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AddNode.html">AddNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html">CropAndResizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>) &# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADT.html">ADT</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_D"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  D  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html">PatternTuple</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1StmtNode.html">StmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html">ADTObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html">PatternTupleNode</a> (<a class="el" href="namespacetvm_1_1relay [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AffineGridAttrs.html">AffineGridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducer.html">DataProducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> (<a class="el" href="namesp [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Allocate.html">Allocate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html">DataProducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html">IntSetNode</a> (<a class="el" href="namespacetvm_1_1arith.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AllocateNode.html">AllocateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DataType.html">DataType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModule.html">IRModule</a> (<a class="el" href="namespacetvm.html">tvm</a>)  [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocStorageAttrs.html">AllocStorageAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html">DataTypePattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a> (<a class="el" href="nam [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocTensorAttrs.html">AllocTensorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html">DataTypePatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html">IterAdapter</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPattern.html">AltPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html">DebugAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVar.html">IterVar</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm: [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPatternNode.html">AltPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html">DeformableConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html">IterVarAttr</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html">DenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttrNode.html">IterVarAttrNode</a> (<a class="el" href="namespacetvm_1_1te. [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1And.html">And</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DenseMapNode.html">DenseMapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVarNode.html">IterVarNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><t [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AndNode.html">AndNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html">DeviceAPI</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html">IterVarRelation</a> (<a class="el" href="namespacetvm_1_1te.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Any.html">Any</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html">DeviceCopyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelationNode.html">IterVarRelationNode</a> (<a class="el" href="namespacetvm_1_1t [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AnyNode.html">AnyNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPattern.html">DFPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_L"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  L  [...]
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html">PReluAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html">SubPixelAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArangeAttrs.html">ArangeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html">DFPatternCallback</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> (<a class="el" href="namespacetvm.html"> [...]
+</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html">ConvGemmWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternMutator.h [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html">ConvWinogradWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternNode.html">Patte [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode< [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html">CropAndResizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="name [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Add.html">Add</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_D"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  D  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html">StridedSliceAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AddNode.html">AddNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">PatternVarNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::re [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADT.html">ADT</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducer.html">DataProducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html">ADTObj</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html">DataProducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntSetNode.html">IntSetNode</a> (<a class="el" href="namespacetvm_1_1ari [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AffineGridAttrs.html">AffineGridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DataType.html">DataType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModule.html">IRModule</a> (<a class="el" href="namespacetvm.html">tvm< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Allocate.html">Allocate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html">DataTypePattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)& [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AllocateNode.html">AllocateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html">DataTypePatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1IterAdapter.html">IterAdapter</a> (<a class="el" href="nam [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocStorageAttrs.html">AllocStorageAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html">DebugAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVar.html">IterVar</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AllocTensorAttrs.html">AllocTensorAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html">DeformableConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html">IterVarAttr</a> (<a class [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPattern.html">AltPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html">DenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarAttrNode.html">IterVarAttrNode</a> (<a class="el" href="namespacetvm_1_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AltPatternNode.html">AltPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DenseMapNode.html">DenseMapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IterVarNode.html">IterVarNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArgsortAttrs.html">ArgsortAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html">DFPatternCallbackNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html">L2NormalizeAttrs</a> (<a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html">DFPatternFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html">LayerNormAttrs</a> (<a class="el" href="namesp [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html">SimpleObjAllocator::ArrayHandler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html">DFPatternFunctor< R(const DFPattern &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html">ArrayNode</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html">DFPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html">LayoutAxis</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html">AssertStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html">DFPatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html">LayoutNode</a> (<a class="el" href="namespacetvm_1_1tir [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html">AssertStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrs.html">DictAttrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html">LayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html">AttrDocEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrsNode.html">DictAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LE.html">LE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  &# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html">AttrDocVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html">DilateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html">LeakyReluAttrs</a> (<a class="el" href=" [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1AttrError.html">AttrError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html">Dilation2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1LENode.html">LENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) &# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrExistVisitor.html">AttrExistVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Div.html">Div</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfo.html">AttrFieldInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DivNode.html">DivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><t [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfoNode.html">AttrFieldInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html">DominatorPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetNode.html">LetNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm: [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html">AttrInitEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html">DominatorPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1LetNode.html">LetNode</a> (<a class="el" href= [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html">TensorComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html">AttrInitVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html">DropoutAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmt.html">LetStmt</a> (<a class="el" href="namespacetvm_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNonDefaultVisitor.html">AttrNonDefaultVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_E"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  E  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html">LetStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1qnn_1_1QuantizeAttrs.html">QuantizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay_1_1qnn.html">tvm::relay::qnn</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html">TensorIntrin</a> (<a class="el" href= [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrNopEntry.html">AttrNopEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Load.html">Load</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_R"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  R&# [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html">TensorIntrinCall</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNormalVisitor.html">AttrNormalVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFunc.html">EnvFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LoadNode.html">LoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html">AttrPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFuncNode.html">EnvFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html">LRNAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>) [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPatternNode.html">AttrPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQ.html">EQ</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LT.html">LT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  &# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMap.html">AttrRegistryMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQNode.html">EQNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LTNode.html">LTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Error.html">Error</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_M"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  M  </div> [...]
