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Posted to commits@tvm.apache.org by tq...@apache.org on 2024/11/14 17:14:47 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@4cfd1817f724066030676d3830ff4cc6649030c4)
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/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 956db5a2e5 deploying docs (apache/tvm@4cfd1817f724066030676d3830ff4cc6649030c4)
956db5a2e5 is described below
commit 956db5a2e5fb27eae3f84de1113d20536e852ff9
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Thu Nov 14 17:14:40 2024 +0000
deploying docs (apache/tvm@4cfd1817f724066030676d3830ff4cc6649030c4)
---
.../deploy_model_on_adreno_tvmc.ipynb | 10 +-
.../deploy_model_on_adreno_tvmc.py | 34 +-
.../relax/tutorials/sg_execution_times.rst.txt | 6 +-
.../tensor_ir/tutorials/sg_execution_times.rst.txt | 6 +-
.../tensor_ir/tutorials/tir_transformation.rst.txt | 6 +-
.../get_started/tutorials/ir_module.rst.txt | 8 +-
.../tutorials/sg_execution_times.rst.txt | 6 +-
.../how_to/compile_models/from_darknet.rst.txt | 2 +-
.../how_to/compile_models/from_keras.rst.txt | 2 +-
.../how_to/compile_models/from_oneflow.rst.txt | 2 +-
.../how_to/compile_models/from_paddle.rst.txt | 2 +-
.../how_to/compile_models/from_pytorch.rst.txt | 2 +-
.../how_to/compile_models/from_tensorflow.rst.txt | 2 +-
.../compile_models/sg_execution_times.rst.txt | 20 +-
.../deploy_models/deploy_model_on_adreno.rst.txt | 4 +-
.../deploy_model_on_adreno_tvmc.rst.txt | 695 +++++++++++---------
.../deploy_models/deploy_model_on_android.rst.txt | 2 +-
.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 2 +-
docs/_sources/how_to/deploy_models/index.rst.txt | 2 +-
.../deploy_models/sg_execution_times.rst.txt | 18 +-
.../how_to/extend_tvm/sg_execution_times.rst.txt | 8 +-
.../how_to/extend_tvm/use_pass_instrument.rst.txt | 16 +-
.../optimize_operators/opt_conv_cuda.rst.txt | 2 +-
.../optimize_operators/opt_conv_tensorcore.rst.txt | 2 +-
.../how_to/optimize_operators/opt_gemm.rst.txt | 16 +-
.../optimize_operators/sg_execution_times.rst.txt | 8 +-
.../sg_execution_times.rst.txt | 14 +-
.../tune_conv2d_layer_cuda.rst.txt | 2 +-
.../tune_network_cuda.rst.txt | 4 +-
.../tune_network_x86.rst.txt | 4 +-
.../tune_with_autotvm/sg_execution_times.rst.txt | 10 +-
.../tune_with_autotvm/tune_conv2d_cuda.rst.txt | 2 +-
.../tutorials/cross_compilation_and_rpc.rst.txt | 2 +-
.../_sources/how_to/tutorials/optimize_llm.rst.txt | 2 +-
.../how_to/tutorials/sg_execution_times.rst.txt | 10 +-
.../work_with_microtvm/micro_autotune.rst.txt | 18 +-
.../work_with_microtvm/micro_pytorch.rst.txt | 4 +-
.../how_to/work_with_microtvm/micro_train.rst.txt | 16 +-
.../work_with_microtvm/sg_execution_times.rst.txt | 14 +-
.../work_with_relay/sg_execution_times.rst.txt | 8 +-
.../work_with_schedules/sg_execution_times.rst.txt | 16 +-
.../tutorials/frontend/deploy_detection.rst.txt | 4 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 4 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
.../topic/vta/tutorials/sg_execution_times.rst.txt | 6 +-
.../tutorial/auto_scheduler_matmul_x86.rst.txt | 4 +-
docs/_sources/tutorial/autotvm_matmul_x86.rst.txt | 20 +-
docs/_sources/tutorial/autotvm_relay_x86.rst.txt | 56 +-
docs/_sources/tutorial/intro_topi.rst.txt | 2 +-
docs/_sources/tutorial/sg_execution_times.rst.txt | 20 +-
.../tutorial/tensor_expr_get_started.rst.txt | 51 +-
docs/commit_hash | 2 +-
.../relax/tutorials/sg_execution_times.html | 6 +-
.../tensor_ir/tutorials/sg_execution_times.html | 6 +-
.../tensor_ir/tutorials/tir_creation.html | 24 +-
.../tensor_ir/tutorials/tir_transformation.html | 23 +-
docs/get_started/tutorials/ir_module.html | 8 +-
docs/get_started/tutorials/sg_execution_times.html | 6 +-
docs/how_to/compile_models/from_darknet.html | 2 +-
docs/how_to/compile_models/from_keras.html | 2 +-
docs/how_to/compile_models/from_oneflow.html | 15 +-
docs/how_to/compile_models/from_paddle.html | 2 +-
docs/how_to/compile_models/from_pytorch.html | 14 +-
docs/how_to/compile_models/from_tensorflow.html | 2 +-
docs/how_to/compile_models/sg_execution_times.html | 20 +-
.../deploy_models/deploy_model_on_adreno.html | 4 +-
.../deploy_models/deploy_model_on_adreno_tvmc.html | 705 +++++++++++----------
.../deploy_models/deploy_model_on_android.html | 2 +-
.../deploy_object_detection_pytorch.html | 37 +-
docs/how_to/deploy_models/deploy_prequantized.html | 8 +-
.../deploy_models/deploy_prequantized_tflite.html | 2 +-
docs/how_to/deploy_models/index.html | 2 +-
docs/how_to/deploy_models/sg_execution_times.html | 26 +-
docs/how_to/extend_tvm/sg_execution_times.html | 8 +-
docs/how_to/extend_tvm/use_pass_instrument.html | 16 +-
docs/how_to/optimize_operators/opt_conv_cuda.html | 2 +-
.../optimize_operators/opt_conv_tensorcore.html | 2 +-
docs/how_to/optimize_operators/opt_gemm.html | 16 +-
.../optimize_operators/sg_execution_times.html | 8 +-
.../sg_execution_times.html | 14 +-
.../tune_conv2d_layer_cuda.html | 2 +-
.../tune_with_autoscheduler/tune_network_cuda.html | 4 +-
.../tune_with_autoscheduler/tune_network_x86.html | 4 +-
.../tune_with_autotvm/sg_execution_times.html | 10 +-
.../how_to/tune_with_autotvm/tune_conv2d_cuda.html | 2 +-
.../tutorials/cross_compilation_and_rpc.html | 2 +-
docs/how_to/tutorials/optimize_llm.html | 2 +-
docs/how_to/tutorials/sg_execution_times.html | 10 +-
docs/how_to/work_with_microtvm/micro_autotune.html | 18 +-
docs/how_to/work_with_microtvm/micro_pytorch.html | 4 +-
docs/how_to/work_with_microtvm/micro_train.html | 16 +-
.../work_with_microtvm/sg_execution_times.html | 14 +-
.../how_to/work_with_relay/sg_execution_times.html | 8 +-