-</td><td valign="top"><a class="el" href="classtvm_1_1RangeNode.html">RangeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TensorTypeNode.html">TensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1ErrorBuilder.html">ErrorBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Rebase.html">Rebase</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ErrorReporter.html">ErrorReporter</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Map.html">Map</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Evaluate.html">Evaluate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MapNode.html">MapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSHashVisitor.html">AttrsSHashVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html">EvaluateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> (<a class="el" href="namespacetvm_1_1re [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html">AttrStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html">Executable</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MatchNode.html">MatchNode</a> (<a class="el" href="namespacetvm_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Max.html">Max</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm: [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MaxNode.html">MaxNode</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html">MaxPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html">MaxPool2DAttrs</a> (<a class="el" href="nam [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href=" [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html">DeviceAPI</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html">IterVarRelation</a> (<a class="el" href="namespacetvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1And.html">And</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html">DeviceCopyAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1IterVarRelationNode.html">IterVarRelationNode</a> (<a class="el" href="namespacetvm_1_1t [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AndNode.html">AndNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPattern.html">DFPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_L"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  L  [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1PrimExprNode.html">PrimExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Target.html">Target</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Any.html">Any</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html">DFPatternCallback</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html">PrimFunc</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir< [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AnyNode.html">AnyNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html">DFPatternCallbackNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html">L2NormalizeAttrs</a> (<a class="el" href=" [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArangeAttrs.html">ArangeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html">DFPatternFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html">LayerNormAttrs</a> (<a class="el" href= [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ArgsortAttrs.html">ArgsortAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html">DFPatternFunctor< R(const DFPattern &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a c [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html">DFPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html">LayoutAxis</a> (<a class="el" href="namespacetvm_1_1tir.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1ArrayHandler.html">SimpleObjAllocator::ArrayHandler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html">DFPatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LayoutNode.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html">ArrayNode</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrs.html">DictAttrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html">LayoutTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.htm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html">AssertStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1DictAttrsNode.html">DictAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LE.html">LE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html">AssertStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html">DilateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html">LeakyReluAttrs</a> (<a class="el" href="namespace [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocEntry.html">AttrDocEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html">Dilation2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1LENode.html">LENode</a> (<a class="el" href="namespacetvm_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrDocVisitor.html">AttrDocVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Div.html">Div</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1AttrError.html">AttrError</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1DivNode.html">DivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowsp [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html">TensorIntrin</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrExistVisitor.html">AttrExistVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html">DominatorPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetNode.html">LetNode</a> (<a class="el" href="name [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfo.html">AttrFieldInfo</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html">DominatorPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1LetNode.html">LetNode</a> (<a class="el" href="namespacetvm_1_1relay.html"> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrFieldInfoNode.html">AttrFieldInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html">DropoutAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmt.html">LetStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html">TensorIntrinNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrInitEntry.html">AttrInitEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_E"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  E  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html">LetStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1TensorNode.html">TensorNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrInitVisitor.html">AttrInitVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Load.html">Load</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Ramp.html">Ramp</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNonDefaultVisitor.html">AttrNonDefaultVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFunc.html">EnvFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LoadNode.html">LoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir< [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrNopEntry.html">AttrNopEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1EnvFuncNode.html">EnvFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html">LRNAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrNormalVisitor.html">AttrNormalVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQ.html">EQ</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LT.html">LT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) & [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html">AttrPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EQNode.html">EQNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1LTNode.html">LTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1AttrPatternNode.html">AttrPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Error.html">Error</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_M"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  M  </div [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1RebaseNode.html">RebaseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html">TransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMap.html">AttrRegistryMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1ErrorBuilder.html">ErrorBuilder</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrRegistryMapContainerMap.html">AttrRegistryMapContainerMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1ErrorReporter.html">ErrorReporter</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Map.html">Map</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td va [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Attrs.html">Attrs</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Evaluate.html">Evaluate</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MapNode.html">MapNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrsNode.html">AttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html">EvaluateNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   < [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSEqualVisitor.html">AttrsSEqualVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Executable.html">Executable</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MatchNode.html">MatchNode</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1detail_1_1AttrsSHashVisitor.html">AttrsSHashVisitor</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html">ExpandDimsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Max.html">Max</a> (<a class="el" href="namespacetv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html">AttrStmt</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html">ExprDeepEqual</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MaxNode.html">MaxNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)& [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html">AttrStmtNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html">MaxPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1rela [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1detail_1_1AttrTriggerNonDefaultEntry.html">AttrTriggerNonDefaultEntry</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html">ExprFunctor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html">MaxPool2DAttrs</ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const Expr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MaxPool3DA [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool1DAttrs.html">AvgPool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html">ExprFunctor< R(const PrimExpr &n, Args...)></a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool2DAttrs.html">AvgPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfoNode.html">MemoryInfoNode</a> (<a class="el" href="namespacetvm.html"> [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AvgPool3DAttrs.html">AvgPool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html">MeshgridAttrs</a> (<a class="el" href="names [...]
<tr><td rowspan="2" valign="bottom"><a name="letter_B"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  B  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1MemoryInfoNode.html">MemoryInfoNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefRead.html">RefRead</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html">ExprMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html">MeshgridAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html">RefReadNode</a> (<a class="el" href="namespacetvm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseAttrsNode.html">BaseAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html">ExprPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Min.html">Min</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)    [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html">BaseComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html">ExprPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MinNode.html">MinNode</a> (<a class="el" href="namespacetvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExpr.html">BaseExpr</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html">ExprRewriter</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html">MirrorPadAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExprNode.html">BaseExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html">MixedModeMutator</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::rel [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFunc.html">BaseFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html">MixedModeVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFuncNode.html">BaseFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOp.html">ExternOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mod.html">Mod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign= [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorType.html">BaseTensorType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html">ExternOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ModNode.html">ModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  &# [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorTypeNode.html">BaseTensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_F"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  F  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1RelayExprNode.html">RelayExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1TVMPODValue__.html">TVMPODValue_</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm: [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueEqual.html">BaseValueEqual</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RelayNode.html">RelayNode</a> (<a class="el" href="namespacetvm_1_1relay.html [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueHash.html">BaseValueHash</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html">FeatureSet</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::a [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html">BatchNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html">FIFOBufferAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">Module</a> (<a class="el" href="namespace [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html">BiasAddAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html">SeqStmt::Flattener</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a> (<a class="el" href="name [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html">BijectiveLayout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><t [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html">BijectiveLayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImmNode.html">FloatImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)& [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html">BinaryConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html">FloorDiv</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">MultiBoxPriorAttrs</a> (<a class="el" href="n [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html">BinaryDenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html">FloorDivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBoxTransformLocAttrs</a> (< [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html">BinaryOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorMod.html">FloorMod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_N"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  N [...]
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html">ResizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeDataNode.html">TypeDataNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html">BitPackAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html">ReverseAttrs</a> (<a class="el" href="namespacetvm_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Bool.html">Bool</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="to [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Broadcast.html">Broadcast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html">NdarraySizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tv [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html">BroadcastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html">StringObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NE.html">NE</a> (<a class="el" href="namespacetvm_1_1t [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Function.html">Function</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NENode.html">NENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html">BufferLoad</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html">FunctionNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor.html">NodeFunctor</a> (<a class="el" href="namespacetvm.html">tvm</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncType.html">FuncType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html">NodeFunctor< R(const ObjectRef &n, [...]
-</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html">TypeIndex</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncTypeNode.html">FuncTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html">NonMaximumSuppressionAttrs</a> (<a class="el" href="namespacetvm_1_1r [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Fuse.html">Fuse</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Not.html">Not</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </t [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NotNode.html">NotNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_G"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  G  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html">ScatterAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01DataType_01_4.html">TypeName< DataType > [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_O"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  O  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01double_01_4.html">TypeName< double ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td></tr>
+</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html">ExprPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Min.html">Min</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefWrite.html">RefWrite</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)& [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html">ExprPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MinNode.html">MinNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html">RefWriteNode</a> (<a class="el" href="namespacetvm_1_1relay [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseAttrsNode.html">BaseAttrsNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html">ExprRewriter</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html">MirrorPadAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">t [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html">BaseComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html">MixedModeMutator</a> (<a class="el" href="namespacet [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExpr.html">BaseExpr</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html">ExprVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html">MixedModeVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseExprNode.html">BaseExprNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOp.html">ExternOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mod.html">Mod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign= [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFunc.html">BaseFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html">ExternOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ModNode.html">ModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseFuncNode.html">BaseFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_F"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  F  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1RelayRefType.html">RelayRefType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structTVMOpParam.html">TVMOpParam</a>   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorType.html">BaseTensorType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1RelayRefTypeNode.html">RelayRefTypeNode</a> (<a class="el" href="namespacetvm.html">tv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseTensorTypeNode.html">BaseTensorTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html">FeatureSet</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> (<a class="el" href="namespacetvm_1_1arith.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueEqual.html">BaseValueEqual</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html">FIFOBufferAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Module.html">Module</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm:: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1BaseValueHash.html">BaseValueHash</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html">SeqStmt::Flattener</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a> (<a class="el" href="namespacetvm_1_1runtime.html [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BatchNormAttrs.html">BatchNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </ [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html">BiasAddAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FloatImmNode.html">FloatImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) &# [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html">BijectiveLayout</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html">FloorDiv</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">MultiBoxPriorAttrs</a> (<a class="el" href="namespacetvm [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html">BijectiveLayoutNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html">FloorDivNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBoxTransformLocAttrs</a> (<a [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html">BinaryConv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorMod.html">FloorMod</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_N"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah"> [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ReverseIterAdapter.html">ReverseIterAdapter</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeConstraint.html">TypeConstraint</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html">BinaryDenseAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ReverseSequenceAttrs.html">ReverseSequenceAttrs</a> (<a class="e [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html">BinaryOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray.html">NDArray</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)&# [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html">BitPackAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html">NdarraySizeAttrs</a> (<a class="el" href="namespacetvm_1_1r [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Bool.html">Bool</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1StringObj_1_1FromStd.html">StringObj::FromStd</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NE.html">NE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Broadcast.html">Broadcast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Function.html">Function</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NENode.html">NENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html">TypedEnvFunc< R(Args...)></a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html">BroadcastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html">FunctionNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor.html">NodeFunctor</a> (<a class="el" href="namespacetvm.html">tvm</a> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Buffer.html">Buffer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncType.html">FuncType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html">NodeFunctor< R(const ObjectRef &n, Args...)></a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html">BufferLoad</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1FuncTypeNode.html">FuncTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html">NonMaximumSuppressionAttrs</a> (<a class="el" href="namespacetvm_1_1r [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html">BufferLoadNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Fuse.html">Fuse</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Not.html">Not</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   < [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferNode.html">BufferNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1FuseNode.html">FuseNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1NotNode.html">NotNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_G"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  G  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ScheduleNode.html">ScheduleNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeMutator.html">TypeMutator</a> (<a class="el" href="namespacetvm.html">tvm</a [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_O"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  O  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Select.html">Select</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1TypeName.html">TypeName</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SelectNode.html">SelectNode</a> (<a class="el" href="namespacetvm_1_1tir.html"> [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" href="namespacetvm_1_1runtime.htm [...]
<tr><td rowspan="2" valign="bottom"><a name="letter_C"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  C  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1ScheduleNode.html">ScheduleNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1TypeName_3_01int_01_4.html">TypeName< int ></a> (<a class="el" href=" [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Select.html">Select</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespac [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html">GlobalPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class="el" h [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVar.html">GlobalTypeVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a> (<a class="el" href="namespacetvm_1_1runtime [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> (<a class="el" href="namespacetvm_1_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Cast.html">Cast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html">ObjectTypeChecker</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>) [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVarNode.html">GlobalVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker< Array< T > > [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html"> [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CastNode.html">CastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namespacetvm_1_1r [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GT.html">GT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>) & [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html">ClauseNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Op.html">Op</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign=" [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectSEqualReduce_3_01T_00_01TraitName_00_01false_01_4.html">SelectSEqualReduce< T, TraitName, [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1SelectSHashReduce.html">SelectSHashReduce</a> (<a class="el" href="namespacetvm_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)  [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespacetvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html">GlobalPool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class="el" href="namespa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVar.html">GlobalTypeVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html">ObjectPtrHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::ru [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">ObjectRef</a> (<a class="el" href="namespacetvm_1_1runtime.html">tv [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html">ObjectTypeChecker</a> (<a class="el" href="namespacetvm_1_1runtime [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GlobalVarNode.html">GlobalVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker< Ar [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Cast.html">Cast</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html">ObjectTypeChecker< Map [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namespac [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GT.html">GT</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::re [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CastNode.html">CastNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Op.html">Op</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a cl [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_H"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  H  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html">ShapePatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1TypeVarNode.html">TypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)    [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html">ClauseNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Operation.html">Operation</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer.html">SHashReducer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html">ClipAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html">SEqualReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1OperationNode.html">OperationNode</a> (<a class="el" href="namespacetvm_1_1te.h [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html">ClipAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_H"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  H  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html">ShapeOfAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Closure.html">Closure</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Operation.html">Operation</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html">ShapePattern</a> (<a class="el" href="namespacetvm_1 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1ClosureObj.html">ClosureObj</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SEqualReducer_1_1Handler.html">SEqualReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1OperationNode.html">OperationNode</a> (<a class="el" hre [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html">OpImplementation</a> (<a class="el" href="namespacetvm_1_1rela [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Closure.html">Closure</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html">OpImplementation</a> (<a class="el" hr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1ClosureObj.html">ClosureObj</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html">SimpleObjAllocator::Handler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementa [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLContext_01_4.html">Handler< DLContext ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a class="el" hre [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDataType_01_4.html">Handler< DLDataType ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEntry</a> (<a [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1SimpleObjAllocator_1_1Handler.html">SimpleObjAllocator::Handler</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html">OpImplement [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html">CommReducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLContext_01_4.html">Handler< DLContext ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a cl [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLDataType_01_4.html">Handler< DLDataType ></a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEnt [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOp.html">HybridOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html">OpSpecialization</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::re [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html">HybridOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html">OpSpecializationNode</a> (<a class="el" href="namespacetv [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html">ConcatenateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_I"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  I  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html">OpStrategy</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SizeVar.html">SizeVar</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)  [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html">OpStrategyNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1SizeVarNode.html">SizeVarNode</a> (<a class="el" href="namespacetvm_1_1tir [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html">ConstantNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Id.html">Id</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::ru [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html">ConstantPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IdNode.html">IdNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Or.html">Or</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>) [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html">ConstantPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1If.html">If</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1OrNode.html">OrNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm:: [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html">ConstIntBound</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfNode.html">IfNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_P"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah"> & [...]
-</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html">SoftmaxAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VirtualMachine.html">VirtualMachine</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html">IfThenElse</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1SourceName.html">SourceName</a> (<a class="el" href="namespacetvm.htm [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html">IfThenElseNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">PackedFunc</a> (<a class="el" href="names [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce.html">ImplSEqualReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html">PackedFuncVa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce_3_01T_00_01true_01_4.html">ImplSEqualReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01Optional_3_01 [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce.html">ImplSHashReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.html">PackedFuncValueConverter& [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html">CommReducerNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOp.html">HybridOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html">OpSpecialization</a> (<a class="el" href="namespacetvm_1_1rela [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html">CompilerAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html">HybridOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html">OpSpecializationNode</a> (<a class="el" href="n [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_I"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  I  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html">OpStrategy</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html">Tensor::Slice</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ValueTypeInfoMaker.html">ValueTypeInfoMaker</a> (<a class="el" href="names [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html">OpStrategyNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html">SliceLikeAttrs</a> (<a class="el" href="namespace [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html">ConcatenateAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Id.html">Id</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a> (<a class="el" href="namespacetvm_1_1runtime.html [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IdNode.html">IdNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Or.html">Or</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  &# [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html">ConstantNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1If.html">If</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1OrNode.html">OrNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)  [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html">ConstantPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1IfNode.html">IfNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_P"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah"> [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1SourceNameNode.html">SourceNameNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1VarPattern.html">VarPattern</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html">ConstantPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html">IfThenElse</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Span.html">Span</a> (<a class="el" href="namespacetvm.html">tvm</a>)  [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html">ConstIntBound</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html">IfThenElseNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html">PackedFunc</a> (<a class="el" href="namespacetvm_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce.html">ImplSEqualReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter.html">Pack [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSEqualReduce_3_01T_00_01true_01_4.html">ImplSEqualReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1Packe [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce.html">ImplSHashReduce</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01PrimExpr_01_4.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Bool_01_ [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Integer_01_4.html">PackedFuncValueConver [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplSHashReduce_3_01T_00_01true_01_4.html">ImplSHashReduce< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFun [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs.html">ImplVisitAttrs</a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncValueConverter_3_01tvm_1_1Intege [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html">PadAttrs</a> (<a class="el" href="namespacetv [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pass.html">Pass</a> (<a class="el" href="namespacetvm_1_1transform.html">tvm:: [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1detail_1_1ImplVisitAttrs_3_01T_00_01true_01_4.html">ImplVisitAttrs< T, true ></a> (<a class="el" href="namespacetvm_1_1detail.html">tvm::detail</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1PackedFuncV [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html">PadAttrs</a> (<a class="el" href="namespacetvm_1_1relay.ht [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pass.html">Pass</a> (<a class="el" href="namespacetvm_1_1 [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a class="el" [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html">Conv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a clas [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a clas [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html">Conv2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoNode</a> (<a clas [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassNode.html">PassNode</a> (<a class="el" [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html">Conv2DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoN [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html">Conv2DWinogradNNPACKWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1Pas [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html">Conv3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html">IntConstraintsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Pattern.html">Pattern</a> (<a class="el" href="namespacet [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html">Conv2DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html">IntConstraintsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Pattern.html">Pattern</a> (<a class="el" [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html">Conv2DWinogradNNPACKWeightTransformAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html">IntConstraintsTransform</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1 [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html">Conv3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html">PatternCon [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html">Conv3DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html">PatternFunctor</a> (<a class="el" href="namespacetvm_1_1relay.htm [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html">Conv3DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01 [...]