.../work_with_schedules/sg_execution_times.html | 20 +-
docs/reference/api/python/auto_scheduler.html | 4 +-
.../api/typedoc/classes/ArtifactCache.html | 14 +-
.../typedoc/classes/ArtifactIndexedDBCache.html | 16 +-
docs/reference/api/typedoc/classes/DLDataType.html | 14 +-
docs/reference/api/typedoc/classes/DLDevice.html | 12 +-
docs/reference/api/typedoc/classes/Instance.html | 116 ++--
.../classes/LinearCongruentialGenerator.html | 16 +-
docs/reference/api/typedoc/classes/Module.html | 12 +-
docs/reference/api/typedoc/classes/NDArray.html | 30 +-
docs/reference/api/typedoc/classes/RPCServer.html | 16 +-
docs/reference/api/typedoc/classes/Scalar.html | 8 +-
docs/reference/api/typedoc/classes/TVMArray.html | 16 +-
docs/reference/api/typedoc/classes/TVMObject.html | 12 +-
.../api/typedoc/classes/VirtualMachine.html | 10 +-
.../classes/_internal_.AsyncifyHandler.html | 12 +-
.../classes/_internal_.CachedCallStack.html | 36 +-
.../classes/_internal_.CanvasRenderManager.html | 10 +-
.../typedoc/classes/_internal_.Environment.html | 14 +-
.../api/typedoc/classes/_internal_.FFILibrary.html | 22 +-
.../api/typedoc/classes/_internal_.Memory.html | 36 +-
.../typedoc/classes/_internal_.PackedFuncCell.html | 8 +-
.../typedoc/classes/_internal_.RuntimeContext.html | 60 +-
.../api/typedoc/classes/_internal_.TVMString.html | 14 +-
.../typedoc/classes/_internal_.WebGPUContext.html | 28 +-
.../enums/_internal_.AsyncifyStateKind.html | 8 +-
.../typedoc/enums/_internal_.RPCServerState.html | 14 +-
docs/reference/api/typedoc/functions/assert.html | 2 +-
.../api/typedoc/functions/createPolyfillWASI.html | 2 +-
.../api/typedoc/functions/deleteNDArrayCache.html | 2 +-
.../api/typedoc/functions/detectGPUDevice.html | 2 +-
.../api/typedoc/functions/hasNDArrayInCache.html | 2 +-
.../api/typedoc/functions/instantiate.html | 2 +-
docs/reference/api/typedoc/functions/wasmPath.html | 2 +-
.../typedoc/interfaces/ArtifactCacheTemplate.html | 10 +-
.../api/typedoc/interfaces/Disposable.html | 4 +-
.../typedoc/interfaces/GPUDeviceDetectOutput.html | 8 +-
.../api/typedoc/interfaces/InitProgressReport.html | 8 +-
.../api/typedoc/interfaces/LibraryProvider.html | 6 +-
.../interfaces/_internal_.FunctionInfo.html | 8 +-
.../interfaces/_internal_.NDArrayCacheEntry.html | 14 +-
.../interfaces/_internal_.NDArrayShardEntry.html | 10 +-
.../api/typedoc/types/InitProgressCallback.html | 2 +-
docs/reference/api/typedoc/types/PackedFunc.html | 2 +-
.../typedoc/types/_internal_.AsyncPackedFunc.html | 2 +-
.../types/_internal_.FObjectConstructor.html | 2 +-
.../types/_internal_.FTVMWasmAllocSpace.html | 2 +-
.../types/_internal_.FTVMWasmFreeSpace.html | 2 +-
.../types/_internal_.FTVMWasmPackedCFunc.html | 2 +-
.../api/typedoc/types/_internal_.Pointer.html | 2 +-
.../typedoc/types/_internal_.TVMObjectBase.html | 2 +-
docs/searchindex.js | 2 +-
.../vta/tutorials/frontend/deploy_detection.html | 4 +-
.../vta/tutorials/frontend/sg_execution_times.html | 4 +-
.../vta/tutorials/optimize/sg_execution_times.html | 6 +-
docs/topic/vta/tutorials/sg_execution_times.html | 6 +-
docs/tutorial/auto_scheduler_matmul_x86.html | 4 +-
docs/tutorial/autotvm_matmul_x86.html | 20 +-
docs/tutorial/autotvm_relay_x86.html | 262 ++++----
docs/tutorial/intro_topi.html | 2 +-
docs/tutorial/sg_execution_times.html | 26 +-
docs/tutorial/tensor_expr_get_started.html | 47 +-
157 files changed, 1710 insertions(+), 1623 deletions(-)
diff --git a/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb b/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb
index 9bcb66ac48..4fd855d96b 100644
--- a/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb
+++ b/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\n# Deploy the Pretrained Model on Adreno\u2122 with tvmc Interface\n**Author**: Siva Rama Krishna\n\nThis article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno\u2122.\n\nBesides that, you should have TVM built for Android.\nSee the following instructions on how to build it and setup RPC environment.\n\n[Deploy to Adreno GPU](https://tvm.apache.org/docs/how_to/deploy/adreno.html)\n"
+ "\n\n# Deploy the Pretrained Model on Adreno\u2122 with tvmc Interface\n**Author**: Siva Rama Krishna\n\nThis article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 model on Adreno\u2122.\n\nBesides that, you should have TVM built for Android.\nSee the following instructions on how to build it and setup RPC environment.\n\n[Deploy to Adreno GPU](https://tvm.apache.org/docs/how_to/deploy/adreno.html)\n"
]
},
{
@@ -51,7 +51,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Make a Keras Resnet50 Model\n\n"
+ "## Make a PyTorch Resnet50 Model\n\n"
]
},
{
@@ -62,7 +62,7 @@
},
"outputs": [],
"source": [
- "from tensorflow.keras.applications.resnet50 import ResNet50\n\ntmp_path = utils.tempdir()\nmodel_file_name = tmp_path.relpath(\"resnet50.h5\")\n\nmodel = ResNet50(include_top=True, weights=\"imagenet\", input_shape=(224, 224, 3), classes=1000)\nmodel.save(model_file_name)"
+ "import torch\nimport torchvision.models as models\n\n# Load the ResNet50 model pre-trained on ImageNet\nmodel = models.resnet50(pretrained=True)\n\n# Set the model to evaluation mode\nmodel.eval()\n\n# Define the input shape\ndummy_input = torch.randn(1, 3, 224, 224)\n\n# Trace the model\ntraced_model = torch.jit.trace(model, dummy_input)\n\n# Save the traced model\nmodel_file_name = \"resnet50_traced.pt\"\ntraced_model.save(model_file_name)"
]
},
{
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "tvmc_model = tvmc.load(model_file_name)\n\nprint(tvmc_model.mod)\n\n# tvmc_model consists of tvmc_mode.mod which is relay module and tvmc_model.params which parms of the module."