<tr><td></td><td></td><td></td><td></td><td></td></tr>
</table>
<div class="qindex"><a class="qindex" href="#letter_A">A</a> | <a class="qindex" href="#letter_B">B</a> | <a class="qindex" href="#letter_C">C</a> | <a class="qindex" href="#letter_D">D</a> | <a class="qindex" href="#letter_E">E</a> | <a class="qindex" href="#letter_F">F</a> | <a class="qindex" href="#letter_G">G</a> | <a class="qindex" href="#letter_H">H</a> | <a class="qindex" href="#letter_I">I</a> |& [...]
diff --git a/docs/api/doxygen/classtvm_1_1SpanNode-members.html b/docs/api/doxygen/classtvm_1_1SpanNode-members.html
index 106304f..ac30e31 100644
--- a/docs/api/doxygen/classtvm_1_1SpanNode-members.html
+++ b/docs/api/doxygen/classtvm_1_1SpanNode-members.html
@@ -95,8 +95,8 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<p>This is the complete list of members for <a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a>, including all inherited members.</p>
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#a3ce9fb38ce356931682dd0ff6e7e511f">_type_key</a></td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#a78d2dc28f35d2625b95697de63a16264">col_offset</a></td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#ad605e038c640219d8372d83ae68f2173">lineno</a></td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#a51f1f2feef5505e4686c1906b84a4bb1">column</a></td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#a6890bb225890f64611e6f77a401ee617">line</a></td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#a303175c2e781ef104cd03e26010200b8">SEqualReduce</a>(const SpanNode *other, SEqualReducer equal) const </td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#afde422b06b0b110c002b543770360f84">source</a></td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html#a18bcfd7f4d291e5973c510fddd53cb88">TVM_DECLARE_FINAL_OBJECT_INFO</a>(SpanNode, Object)</td><td class="entry"><a class="el" href="classtvm_1_1SpanNode.html">tvm::SpanNode</a></td><td class="entry"></td></tr>
diff --git a/docs/api/doxygen/classtvm_1_1SpanNode.html b/docs/api/doxygen/classtvm_1_1SpanNode.html
index 7905ab6..e2deab8 100644
--- a/docs/api/doxygen/classtvm_1_1SpanNode.html
+++ b/docs/api/doxygen/classtvm_1_1SpanNode.html
@@ -128,12 +128,12 @@ Public Attributes</h2></td></tr>
<tr class="memitem:afde422b06b0b110c002b543770360f84"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1SourceName.html">SourceName</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1SpanNode.html#afde422b06b0b110c002b543770360f84">source</a></td></tr>
<tr class="memdesc:afde422b06b0b110c002b543770360f84"><td class="mdescLeft"> </td><td class="mdescRight">The source name. <a href="#afde422b06b0b110c002b543770360f84">More...</a><br /></td></tr>
<tr class="separator:afde422b06b0b110c002b543770360f84"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad605e038c640219d8372d83ae68f2173"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1SpanNode.html#ad605e038c640219d8372d83ae68f2173">lineno</a></td></tr>
-<tr class="memdesc:ad605e038c640219d8372d83ae68f2173"><td class="mdescLeft"> </td><td class="mdescRight">Line number. <a href="#ad605e038c640219d8372d83ae68f2173">More...</a><br /></td></tr>
-<tr class="separator:ad605e038c640219d8372d83ae68f2173"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a78d2dc28f35d2625b95697de63a16264"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1SpanNode.html#a78d2dc28f35d2625b95697de63a16264">col_offset</a></td></tr>
-<tr class="memdesc:a78d2dc28f35d2625b95697de63a16264"><td class="mdescLeft"> </td><td class="mdescRight">column offset <a href="#a78d2dc28f35d2625b95697de63a16264">More...</a><br /></td></tr>
-<tr class="separator:a78d2dc28f35d2625b95697de63a16264"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6890bb225890f64611e6f77a401ee617"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1SpanNode.html#a6890bb225890f64611e6f77a401ee617">line</a></td></tr>
+<tr class="memdesc:a6890bb225890f64611e6f77a401ee617"><td class="mdescLeft"> </td><td class="mdescRight">The line number. <a href="#a6890bb225890f64611e6f77a401ee617">More...</a><br /></td></tr>
+<tr class="separator:a6890bb225890f64611e6f77a401ee617"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a51f1f2feef5505e4686c1906b84a4bb1"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1SpanNode.html#a51f1f2feef5505e4686c1906b84a4bb1">column</a></td></tr>
+<tr class="memdesc:a51f1f2feef5505e4686c1906b84a4bb1"><td class="mdescLeft"> </td><td class="mdescRight">The column offset. <a href="#a51f1f2feef5505e4686c1906b84a4bb1">More...</a><br /></td></tr>
+<tr class="separator:a51f1f2feef5505e4686c1906b84a4bb1"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-attribs"></a>
Static Public Attributes</h2></td></tr>
@@ -248,31 +248,31 @@ Static Public Attributes</h2></td></tr>
</div>
</div>
-<a class="anchor" id="a78d2dc28f35d2625b95697de63a16264"></a>
+<a class="anchor" id="a51f1f2feef5505e4686c1906b84a4bb1"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">int tvm::SpanNode::col_offset</td>
+ <td class="memname">int tvm::SpanNode::column</td>
</tr>
</table>
</div><div class="memdoc">
-<p>column offset </p>
+<p>The column offset. </p>
</div>
</div>
-<a class="anchor" id="ad605e038c640219d8372d83ae68f2173"></a>
+<a class="anchor" id="a6890bb225890f64611e6f77a401ee617"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">int tvm::SpanNode::lineno</td>
+ <td class="memname">int tvm::SpanNode::line</td>
</tr>
</table>
</div><div class="memdoc">
-<p>Line number. </p>
+<p>The line number. </p>
</div>
</div>
diff --git a/docs/api/doxygen/classtvm_1_1SpanNode__coll__graph.svg b/docs/api/doxygen/classtvm_1_1SpanNode__coll__graph.svg
index 1764e2a..8635394 100644
--- a/docs/api/doxygen/classtvm_1_1SpanNode__coll__graph.svg
+++ b/docs/api/doxygen/classtvm_1_1SpanNode__coll__graph.svg
@@ -14,8 +14,8 @@
<polygon fill="#bfbfbf" stroke="black" points="-1.42109e-14,-0.5 -1.42109e-14,-101.5 205,-101.5 205,-0.5 -1.42109e-14,-0.5"/>
<text text-anchor="middle" x="102.5" y="-89.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::SpanNode</text>
<polyline fill="none" stroke="black" points="-1.42109e-14,-82.5 205,-82.5 "/>
-<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ lineno</text>
-<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ col_offset</text>
+<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ line</text>
+<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ column</text>
<text text-anchor="start" x="8" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