+ "input_shape = (1, 3, 224, 224) # Batch size, channels, height, width\n\n# Load the TorchScript model with TVMC\ntvmc_model = tvmc.load(model_file_name, shape_dict={\"input\": input_shape}, model_format=\"pytorch\")\n\nprint(tvmc_model.mod)\n\n# tvmc_model consists of tvmc_mode.mod which is relay module and tvmc_model.params which parms of the module."
]
},
{
@@ -116,7 +116,7 @@
},
"outputs": [],
"source": [
- "# This generated example running on our x86 server for demonstration.\n# To deply and tun on real target over RPC please set :code:`local_demo` to False in above configuration sestion.\n\n# OpenCLML offloading will try to accelerate supported operators by using OpenCLML proprietory operator library.\n# By default :code:`enable_clml` is set to False in above configuration section.\n\nif not enable_clml:\n if local_demo:\n tvmc_package = tvmc.compile(\n tvmc_m [...]
+ "# This generated example running on our x86 server for demonstration.\n# To deply and tun on real target over RPC please set :code:`local_demo` to False in above configuration sestion.\n\n# OpenCLML offloading will try to accelerate supported operators by using OpenCLML proprietory operator library.\n# By default :code:`enable_clml` is set to False in above configuration section.\n\nif not enable_clml:\n if local_demo:\n tvmc_package = tvmc.compile(\n tvmc_m [...]
]
},
{
diff --git a/docs/_downloads/f84e457773d8f292bfac905e2ed3307f/deploy_model_on_adreno_tvmc.py b/docs/_downloads/f84e457773d8f292bfac905e2ed3307f/deploy_model_on_adreno_tvmc.py
index b54ac1b2c6..0e037e9f91 100644
--- a/docs/_downloads/f84e457773d8f292bfac905e2ed3307f/deploy_model_on_adreno_tvmc.py
+++ b/docs/_downloads/f84e457773d8f292bfac905e2ed3307f/deploy_model_on_adreno_tvmc.py
@@ -22,7 +22,7 @@ Deploy the Pretrained Model on Adreno™ with tvmc Interface
==========================================================
**Author**: Siva Rama Krishna
-This article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno™.
+This article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 model on Adreno™.
Besides that, you should have TVM built for Android.
See the following instructions on how to build it and setup RPC environment.
@@ -71,16 +71,27 @@ cross_compiler = (
)
#######################################################################
-# Make a Keras Resnet50 Model
-# ---------------------------
+# Make a PyTorch Resnet50 Model
+# -----------------------------
-from tensorflow.keras.applications.resnet50 import ResNet50
+import torch
+import torchvision.models as models
-tmp_path = utils.tempdir()
-model_file_name = tmp_path.relpath("resnet50.h5")
+# Load the ResNet50 model pre-trained on ImageNet
+model = models.resnet50(pretrained=True)
-model = ResNet50(include_top=True, weights="imagenet", input_shape=(224, 224, 3), classes=1000)
-model.save(model_file_name)
+# Set the model to evaluation mode
+model.eval()
+
+# Define the input shape
+dummy_input = torch.randn(1, 3, 224, 224)
+
+# Trace the model
+traced_model = torch.jit.trace(model, dummy_input)
+
+# Save the traced model
+model_file_name = "resnet50_traced.pt"
+traced_model.save(model_file_name)
#######################################################################
@@ -89,7 +100,10 @@ model.save(model_file_name)
# Convert a model from any framework to a tvm relay module.
# tvmc.load supports models from any framework (like tensorflow saves_model, onnx, tflite ..etc) and auto detects the filetype.
-tvmc_model = tvmc.load(model_file_name)
+input_shape = (1, 3, 224, 224) # Batch size, channels, height, width
+
+# Load the TorchScript model with TVMC
+tvmc_model = tvmc.load(model_file_name, shape_dict={"input": input_shape}, model_format="pytorch")
print(tvmc_model.mod)
@@ -158,7 +172,7 @@ else:
# Altrernatively, we can save the compilation output and save it as a TVMCPackage.
# This way avoids loading of compiled module without compiling again.
target = target + ", clml"
- pkg_path = tmp_path.relpath("keras-resnet50.tar")
+ pkg_path = tmp_path.relpath("torch-resnet50.tar")
tvmc.compile(
tvmc_model,
target=target,
diff --git a/docs/_sources/deep_dive/relax/tutorials/sg_execution_times.rst.txt b/docs/_sources/deep_dive/relax/tutorials/sg_execution_times.rst.txt
index b1fdcf6424..ddbde4488c 100644
--- a/docs/_sources/deep_dive/relax/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/deep_dive/relax/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:00.266** total execution time for **deep_dive_relax_tutorials** files:
+**00:00.240** total execution time for **deep_dive_relax_tutorials** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_deep_dive_relax_tutorials_relax_creation.py` (``relax_creation.py``) | 00:00.179 | 0.0 MB |
+| :ref:`sphx_glr_deep_dive_relax_tutorials_relax_creation.py` (``relax_creation.py``) | 00:00.158 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_deep_dive_relax_tutorials_relax_transformation.py` (``relax_transformation.py``) | 00:00.086 | 0.0 MB |
+| :ref:`sphx_glr_deep_dive_relax_tutorials_relax_transformation.py` (``relax_transformation.py``) | 00:00.082 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt b/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt
index eb46e074d4..254bdaf930 100644
--- a/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:00.582** total execution time for **deep_dive_tensor_ir_tutorials** files:
+**00:00.536** total execution time for **deep_dive_tensor_ir_tutorials** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_transformation.py` (``tir_transformation.py``) | 00:00.343 | 0.0 MB |
+| :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_transformation.py` (``tir_transformation.py``) | 00:00.322 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_creation.py` (``tir_creation.py``) | 00:00.239 | 0.0 MB |
+| :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_creation.py` (``tir_creation.py``) | 00:00.214 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt b/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt
index 03e3f0a0e4..ce1e996f3d 100644
--- a/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt
+++ b/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt
@@ -123,7 +123,7 @@ original implementation.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 2.3499 2.3499 2.3499 2.3499 0.0000
+ 2.2376 2.2376 2.2376 2.2376 0.0000
@@ -295,7 +295,7 @@ action involves reordering these two loops.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.9641 0.9641 0.9641 0.9641 0.0000
+ 0.8585 0.8585 0.8585 0.8585 0.0000
@@ -423,7 +423,7 @@ from the reduction update via the **decompose_reduction** primitive.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.3365 0.3365 0.3365 0.3365 0.0000
+ 0.3192 0.3192 0.3192 0.3192 0.0000
diff --git a/docs/_sources/get_started/tutorials/ir_module.rst.txt b/docs/_sources/get_started/tutorials/ir_module.rst.txt
index 08af016615..199ef51895 100644
--- a/docs/_sources/get_started/tutorials/ir_module.rst.txt
+++ b/docs/_sources/get_started/tutorials/ir_module.rst.txt
@@ -698,8 +698,8 @@ We can deploy the IRModule on CPU by specifying the target as ``llvm``.