<polyline fill="none" stroke="black" points="-1.42109e-14,-41.5 205,-41.5 "/>
<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
diff --git a/docs/api/doxygen/classtvm_1_1SpanNode__inherit__graph.svg b/docs/api/doxygen/classtvm_1_1SpanNode__inherit__graph.svg
index bd8fa32..7ebbe48 100644
--- a/docs/api/doxygen/classtvm_1_1SpanNode__inherit__graph.svg
+++ b/docs/api/doxygen/classtvm_1_1SpanNode__inherit__graph.svg
@@ -15,8 +15,8 @@
<text text-anchor="middle" x="102.5" y="-100.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::SpanNode</text>
<polyline fill="none" stroke="black" points="-1.42109e-14,-93.5 205,-93.5 "/>
<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ source</text>
-<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ lineno</text>
-<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ col_offset</text>
+<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ line</text>
+<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ column</text>
<text text-anchor="start" x="8" y="-48.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
<polyline fill="none" stroke="black" points="-1.42109e-14,-41.5 205,-41.5 "/>
<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
diff --git a/docs/api/doxygen/classtvm_1_1TargetIdNode-members.html b/docs/api/doxygen/classtvm_1_1TargetIdNode-members.html
index 6aa7008..de78b52 100644
--- a/docs/api/doxygen/classtvm_1_1TargetIdNode-members.html
+++ b/docs/api/doxygen/classtvm_1_1TargetIdNode-members.html
@@ -101,13 +101,14 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a9ce830f20c377093d7812ffc2eb5c628">detail::ValueTypeInfoMaker</a></td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a15a2875b9d8a8abecbc61b0346a138d2">device_type</a></td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#ac198dd4e56b0c1e3fb5f39f7d0efcdc4">name</a></td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#abb65fc11f1f6ae963db5375842721131">ParseAttrsFromRawString</a>(const std::vector< std::string > &options)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#aaec4eb5c2964764409c3ecc82bb90f57">Target</a> class</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a55df0ea036a739549b850ef868a7e1e7">TargetId</a> class</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a8c87f8c16dd388d8774d189ada51e86f">TargetIdRegEntry</a> class</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a342d381f69da21b03762bfc84b566a7a">TargetValidateSchema</a>(const Map< String, ObjectRef > &)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a8003d72c2e4d074bdb306f043d39b26e">TVM_DECLARE_FINAL_OBJECT_INFO</a>(TargetIdNode, Object)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a4ffbdcb3892132d766cf76382773a782">VisitAttrs</a>(AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a7fb0db6777bf53846510a9a676414a68">ParseAttrsFromRaw</a>(const std::vector< std::string > &options) const </td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a6f02435243d36d37210ab1b2d6cf9b99">StringifyAttrsToRaw</a>(const Map< String, ObjectRef > &attrs) const </td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#aaec4eb5c2964764409c3ecc82bb90f57">Target</a> class</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a55df0ea036a739549b850ef868a7e1e7">TargetId</a> class</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a8c87f8c16dd388d8774d189ada51e86f">TargetIdRegEntry</a> class</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a342d381f69da21b03762bfc84b566a7a">TargetValidateSchema</a>(const Map< String, ObjectRef > &)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a8003d72c2e4d074bdb306f043d39b26e">TVM_DECLARE_FINAL_OBJECT_INFO</a>(TargetIdNode, Object)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html#a4ffbdcb3892132d766cf76382773a782">VisitAttrs</a>(AttrVisitor *v)</td><td class="entry"><a class="el" href="classtvm_1_1TargetIdNode.html">tvm::TargetIdNode</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/api/doxygen/classtvm_1_1TargetIdNode.html b/docs/api/doxygen/classtvm_1_1TargetIdNode.html
index cf66657..bc04a7f 100644
--- a/docs/api/doxygen/classtvm_1_1TargetIdNode.html
+++ b/docs/api/doxygen/classtvm_1_1TargetIdNode.html
@@ -106,28 +106,24 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div class="dynheader">
Inheritance diagram for tvm::TargetIdNode:</div>
<div class="dyncontent">
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+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1TargetIdNode__inherit__graph.svg" width="284" height="302"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<div class="dynheader">
Collaboration diagram for tvm::TargetIdNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1TargetIdNode__coll__graph.svg" width="496" height="1170"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1TargetIdNode__coll__graph.svg" width="496" height="1184"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<table class="memberdecls">
-<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
-Classes</h2></td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structtvm_1_1TargetIdNode_1_1ValueTypeInfo.html">ValueTypeInfo</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Stores the required type_key and type_index of a specific attr of a target. <a href="structtvm_1_1TargetIdNode_1_1ValueTypeInfo.html#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a4ffbdcb3892132d766cf76382773a782"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1TargetIdNode.html#a4ffbdcb3892132d766cf76382773a782">VisitAttrs</a> (<a class="el" href="classtvm_1_1AttrVisitor.html">AttrVisitor</a> *v)</td></tr>
<tr class="separator:a4ffbdcb3892132d766cf76382773a782"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="separator:abb65fc11f1f6ae963db5375842721131"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7fb0db6777bf53846510a9a676414a68"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, ObjectRef > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1TargetIdNode.html#a7fb0db6777bf53846510a9a676414a68">ParseAttrsFromRaw</a> (const std::vector< std::string > &options) const </td></tr>
+<tr class="separator:a7fb0db6777bf53846510a9a676414a68"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6f02435243d36d37210ab1b2d6cf9b99"><td class="memItemLeft" align="right" valign="top">Optional< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1TargetIdNode.html#a6f02435243d36d37210ab1b2d6cf9b99">StringifyAttrsToRaw</a> (const <a class="el" href="classtvm_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, ObjectRef > [...]
+<tr class="separator:a6f02435243d36d37210ab1b2d6cf9b99"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="separator:a8003d72c2e4d074bdb306f043d39b26e"><td class="memSeparator" colspan="2"> </td></tr>
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@@ -172,16 +168,32 @@ Friends</h2></td></tr>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classtvm_1_1Target.html" title="Managed reference class to TargetNode. ">Target</a> Id, specifies the kind of the target. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
-<a class="anchor" id="abb65fc11f1f6ae963db5375842721131"></a>
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+ <td class="memname"><a class="el" href="classtvm_1_1Map.html">Map</a><<a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, ObjectRef> tvm::TargetIdNode::ParseAttrsFromRaw </td>
<td>(</td>
<td class="paramtype">const std::vector< std::string > & </td>
<td class="paramname"><em>options</em></td><td>)</td>
- <td></td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
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+<a class="anchor" id="a6f02435243d36d37210ab1b2d6cf9b99"></a>
+<div class="memitem">
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+ <tr>
+ <td class="memname">Optional<<a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>> tvm::TargetIdNode::StringifyAttrsToRaw </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1runtime_1_1String.html">String</a>, ObjectRef > & </td>
+ <td class="paramname"><em>attrs</em></td><td>)</td>
+ <td> const</td>
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+<text text-anchor="start" x="207.5" y="-178.5" font-family="Helvetica,sans-Serif" font-size="10.00">_OBJECT_REF_METHODS()</text>
+<text text-anchor="start" x="207.5" y="-167.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CanConvertFrom()</text>
+<text text-anchor="start" x="207.5" y="-156.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ HashBytes()</text>
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+<text text-anchor="start" x="224.5" y="-603.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectRef()</text>
<text text-anchor="start" x="224.5" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectRef()</text>
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+<text text-anchor="start" x="224.5" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ same_as()</text>
+<text text-anchor="start" x="224.5" y="-570.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator==()</text>
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+<text text-anchor="start" x="224.5" y="-537.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ defined()</text>
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+<text text-anchor="start" x="224.5" y="-515.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator->()</text>
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+<text text-anchor="start" x="224.5" y="-493.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ use_count()</text>
+<text text-anchor="start" x="224.5" y="-482.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ as()</text>
+<text text-anchor="start" x="224.5" y="-471.5" font-family="Helvetica,sans-Serif" font-size="10.00"># get_mutable()</text>
+<text text-anchor="start" x="224.5" y="-460.5" font-family="Helvetica,sans-Serif" font-size="10.00"># DowncastNoCheck()</text>
+<text text-anchor="start" x="224.5" y="-449.5" font-family="Helvetica,sans-Serif" font-size="10.00"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="224.5" y="-438.5" font-family="Helvetica,sans-Serif" font-size="10.00"># GetDataPtr()</text>
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<g id="node6" class="node"><title>Node6</title>
<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 10 more...\l}">
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+<text text-anchor="start" x="220.5" y="-867.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::runtime::ObjectPtr</text>
+<text text-anchor="middle" x="281.5" y="-856.5" font-family="Helvetica,sans-Serif" font-size="10.00">< tvm::runtime::Object ></text>
+<polyline fill="none" stroke="black" points="212.5,-849.5 350.5,-849.5 "/>
+<text text-anchor="middle" x="281.5" y="-837.5" font-family="Helvetica,sans-Serif" font-size="10.00"> </text>
+<polyline fill="none" stroke="black" points="212.5,-830.5 350.5,-830.5 "/>
+<text text-anchor="start" x="220.5" y="-818.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
<text text-anchor="start" x="220.5" y="-807.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
<text text-anchor="start" x="220.5" y="-796.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
<text text-anchor="start" x="220.5" y="-785.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
<text text-anchor="start" x="220.5" y="-774.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
<text text-anchor="start" x="220.5" y="-763.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
-<text text-anchor="start" x="220.5" y="-752.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ObjectPtr()</text>
-<text text-anchor="start" x="220.5" y="-741.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ~ObjectPtr()</text>
-<text text-anchor="start" x="220.5" y="-730.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ swap()</text>
-<text text-anchor="start" x="220.5" y="-719.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ get()</text>
-<text text-anchor="start" x="220.5" y="-708.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator->()</text>
-<text text-anchor="start" x="220.5" y="-697.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 10 more...</text>
+<text text-anchor="start" x="220.5" y="-752.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ~ObjectPtr()</text>
+<text text-anchor="start" x="220.5" y="-741.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ swap()</text>
+<text text-anchor="start" x="220.5" y="-730.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ get()</text>
+<text text-anchor="start" x="220.5" y="-719.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ operator->()</text>
+<text text-anchor="start" x="220.5" y="-708.5" font-family="Helvetica,sans-Serif" font-size="10.00">and 10 more...</text>
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-<text text-anchor="middle" x="301" y="-664" font-family="Helvetica,sans-Serif" font-size="10.00"> #data_</text>
+<path fill="none" stroke="#404040" d="M281.5,-701.383C281.5,-689.802 281.5,-677.772 281.5,-665.751"/>
+<polygon fill="none" stroke="#404040" points="281.5,-665.75 277.5,-659.75 281.5,-653.75 285.5,-659.75 281.5,-665.75"/>
+<text text-anchor="middle" x="301" y="-675" font-family="Helvetica,sans-Serif" font-size="10.00"> #data_</text>
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diff --git a/docs/api/doxygen/classtvm_1_1TargetIdNode__inherit__graph.svg b/docs/api/doxygen/classtvm_1_1TargetIdNode__inherit__graph.svg
index f5e8a84..eb31c7e 100644
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+++ b/docs/api/doxygen/classtvm_1_1TargetIdNode__inherit__graph.svg
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<title>tvm::TargetIdNode</title>
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+<polygon fill="white" stroke="none" points="-4,4 -4,-222 209,-222 209,4 -4,4"/>
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<g id="node1" class="node"><title>Node1</title>
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-<text text-anchor="middle" x="102.5" y="-100.5" font-family="Helvetica,sans-Serif" font-size="10.00">tvm::TargetIdNode</text>
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-<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ name</text>
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-<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
-<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ ParseAttrsFromRawString()</text>
+<polygon fill="#bfbfbf" stroke="black" points="-1.42109e-14,-0.5 -1.42109e-14,-123.5 205,-123.5 205,-0.5 -1.42109e-14,-0.5"/>
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+<text text-anchor="start" x="8" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ name</text>
+<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ device_type</text>
+<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ default_keys</text>
+<text text-anchor="start" x="8" y="-59.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ _type_key</text>
+<polyline fill="none" stroke="black" points="-1.42109e-14,-52.5 205,-52.5 "/>
+<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ VisitAttrs()</text>
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+<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ StringifyAttrsToRaw()</text>
<text text-anchor="start" x="8" y="-7.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TVM_DECLARE_FINAL_OBJECT_INFO()</text>
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diff --git a/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html b/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html
index e8abb09..ca5ad5f 100644
--- a/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html
+++ b/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer-members.html
@@ -108,7 +108,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#acac92a9522deabe289fea99efbd9eaec">modular_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9dccc7d98b8b9465390e10436d3a9178">operator=</a>(const Analyzer &)=delete</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#acc86c6e8c04cb0de4ff9d78e769924b2">rewrite_simplify</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a2d2bda00f4b06ae8b9fc7bc1cd5b0020">Simplify</a>(const PrimExpr &expr)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">Simplify</a>(const PrimExpr &expr, int steps=2)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