.. code-block:: none
- [[ 0.07704955 0.22635555 -0.02108879 0.14885926 -0.22873458 0.067922
- 0.22715238 0.00922903 0.0203557 -0.12565452]]
+ [[ 0.2722235 0.3597607 0.05561743 -0.29528138 -0.13545823 -0.11075222
+ -0.1564103 0.05459877 -0.1148698 0.04708464]]
@@ -765,8 +765,8 @@ Now we can compile the IRModule on GPU, the similar way as we did on CPU.
.. code-block:: none
- [[ 0.07704955 0.22635555 -0.02108881 0.14885929 -0.22873458 0.06792203
- 0.22715238 0.00922902 0.02035576 -0.12565461]]
+ [[ 0.27222347 0.3597607 0.05561745 -0.29528147 -0.13545828 -0.11075222
+ -0.15641028 0.0545988 -0.11486976 0.04708462]]
diff --git a/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt b/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
index e534b3b45d..3c70cebc78 100644
--- a/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:03.113** total execution time for **get_started_tutorials** files:
+**00:02.681** total execution time for **get_started_tutorials** files:
+---------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_get_started_tutorials_ir_module.py` (``ir_module.py``) | 00:02.898 | 0.0 MB |
+| :ref:`sphx_glr_get_started_tutorials_ir_module.py` (``ir_module.py``) | 00:02.484 | 0.0 MB |
+---------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_get_started_tutorials_quick_start.py` (``quick_start.py``) | 00:00.215 | 0.0 MB |
+| :ref:`sphx_glr_get_started_tutorials_quick_start.py` (``quick_start.py``) | 00:00.197 | 0.0 MB |
+---------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index 368de61381..fd007f1cec 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 39.739 seconds)
+ **Total running time of the script:** ( 1 minutes 35.639 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index daa0cbfe39..0434482195 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
@@ -232,7 +232,7 @@ Look up prediction top 1 index in 1000 class synset.
.. code-block:: none
Relay top-1 id: 285, class name: Egyptian cat
- 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 971ms/step
+ 1/1 [==============================] - ETA: 0s 1/1 [==============================] - 1s 891ms/step
Keras top-1 id: 285, class name: Egyptian cat
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index 4666e5d5ab..de5692708b 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -121,7 +121,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
- 0%| | 0.00/41.5M [00:00<?, ?B/s] 19%|#9 | 7.99M/41.5M [00:00<00:00, 47.6MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 43.2MB/s] 44%|####4 | 18.4M/41.5M [00:00<00:00, 36.5MB/s] 58%|#####7 | 24.0M/41.5M [00:00<00:00, 29.3MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 39.0MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 36.0MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 36.8MB/s]
+ 0%| | 0.00/41.5M [00:00<?, ?B/s] 19%|#9 | 7.99M/41.5M [00:00<00:00, 49.5MB/s] 35%|###4 | 14.3M/41.5M [00:00<00:00, 32.5MB/s] 43%|####2 | 17.7M/41.5M [00:00<00:00, 32.0MB/s] 54%|#####3 | 22.3M/41.5M [00:00<00:00, 30.4MB/s] 61%|###### | 25.3M/41.5M [00:00<00:00, 27.1MB/s] 77%|#######7 | 32.0M/41.5M [00:00<00:00, 35.7MB/s] 92%|#########2| 38.3M/41.5M [00:01<00:00, 36.3MB/s] 100%|##########| 41.5M/41.5M [00:01<00:00, 33.2MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 4dc9cdbe01..bb0569ce60 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 9.272 seconds)
+ **Total running time of the script:** ( 1 minutes 4.945 seconds)
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 91445934fb..8d6d256026 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -101,7 +101,7 @@ Load a pretrained PyTorch model
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 10%|9 | 4.38M/44.7M [00:00<00:00, 45.6MB/s] 20%|## | 9.00M/44.7M [00:00<00:00, 47.0MB/s] 30%|### | 13.5M/44.7M [00:00<00:00, 42.0MB/s] 39%|###9 | 17.6M/44.7M [00:00<00:00, 40.6MB/s] 49%|####9 | 22.0M/44.7M [00:00<00:00, 41.4MB/s] 58%|#####8 | 26.0M/44.7M [00:00<00:00, 41.3MB/s] 67%|######7 | 30.0M/44.7M [00:00<00:00, 37.9MB/s] 76%|#######6 | 34.0M/44.7M [00:00<00:00, 38.1MB/s] 86%|########6 | 38.5M/44.7M [00:00<00:00, 40.2MB/s] 97%|#########7| 43.4M/44.7M [00:01<00:00, 43.3MB/s] 100%|##########| 44.7M/44.7M [00:01<00:00, 40.3MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 34%|###3 | 15.1M/44.7M [00:00<00:00, 158MB/s] 80%|#######9 | 35.6M/44.7M [00:00<00:00, 191MB/s] 100%|##########| 44.7M/44.7M [00:00<00:00, 191MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 2110a7bb1f..b438af7fc6 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -424,7 +424,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.197 seconds)
+ **Total running time of the script:** ( 1 minutes 32.598 seconds)
.. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index 8ced120068..3dc943974a 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**07:10.069** total execution time for **how_to_compile_models** files:
+**06:46.016** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:39.739 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:35.639 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:36.197 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:32.598 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:09.272 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:04.945 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:47.902 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:44.648 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:42.212 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:39.243 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:31.124 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.235 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:28.298 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:26.539 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.312 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:12.370 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:03.013 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.800 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
index 850ecc41bd..88c97310db 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -664,7 +664,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4121.0005 4120.2496 4127.3097 4118.5587 2.4621
+ 3991.8919 3991.3090 3995.4704 3988.8212 2.2194
@@ -672,7 +672,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 22.353 seconds)
+ **Total running time of the script:** ( 1 minutes 18.100 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_model_on_adreno.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
index 7f0f803970..e3870676e4 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -28,7 +28,7 @@ Deploy the Pretrained Model on Adreno™ with tvmc Interface
==========================================================
**Author**: Siva Rama Krishna
-This article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno™.
+This article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 model on Adreno™.
Besides that, you should have TVM built for Android.
See the following instructions on how to build it and setup RPC environment.
@@ -101,21 +101,32 @@ to run this tutorial with a real device over rpc.
.. GENERATED FROM PYTHON SOURCE LINES 74-76
-Make a Keras Resnet50 Model
----------------------------
+Make a PyTorch Resnet50 Model
+-----------------------------
-.. GENERATED FROM PYTHON SOURCE LINES 76-86
+.. GENERATED FROM PYTHON SOURCE LINES 76-97
.. code-block:: default
- from tensorflow.keras.applications.resnet50 import ResNet50
+ import torch
+ import torchvision.models as models
- tmp_path = utils.tempdir()
- model_file_name = tmp_path.relpath("resnet50.h5")
+ # Load the ResNet50 model pre-trained on ImageNet
+ model = models.resnet50(pretrained=True)
- model = ResNet50(include_top=True, weights="imagenet", input_shape=(224, 224, 3), classes=1000)
- model.save(model_file_name)
+ # Set the model to evaluation mode
+ model.eval()
+
+ # Define the input shape
+ dummy_input = torch.randn(1, 3, 224, 224)
+
+ # Trace the model
+ traced_model = torch.jit.trace(model, dummy_input)
+
+ # Save the traced model
+ model_file_name = "resnet50_traced.pt"
+ traced_model.save(model_file_name)
@@ -126,25 +137,32 @@ Make a Keras Resnet50 Model
.. code-block:: none
- Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
- 8192/102967424 [..............................] - ETA: 0s 2039808/102967424 [..............................] - ETA: 2s 5890048/102967424 [>.............................] - ETA: 1s 8380416/102967424 [=>............................] - ETA: 2s 14188544/102967424 [===>..........................] - ETA: 1s 16769024/102967424 [===>..........................] - ETA: 1s 24141824/102967424 [======>.......................] - ETA: 1s 25157632/102967424 [======>.......................] - ETA: 1s
33546240/102967424 [========>.....................] - ETA: 1s 40189952/102967424 [==========>...................] - ETA: 1s 41934848/102967424 [===========>..................] - ETA: 1s 48578560/102967424 [=============>................] - ETA: 0s 50323456/102967424 [=============>................] - ETA: 0s 56967168/102967424 [===============>..............] - ETA: 0s 58712064/102967424 [================>.............] - ETA: 0s 65355776/102967424 [==================>...........] -
ETA: 0s 67100672/102967424 [==================>...........] - ETA: 0s 69296128/102967424 [===================>..........] - ETA: 0s 71540736/102967424 [===================>..........] - ETA: 0s 75489280/102967424 [====================>.........] - ETA: 0s 83877888/102967424 [=======================>......] - ETA: 0s 92266496/102967424 [=========================>....] - ETA: 0s 98910208/102967424 [===========================>..] - ETA: 0s 100646912/102967424
[============================>.] - ETA: 0s 102850560/102967424 [============================>.] - ETA: 0s 102967424/102967424 [==============================] - 2s 0us/step
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+ warnings.warn(
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
+ warnings.warn(msg)
+ Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /workspace/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
+ 0%| | 0.00/97.8M [00:00<?, ?B/s] 17%|#6 | 16.5M/97.8M [00:00<00:00, 173MB/s] 42%|####1 | 40.8M/97.8M [00:00<00:00, 220MB/s] 66%|######5 | 64.2M/97.8M [00:00<00:00, 232MB/s] 90%|########9 | 87.8M/97.8M [00:00<00:00, 237MB/s] 100%|##########| 97.8M/97.8M [00:00<00:00, 231MB/s]
-.. GENERATED FROM PYTHON SOURCE LINES 87-91
+.. GENERATED FROM PYTHON SOURCE LINES 98-102
Load Model
----------
Convert a model from any framework to a tvm relay module.
tvmc.load supports models from any framework (like tensorflow saves_model, onnx, tflite ..etc) and auto detects the filetype.
-.. GENERATED FROM PYTHON SOURCE LINES 91-98
+.. GENERATED FROM PYTHON SOURCE LINES 102-112
.. code-block:: default
- tvmc_model = tvmc.load(model_file_name)
+ input_shape = (1, 3, 224, 224) # Batch size, channels, height, width
+
+ # Load the TorchScript model with TVMC
+ tvmc_model = tvmc.load(model_file_name, shape_dict={"input": input_shape}, model_format="pytorch")
print(tvmc_model.mod)
@@ -158,299 +176,357 @@ tvmc.load supports models from any framework (like tensorflow saves_model, onnx,
.. code-block:: none
- def @main(%input_2: Tensor[(1, 224, 224, 3), float32], %v_param_1: Tensor[(7, 7, 3, 64), float32], %v_param_2: Tensor[(64), float32], %v_param_3: Tensor[(64), float32], %v_param_4: Tensor[(64), float32], %v_param_5: Tensor[(64), float32], %v_param_6: Tensor[(64), float32], %v_param_19: Tensor[(1, 1, 64, 256), float32], %v_param_20: Tensor[(256), float32], %v_param_23: Tensor[(256), float32], %v_param_24: Tensor[(256), float32], %v_param_25: Tensor[(256), float32], %v_param_26: Tensor [...]