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diff --git a/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer.html b/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer.html
index aa2a82c..4b15219 100644
--- a/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer.html
+++ b/docs/api/doxygen/classtvm_1_1arith_1_1Analyzer.html
@@ -134,9 +134,9 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:af84b3978b1df3b2680fb47818e5dc30d"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#af84b3978b1df3b2680fb47818e5dc30d">CanProve</a> (const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> &cond)</td></tr>
<tr class="memdesc:af84b3978b1df3b2680fb47818e5dc30d"><td class="mdescLeft"> </td><td class="mdescRight">Whether can we prove condition. <a href="#af84b3978b1df3b2680fb47818e5dc30d">More...</a><br /></td></tr>
<tr class="separator:af84b3978b1df3b2680fb47818e5dc30d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2d2bda00f4b06ae8b9fc7bc1cd5b0020"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a2d2bda00f4b06ae8b9fc7bc1cd5b0020">Simplify</a> (const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> &expr)</td></tr>
-<tr class="memdesc:a2d2bda00f4b06ae8b9fc7bc1cd5b0020"><td class="mdescLeft"> </td><td class="mdescRight">Simplify expr. <a href="#a2d2bda00f4b06ae8b9fc7bc1cd5b0020">More...</a><br /></td></tr>
-<tr class="separator:a2d2bda00f4b06ae8b9fc7bc1cd5b0020"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9b440f852f12ad0a4d8ed5ed97054425"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html#a9b440f852f12ad0a4d8ed5ed97054425">Simplify</a> (const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> &expr, int steps=2)</td></tr>
+<tr class="memdesc:a9b440f852f12ad0a4d8ed5ed97054425"><td class="mdescLeft"> </td><td class="mdescRight">Simplify expr. <a href="#a9b440f852f12ad0a4d8ed5ed97054425">More...</a><br /></td></tr>
+<tr class="separator:a9b440f852f12ad0a4d8ed5ed97054425"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
@@ -450,7 +450,7 @@ Public Attributes</h2></td></tr>
</div>
</div>
-<a class="anchor" id="a2d2bda00f4b06ae8b9fc7bc1cd5b0020"></a>
+<a class="anchor" id="a9b440f852f12ad0a4d8ed5ed97054425"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -458,8 +458,18 @@ Public Attributes</h2></td></tr>
<td class="memname"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> tvm::arith::Analyzer::Simplify </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> & </td>
- <td class="paramname"><em>expr</em></td><td>)</td>
+ <td class="paramname"><em>expr</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
<td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>steps</em> = <code>2</code> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
@@ -468,6 +478,7 @@ Public Attributes</h2></td></tr>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">expr</td><td>The expression to be simplified. </td></tr>
+ <tr><td class="paramname">steps</td><td>The simplification runs in the order of rewrite_simplify (step 1) -> canonical_simplify (step 2) -> rewrite_simplify (step 3) -> canonical_simplify (step 4) -> ... param steps controls how many steps to run. Default is 2, i.e., rewrite_simplify + canonical_simplify. </td></tr>
</table>
</dd>
</dl>
diff --git a/docs/api/doxygen/classtvm_1_1runtime_1_1TVMArgs-members.html b/docs/api/doxygen/classtvm_1_1arith_1_1IntGroupBounds-members.html
similarity index 64%
copy from docs/api/doxygen/classtvm_1_1runtime_1_1TVMArgs-members.html
copy to docs/api/doxygen/classtvm_1_1arith_1_1IntGroupBounds-members.html
index 7dbdc4f..266e2ea 100644
--- a/docs/api/doxygen/classtvm_1_1runtime_1_1TVMArgs-members.html
+++ b/docs/api/doxygen/classtvm_1_1arith_1_1IntGroupBounds-members.html
@@ -83,23 +83,23 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div id="nav-path" class="navpath">
<ul>
-<li class="navelem"><a class="el" href="namespacetvm.html">tvm</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1runtime.html">runtime</a></li><li class="navelem"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">TVMArgs</a></li> </ul>
+<li class="navelem"><a class="el" href="namespacetvm.html">tvm</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1arith.html">arith</a></li><li class="navelem"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a></li> </ul>
</div>
</div><!-- top -->
<div class="header">
<div class="headertitle">
-<div class="title">tvm::runtime::TVMArgs Member List</div> </div>
+<div class="title">tvm::arith::IntGroupBounds Member List</div> </div>
</div><!--header-->
<div class="contents">
-<p>This is the complete list of members for <a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a>, including all inherited members.</p>
+<p>This is the complete list of members for <a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#afec79c909bdf103a01cdb4732c0afdf3">num_args</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#a92845a3902afa7207adcaca1e03f8aaa">operator[]</a>(int i) const </td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#aab545038cd3023195731bae5d6026b9f">size</a>() const </td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#a5d2aeac7387e436e6e19d7a6dfe91307">TVMArgs</a>(const TVMValue *values, const int *type_codes, int num_args)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#abdb0f929087b14eb28d75c7a047cef9e">type_codes</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html#a3b99059e2f1ad08c99b42b5bee82752f">values</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html">tvm::runtime::TVMArgs</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a6497afa70a1d266d3e62cca1ae1da0d1">FindBestRange</a>(const Map< Var, Range > &vranges_addl={}) const </td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a1ada3993bd9b6500ccd95d97a5c9f17b">FromRange</a>(const Range &r)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a7f2fbfd39f1fc030b13056cf28d7c17c">IntGroupBounds</a>(PrimExpr coef, Array< PrimExpr > lower, Array< PrimExpr > equal, Array< PrimExpr > upper)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a5de071b5c736097d25fa1a508b06f565">operator+</a>(const Range &r)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#ab722c6d564384b7d6e9160e9b83297c2">Substitute</a>(const Map< Var, PrimExpr > &subst) const </td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#ae489402cb3ec9fbcfd57bd8404803dd2">TVM_DEFINE_OBJECT_REF_METHODS</a>(IntGroupBounds, ObjectRef, IntGroupBoundsNode)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">tvm::arith::IntGroupBounds</a></td><td class="entry"></td></tr>
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diff --git a/docs/api/doxygen/classtvm_1_1arith_1_1IntGroupBounds.html b/docs/api/doxygen/classtvm_1_1arith_1_1IntGroupBounds.html
new file mode 100644
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+++ b/docs/api/doxygen/classtvm_1_1arith_1_1IntGroupBounds.html
@@ -0,0 +1,337 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+<meta name="generator" content="Doxygen 1.8.11"/>
+<title>tvm: tvm::arith::IntGroupBounds Class Reference</title>
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+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+ <td id="projectalign" style="padding-left: 0.5em;">
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+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.11 -->
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
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+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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+ name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div id="nav-path" class="navpath">
+ <ul>
+<li class="navelem"><a class="el" href="namespacetvm.html">tvm</a></li><li class="navelem"><a class="el" href="namespacetvm_1_1arith.html">arith</a></li><li class="navelem"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a></li> </ul>
+</div>
+</div><!-- top -->
+<div class="header">
+ <div class="summary">
+<a href="#pub-methods">Public Member Functions</a> |
+<a href="#pub-static-methods">Static Public Member Functions</a> |
+<a href="classtvm_1_1arith_1_1IntGroupBounds-members.html">List of all members</a> </div>
+ <div class="headertitle">
+<div class="title">tvm::arith::IntGroupBounds Class Reference</div> </div>
+</div><!--header-->
+<div class="contents">
+
+<p>Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusive), upper bounds (inclusive) and equalities. It also contains coefficient as a multiplier for the bounds, i.e., coef * var >= lower coef * var == equal coef * var <= upper. ">IntGroupBoundsNode</a>.
+ <a href="classtvm_1_1arith_1_1IntGroupBounds.html#details">More...</a></p>
+
+<p><code>#include <<a class="el" href="int__solver_8h_source.html">int_solver.h</a>></code></p>
+<div class="dynheader">
+Inheritance diagram for tvm::arith::IntGroupBounds:</div>
+<div class="dyncontent">
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1arith_1_1IntGroupBounds__inherit__graph.svg" width="211" height="302"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</div>
+</div>
+<div class="dynheader">
+Collaboration diagram for tvm::arith::IntGroupBounds:</div>
+<div class="dyncontent">
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1arith_1_1IntGroupBounds__coll__graph.svg" width="211" height="302"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+</div>
+</div>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
+Public Member Functions</h2></td></tr>
+<tr class="memitem:a7f2fbfd39f1fc030b13056cf28d7c17c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a7f2fbfd39f1fc030b13056cf28d7c17c">IntGroupBounds</a> (<a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> coef, Array< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > <a class="el" href="namespacetvm.html#a3ab284fe08e7bcfb9b9fe33f18f5f205">lower [...]