- %0 = nn.pad(%input_2, 0, pad_width=[[0, 0], [3, 3], [3, 3], [0, 0]]);
- %1 = nn.conv2d(%0, %v_param_1, strides=[2, 2], padding=[0, 0, 0, 0], channels=64, kernel_size=[7, 7], data_layout="NHWC", kernel_layout="HWIO");
- %2 = nn.bias_add(%1, %v_param_2, axis=-1);
- %3 = nn.batch_norm(%2, %v_param_3, %v_param_4, %v_param_5, %v_param_6, axis=3, epsilon=1.001e-05f);
- %4 = %3.0;
- %5 = nn.relu(%4);
- %6 = nn.pad(%5, 0, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]);
- %7 = nn.max_pool2d(%6, pool_size=[3, 3], strides=[2, 2], padding=[0, 0, 0, 0], layout="NHWC");
- %8 = nn.conv2d(%7, %v_param_19, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %9 = nn.bias_add(%8, %v_param_20, axis=-1);
- %10 = nn.batch_norm(%9, %v_param_23, %v_param_24, %v_param_25, %v_param_26, axis=3, epsilon=1.001e-05f);
- %11 = nn.conv2d(%7, %v_param_7, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %12 = nn.bias_add(%11, %v_param_8, axis=-1);
- %13 = nn.batch_norm(%12, %v_param_9, %v_param_10, %v_param_11, %v_param_12, axis=3, epsilon=1.001e-05f);
- %14 = %13.0;
- %15 = nn.relu(%14);
- %16 = nn.conv2d(%15, %v_param_13, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %17 = nn.bias_add(%16, %v_param_14, axis=-1);
- %18 = nn.batch_norm(%17, %v_param_15, %v_param_16, %v_param_17, %v_param_18, axis=3, epsilon=1.001e-05f);
- %19 = %18.0;
- %20 = nn.relu(%19);
- %21 = nn.conv2d(%20, %v_param_21, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %22 = nn.bias_add(%21, %v_param_22, axis=-1);
- %23 = nn.batch_norm(%22, %v_param_27, %v_param_28, %v_param_29, %v_param_30, axis=3, epsilon=1.001e-05f);
- %24 = %10.0;
- %25 = %23.0;
- %26 = add(%24, %25);
- %27 = nn.relu(%26);
- %28 = nn.conv2d(%27, %v_param_31, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %29 = nn.bias_add(%28, %v_param_32, axis=-1);
- %30 = nn.batch_norm(%29, %v_param_33, %v_param_34, %v_param_35, %v_param_36, axis=3, epsilon=1.001e-05f);
- %31 = %30.0;
- %32 = nn.relu(%31);
- %33 = nn.conv2d(%32, %v_param_37, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %34 = nn.bias_add(%33, %v_param_38, axis=-1);
- %35 = nn.batch_norm(%34, %v_param_39, %v_param_40, %v_param_41, %v_param_42, axis=3, epsilon=1.001e-05f);
- %36 = %35.0;
- %37 = nn.relu(%36);
- %38 = nn.conv2d(%37, %v_param_43, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %39 = nn.bias_add(%38, %v_param_44, axis=-1);
- %40 = nn.batch_norm(%39, %v_param_45, %v_param_46, %v_param_47, %v_param_48, axis=3, epsilon=1.001e-05f);
- %41 = %40.0;
- %42 = add(%27, %41);
- %43 = nn.relu(%42);
- %44 = nn.conv2d(%43, %v_param_49, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %45 = nn.bias_add(%44, %v_param_50, axis=-1);
- %46 = nn.batch_norm(%45, %v_param_51, %v_param_52, %v_param_53, %v_param_54, axis=3, epsilon=1.001e-05f);
- %47 = %46.0;
- %48 = nn.relu(%47);
- %49 = nn.conv2d(%48, %v_param_55, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %50 = nn.bias_add(%49, %v_param_56, axis=-1);
- %51 = nn.batch_norm(%50, %v_param_57, %v_param_58, %v_param_59, %v_param_60, axis=3, epsilon=1.001e-05f);
- %52 = %51.0;
- %53 = nn.relu(%52);
- %54 = nn.conv2d(%53, %v_param_61, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %55 = nn.bias_add(%54, %v_param_62, axis=-1);
- %56 = nn.batch_norm(%55, %v_param_63, %v_param_64, %v_param_65, %v_param_66, axis=3, epsilon=1.001e-05f);
- %57 = %56.0;
- %58 = add(%43, %57);
- %59 = nn.relu(%58);
- %60 = nn.conv2d(%59, %v_param_79, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %61 = nn.bias_add(%60, %v_param_80, axis=-1);
- %62 = nn.batch_norm(%61, %v_param_83, %v_param_84, %v_param_85, %v_param_86, axis=3, epsilon=1.001e-05f);
- %63 = nn.conv2d(%59, %v_param_67, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %64 = nn.bias_add(%63, %v_param_68, axis=-1);
- %65 = nn.batch_norm(%64, %v_param_69, %v_param_70, %v_param_71, %v_param_72, axis=3, epsilon=1.001e-05f);
- %66 = %65.0;
- %67 = nn.relu(%66);
- %68 = nn.conv2d(%67, %v_param_73, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %69 = nn.bias_add(%68, %v_param_74, axis=-1);
- %70 = nn.batch_norm(%69, %v_param_75, %v_param_76, %v_param_77, %v_param_78, axis=3, epsilon=1.001e-05f);
- %71 = %70.0;
- %72 = nn.relu(%71);
- %73 = nn.conv2d(%72, %v_param_81, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %74 = nn.bias_add(%73, %v_param_82, axis=-1);
- %75 = nn.batch_norm(%74, %v_param_87, %v_param_88, %v_param_89, %v_param_90, axis=3, epsilon=1.001e-05f);
- %76 = %62.0;
- %77 = %75.0;
- %78 = add(%76, %77);
- %79 = nn.relu(%78);
- %80 = nn.conv2d(%79, %v_param_91, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %81 = nn.bias_add(%80, %v_param_92, axis=-1);
- %82 = nn.batch_norm(%81, %v_param_93, %v_param_94, %v_param_95, %v_param_96, axis=3, epsilon=1.001e-05f);
- %83 = %82.0;
- %84 = nn.relu(%83);
- %85 = nn.conv2d(%84, %v_param_97, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %86 = nn.bias_add(%85, %v_param_98, axis=-1);
- %87 = nn.batch_norm(%86, %v_param_99, %v_param_100, %v_param_101, %v_param_102, axis=3, epsilon=1.001e-05f);
- %88 = %87.0;
- %89 = nn.relu(%88);
- %90 = nn.conv2d(%89, %v_param_103, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %91 = nn.bias_add(%90, %v_param_104, axis=-1);
- %92 = nn.batch_norm(%91, %v_param_105, %v_param_106, %v_param_107, %v_param_108, axis=3, epsilon=1.001e-05f);
- %93 = %92.0;
- %94 = add(%79, %93);
- %95 = nn.relu(%94);
- %96 = nn.conv2d(%95, %v_param_109, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %97 = nn.bias_add(%96, %v_param_110, axis=-1);
- %98 = nn.batch_norm(%97, %v_param_111, %v_param_112, %v_param_113, %v_param_114, axis=3, epsilon=1.001e-05f);
- %99 = %98.0;
- %100 = nn.relu(%99);
- %101 = nn.conv2d(%100, %v_param_115, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %102 = nn.bias_add(%101, %v_param_116, axis=-1);
- %103 = nn.batch_norm(%102, %v_param_117, %v_param_118, %v_param_119, %v_param_120, axis=3, epsilon=1.001e-05f);
- %104 = %103.0;
- %105 = nn.relu(%104);
- %106 = nn.conv2d(%105, %v_param_121, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %107 = nn.bias_add(%106, %v_param_122, axis=-1);
- %108 = nn.batch_norm(%107, %v_param_123, %v_param_124, %v_param_125, %v_param_126, axis=3, epsilon=1.001e-05f);
- %109 = %108.0;
- %110 = add(%95, %109);
- %111 = nn.relu(%110);
- %112 = nn.conv2d(%111, %v_param_127, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %113 = nn.bias_add(%112, %v_param_128, axis=-1);
- %114 = nn.batch_norm(%113, %v_param_129, %v_param_130, %v_param_131, %v_param_132, axis=3, epsilon=1.001e-05f);
- %115 = %114.0;
- %116 = nn.relu(%115);
- %117 = nn.conv2d(%116, %v_param_133, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %118 = nn.bias_add(%117, %v_param_134, axis=-1);
- %119 = nn.batch_norm(%118, %v_param_135, %v_param_136, %v_param_137, %v_param_138, axis=3, epsilon=1.001e-05f);
- %120 = %119.0;
- %121 = nn.relu(%120);
- %122 = nn.conv2d(%121, %v_param_139, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %123 = nn.bias_add(%122, %v_param_140, axis=-1);
- %124 = nn.batch_norm(%123, %v_param_141, %v_param_142, %v_param_143, %v_param_144, axis=3, epsilon=1.001e-05f);
- %125 = %124.0;
- %126 = add(%111, %125);
- %127 = nn.relu(%126);
- %128 = nn.conv2d(%127, %v_param_157, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %129 = nn.bias_add(%128, %v_param_158, axis=-1);
- %130 = nn.batch_norm(%129, %v_param_161, %v_param_162, %v_param_163, %v_param_164, axis=3, epsilon=1.001e-05f);
- %131 = nn.conv2d(%127, %v_param_145, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %132 = nn.bias_add(%131, %v_param_146, axis=-1);
- %133 = nn.batch_norm(%132, %v_param_147, %v_param_148, %v_param_149, %v_param_150, axis=3, epsilon=1.001e-05f);
- %134 = %133.0;
- %135 = nn.relu(%134);
- %136 = nn.conv2d(%135, %v_param_151, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %137 = nn.bias_add(%136, %v_param_152, axis=-1);
- %138 = nn.batch_norm(%137, %v_param_153, %v_param_154, %v_param_155, %v_param_156, axis=3, epsilon=1.001e-05f);
- %139 = %138.0;
- %140 = nn.relu(%139);
- %141 = nn.conv2d(%140, %v_param_159, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %142 = nn.bias_add(%141, %v_param_160, axis=-1);
- %143 = nn.batch_norm(%142, %v_param_165, %v_param_166, %v_param_167, %v_param_168, axis=3, epsilon=1.001e-05f);
- %144 = %130.0;
- %145 = %143.0;
- %146 = add(%144, %145);
- %147 = nn.relu(%146);
- %148 = nn.conv2d(%147, %v_param_169, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %149 = nn.bias_add(%148, %v_param_170, axis=-1);
- %150 = nn.batch_norm(%149, %v_param_171, %v_param_172, %v_param_173, %v_param_174, axis=3, epsilon=1.001e-05f);
- %151 = %150.0;
- %152 = nn.relu(%151);
- %153 = nn.conv2d(%152, %v_param_175, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %154 = nn.bias_add(%153, %v_param_176, axis=-1);
- %155 = nn.batch_norm(%154, %v_param_177, %v_param_178, %v_param_179, %v_param_180, axis=3, epsilon=1.001e-05f);
- %156 = %155.0;
- %157 = nn.relu(%156);
- %158 = nn.conv2d(%157, %v_param_181, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %159 = nn.bias_add(%158, %v_param_182, axis=-1);
- %160 = nn.batch_norm(%159, %v_param_183, %v_param_184, %v_param_185, %v_param_186, axis=3, epsilon=1.001e-05f);
- %161 = %160.0;
- %162 = add(%147, %161);
- %163 = nn.relu(%162);
- %164 = nn.conv2d(%163, %v_param_187, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %165 = nn.bias_add(%164, %v_param_188, axis=-1);
- %166 = nn.batch_norm(%165, %v_param_189, %v_param_190, %v_param_191, %v_param_192, axis=3, epsilon=1.001e-05f);
- %167 = %166.0;
- %168 = nn.relu(%167);
- %169 = nn.conv2d(%168, %v_param_193, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %170 = nn.bias_add(%169, %v_param_194, axis=-1);
- %171 = nn.batch_norm(%170, %v_param_195, %v_param_196, %v_param_197, %v_param_198, axis=3, epsilon=1.001e-05f);
- %172 = %171.0;
- %173 = nn.relu(%172);
- %174 = nn.conv2d(%173, %v_param_199, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %175 = nn.bias_add(%174, %v_param_200, axis=-1);
- %176 = nn.batch_norm(%175, %v_param_201, %v_param_202, %v_param_203, %v_param_204, axis=3, epsilon=1.001e-05f);
- %177 = %176.0;
- %178 = add(%163, %177);
- %179 = nn.relu(%178);
- %180 = nn.conv2d(%179, %v_param_205, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %181 = nn.bias_add(%180, %v_param_206, axis=-1);
- %182 = nn.batch_norm(%181, %v_param_207, %v_param_208, %v_param_209, %v_param_210, axis=3, epsilon=1.001e-05f);
- %183 = %182.0;
- %184 = nn.relu(%183);
- %185 = nn.conv2d(%184, %v_param_211, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %186 = nn.bias_add(%185, %v_param_212, axis=-1);
- %187 = nn.batch_norm(%186, %v_param_213, %v_param_214, %v_param_215, %v_param_216, axis=3, epsilon=1.001e-05f);
- %188 = %187.0;
- %189 = nn.relu(%188);
- %190 = nn.conv2d(%189, %v_param_217, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %191 = nn.bias_add(%190, %v_param_218, axis=-1);
- %192 = nn.batch_norm(%191, %v_param_219, %v_param_220, %v_param_221, %v_param_222, axis=3, epsilon=1.001e-05f);
- %193 = %192.0;
- %194 = add(%179, %193);
- %195 = nn.relu(%194);
- %196 = nn.conv2d(%195, %v_param_223, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %197 = nn.bias_add(%196, %v_param_224, axis=-1);
- %198 = nn.batch_norm(%197, %v_param_225, %v_param_226, %v_param_227, %v_param_228, axis=3, epsilon=1.001e-05f);
- %199 = %198.0;
- %200 = nn.relu(%199);
- %201 = nn.conv2d(%200, %v_param_229, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %202 = nn.bias_add(%201, %v_param_230, axis=-1);
- %203 = nn.batch_norm(%202, %v_param_231, %v_param_232, %v_param_233, %v_param_234, axis=3, epsilon=1.001e-05f);
- %204 = %203.0;
- %205 = nn.relu(%204);
- %206 = nn.conv2d(%205, %v_param_235, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %207 = nn.bias_add(%206, %v_param_236, axis=-1);