+<tr class="memdesc:a7f2fbfd39f1fc030b13056cf28d7c17c"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classtvm_1_1Constructor.html" title="Managed reference to ConstructorNode. ">Constructor</a> by fields. <a href="#a7f2fbfd39f1fc030b13056cf28d7c17c">More...</a><br /></td></tr>
+<tr class="separator:a7f2fbfd39f1fc030b13056cf28d7c17c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab722c6d564384b7d6e9160e9b83297c2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#ab722c6d564384b7d6e9160e9b83297c2">Substitute</a> (const <a class="el" href="classtvm_1_1Map.html">Map</a>< Var, <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > &subst) c [...]
+<tr class="memdesc:ab722c6d564384b7d6e9160e9b83297c2"><td class="mdescLeft"> </td><td class="mdescRight">Perform substitution on all components of the struct. <a href="#ab722c6d564384b7d6e9160e9b83297c2">More...</a><br /></td></tr>
+<tr class="separator:ab722c6d564384b7d6e9160e9b83297c2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6497afa70a1d266d3e62cca1ae1da0d1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1Range.html">Range</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a6497afa70a1d266d3e62cca1ae1da0d1">FindBestRange</a> (const <a class="el" href="classtvm_1_1Map.html">Map</a>< Var, <a class="el" href="classtvm_1_1Range.html">Range</a> > &vranges_addl={}) const </td></tr>
+<tr class="memdesc:a6497afa70a1d266d3e62cca1ae1da0d1"><td class="mdescLeft"> </td><td class="mdescRight">Find the best range from the grouped bounds. <a href="#a6497afa70a1d266d3e62cca1ae1da0d1">More...</a><br /></td></tr>
+<tr class="separator:a6497afa70a1d266d3e62cca1ae1da0d1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5de071b5c736097d25fa1a508b06f565"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a5de071b5c736097d25fa1a508b06f565">operator+</a> (const <a class="el" href="classtvm_1_1Range.html">Range</a> &r)</td></tr>
+<tr class="memdesc:a5de071b5c736097d25fa1a508b06f565"><td class="mdescLeft"> </td><td class="mdescRight">Combine the bounds with another range. <a href="#a5de071b5c736097d25fa1a508b06f565">More...</a><br /></td></tr>
+<tr class="separator:a5de071b5c736097d25fa1a508b06f565"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae489402cb3ec9fbcfd57bd8404803dd2"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#ae489402cb3ec9fbcfd57bd8404803dd2">TVM_DEFINE_OBJECT_REF_METHODS</a> (<a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a>, ObjectRef, <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a>)</td></tr>
+<tr class="separator:ae489402cb3ec9fbcfd57bd8404803dd2"><td class="memSeparator" colspan="2"> </td></tr>
+</table><table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
+Static Public Member Functions</h2></td></tr>
+<tr class="memitem:a1ada3993bd9b6500ccd95d97a5c9f17b"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html#a1ada3993bd9b6500ccd95d97a5c9f17b">FromRange</a> (const <a class="el" href="classtvm_1_1Range.html">Range</a> &r)</td></tr>
+<tr class="memdesc:a1ada3993bd9b6500ccd95d97a5c9f17b"><td class="mdescLeft"> </td><td class="mdescRight">Construct bounds from a range. <a href="#a1ada3993bd9b6500ccd95d97a5c9f17b">More...</a><br /></td></tr>
+<tr class="separator:a1ada3993bd9b6500ccd95d97a5c9f17b"><td class="memSeparator" colspan="2"> </td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><p>Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusive), upper bounds (inclusive) and equalities. It also contains coefficient as a multiplier for the bounds, i.e., coef * var >= lower coef * var == equal coef * var <= upper. ">IntGroupBoundsNode</a>. </p>
+<dl class="section see"><dt>See also</dt><dd><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusive), upper bounds (inclusive) and equalities. It also contains coefficient as a multiplier for the bounds, i.e., coef * var >= lower coef * var == equal coef * var <= upper. ">IntGroupBoundsNode</a> </dd></dl>
+</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
+<a class="anchor" id="a7f2fbfd39f1fc030b13056cf28d7c17c"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">tvm::arith::IntGroupBounds::IntGroupBounds </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td>
+ <td class="paramname"><em>coef</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">Array< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>lower</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">Array< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>equal</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">Array< <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > </td>
+ <td class="paramname"><em>upper</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p><a class="el" href="classtvm_1_1Constructor.html" title="Managed reference to ConstructorNode. ">Constructor</a> by fields. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">coef</td><td>The coefficient. Must be integer. coef * var >= lower coef * var == equal coef * var >= upper </td></tr>
+ <tr><td class="paramname">lower</td><td>the lower bounds (include) </td></tr>
+ <tr><td class="paramname">equal</td><td>equalities </td></tr>
+ <tr><td class="paramname">upper</td><td>the upper bounds (include) </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<h2 class="groupheader">Member Function Documentation</h2>
+<a class="anchor" id="a6497afa70a1d266d3e62cca1ae1da0d1"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1Range.html">Range</a> tvm::arith::IntGroupBounds::FindBestRange </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1Map.html">Map</a>< Var, <a class="el" href="classtvm_1_1Range.html">Range</a> > & </td>
+ <td class="paramname"><em>vranges_addl</em> = <code>{}</code></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Find the best range from the grouped bounds. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">vranges_addl</td><td>additional variable ranges that help infer the best range. </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The best range (has the least difference between the lower bound and upper bound). undefined if (-inf, +inf). </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a1ada3993bd9b6500ccd95d97a5c9f17b"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">static <a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> tvm::arith::IntGroupBounds::FromRange </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1Range.html">Range</a> & </td>
+ <td class="paramname"><em>r</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">static</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Construct bounds from a range. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">r</td><td>The range </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>constructed bounds. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a5de071b5c736097d25fa1a508b06f565"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> tvm::arith::IntGroupBounds::operator+ </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1Range.html">Range</a> & </td>
+ <td class="paramname"><em>r</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Combine the bounds with another range. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">r</td><td>range to be combined. </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>combined bounds. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ab722c6d564384b7d6e9160e9b83297c2"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> tvm::arith::IntGroupBounds::Substitute </td>
+ <td>(</td>
+ <td class="paramtype">const <a class="el" href="classtvm_1_1Map.html">Map</a>< Var, <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> > & </td>
+ <td class="paramname"><em>subst</em></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Perform substitution on all components of the struct. </p>
+
+</div>
+</div>
+<a class="anchor" id="ae489402cb3ec9fbcfd57bd8404803dd2"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">tvm::arith::IntGroupBounds::TVM_DEFINE_OBJECT_REF_METHODS </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">ObjectRef </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> </td>
+ <td class="paramname"> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<hr/>The documentation for this class was generated from the following file:<ul>
+<li>include/tvm/arith/<a class="el" href="int__solver_8h_source.html">int_solver.h</a></li>
+</ul>
+</div><!-- contents -->
+<!-- start footer part -->
+<hr class="footer"/><address class="footer"><small>
+Generated by  <a href="http://www.doxygen.org/index.html">
+<img class="footer" src="doxygen.png" alt="doxygen"/>
+</a> 1.8.11
+</small></address>
+</body>
+</html>
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