- %208 = nn.batch_norm(%207, %v_param_237, %v_param_238, %v_param_239, %v_param_240, axis=3, epsilon=1.001e-05f);
- %209 = %208.0;
- %210 = add(%195, %209);
- %211 = nn.relu(%210);
- %212 = nn.conv2d(%211, %v_param_241, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %213 = nn.bias_add(%212, %v_param_242, axis=-1);
- %214 = nn.batch_norm(%213, %v_param_243, %v_param_244, %v_param_245, %v_param_246, axis=3, epsilon=1.001e-05f);
- %215 = %214.0;
- %216 = nn.relu(%215);
- %217 = nn.conv2d(%216, %v_param_247, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %218 = nn.bias_add(%217, %v_param_248, axis=-1);
- %219 = nn.batch_norm(%218, %v_param_249, %v_param_250, %v_param_251, %v_param_252, axis=3, epsilon=1.001e-05f);
- %220 = %219.0;
- %221 = nn.relu(%220);
- %222 = nn.conv2d(%221, %v_param_253, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %223 = nn.bias_add(%222, %v_param_254, axis=-1);
- %224 = nn.batch_norm(%223, %v_param_255, %v_param_256, %v_param_257, %v_param_258, axis=3, epsilon=1.001e-05f);
- %225 = %224.0;
- %226 = add(%211, %225);
- %227 = nn.relu(%226);
- %228 = nn.conv2d(%227, %v_param_271, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %229 = nn.bias_add(%228, %v_param_272, axis=-1);
- %230 = nn.batch_norm(%229, %v_param_275, %v_param_276, %v_param_277, %v_param_278, axis=3, epsilon=1.001e-05f);
- %231 = nn.conv2d(%227, %v_param_259, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %232 = nn.bias_add(%231, %v_param_260, axis=-1);
- %233 = nn.batch_norm(%232, %v_param_261, %v_param_262, %v_param_263, %v_param_264, axis=3, epsilon=1.001e-05f);
- %234 = %233.0;
- %235 = nn.relu(%234);
- %236 = nn.conv2d(%235, %v_param_265, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %237 = nn.bias_add(%236, %v_param_266, axis=-1);
- %238 = nn.batch_norm(%237, %v_param_267, %v_param_268, %v_param_269, %v_param_270, axis=3, epsilon=1.001e-05f);
- %239 = %238.0;
- %240 = nn.relu(%239);
- %241 = nn.conv2d(%240, %v_param_273, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %242 = nn.bias_add(%241, %v_param_274, axis=-1);
- %243 = nn.batch_norm(%242, %v_param_279, %v_param_280, %v_param_281, %v_param_282, axis=3, epsilon=1.001e-05f);
- %244 = %230.0;
- %245 = %243.0;
- %246 = add(%244, %245);
- %247 = nn.relu(%246);
- %248 = nn.conv2d(%247, %v_param_283, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %249 = nn.bias_add(%248, %v_param_284, axis=-1);
- %250 = nn.batch_norm(%249, %v_param_285, %v_param_286, %v_param_287, %v_param_288, axis=3, epsilon=1.001e-05f);
- %251 = %250.0;
- %252 = nn.relu(%251);
- %253 = nn.conv2d(%252, %v_param_289, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %254 = nn.bias_add(%253, %v_param_290, axis=-1);
- %255 = nn.batch_norm(%254, %v_param_291, %v_param_292, %v_param_293, %v_param_294, axis=3, epsilon=1.001e-05f);
- %256 = %255.0;
- %257 = nn.relu(%256);
- %258 = nn.conv2d(%257, %v_param_295, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %259 = nn.bias_add(%258, %v_param_296, axis=-1);
- %260 = nn.batch_norm(%259, %v_param_297, %v_param_298, %v_param_299, %v_param_300, axis=3, epsilon=1.001e-05f);
- %261 = %260.0;
- %262 = add(%247, %261);
- %263 = nn.relu(%262);
- %264 = nn.conv2d(%263, %v_param_301, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %265 = nn.bias_add(%264, %v_param_302, axis=-1);
- %266 = nn.batch_norm(%265, %v_param_303, %v_param_304, %v_param_305, %v_param_306, axis=3, epsilon=1.001e-05f);
- %267 = %266.0;
- %268 = nn.relu(%267);
- %269 = nn.conv2d(%268, %v_param_307, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %270 = nn.bias_add(%269, %v_param_308, axis=-1);
- %271 = nn.batch_norm(%270, %v_param_309, %v_param_310, %v_param_311, %v_param_312, axis=3, epsilon=1.001e-05f);
- %272 = %271.0;
- %273 = nn.relu(%272);
- %274 = nn.conv2d(%273, %v_param_313, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %275 = nn.bias_add(%274, %v_param_314, axis=-1);
- %276 = nn.batch_norm(%275, %v_param_315, %v_param_316, %v_param_317, %v_param_318, axis=3, epsilon=1.001e-05f);
- %277 = %276.0;
- %278 = add(%263, %277);
- %279 = nn.relu(%278);
- %280 = nn.global_avg_pool2d(%279, layout="NHWC");
- %281 = nn.batch_flatten(%280);
- %282 = nn.dense(%281, %v_param_319, units=1000);
- %283 = nn.bias_add(%282, %v_param_320);
- nn.softmax(%283)
+ type List[A] {
+ Cons(A, List[A]),
+ Nil,
+ }
+
+ type Option[A] {
+ Some(A),
+ None,
+ }
+
+ type Tree[A] {
+ Rose(A, List[Tree[A]]),
}
+ type tensor_float16_t {
+ tensor_nil_float16,
+ tensor0_float16(float16),
+ tensor1_float16(Tensor[(?), float16]),
+ tensor2_float16(Tensor[(?, ?), float16]),
+ tensor3_float16(Tensor[(?, ?, ?), float16]),
+ tensor4_float16(Tensor[(?, ?, ?, ?), float16]),
+ tensor5_float16(Tensor[(?, ?, ?, ?, ?), float16]),
+ tensor6_float16(Tensor[(?, ?, ?, ?, ?, ?), float16]),
+ }
+
+ type tensor_float32_t {
+ tensor_nil_float32,
+ tensor0_float32(float32),
+ tensor1_float32(Tensor[(?), float32]),
+ tensor2_float32(Tensor[(?, ?), float32]),
+ tensor3_float32(Tensor[(?, ?, ?), float32]),
+ tensor4_float32(Tensor[(?, ?, ?, ?), float32]),
+ tensor5_float32(Tensor[(?, ?, ?, ?, ?), float32]),
+ tensor6_float32(Tensor[(?, ?, ?, ?, ?, ?), float32]),
+ }
+ type tensor_float64_t {
+ tensor_nil_float64,
+ tensor0_float64(float64),
+ tensor1_float64(Tensor[(?), float64]),
+ tensor2_float64(Tensor[(?, ?), float64]),
+ tensor3_float64(Tensor[(?, ?, ?), float64]),
+ tensor4_float64(Tensor[(?, ?, ?, ?), float64]),
+ tensor5_float64(Tensor[(?, ?, ?, ?, ?), float64]),
+ tensor6_float64(Tensor[(?, ?, ?, ?, ?, ?), float64]),
+ }
+ type tensor_int16_t {
+ tensor_nil_int16,
+ tensor0_int16(int16),
+ tensor1_int16(Tensor[(?), int16]),
+ tensor2_int16(Tensor[(?, ?), int16]),
+ tensor3_int16(Tensor[(?, ?, ?), int16]),
+ tensor4_int16(Tensor[(?, ?, ?, ?), int16]),
+ tensor5_int16(Tensor[(?, ?, ?, ?, ?), int16]),
+ tensor6_int16(Tensor[(?, ?, ?, ?, ?, ?), int16]),
+ }
+ type tensor_int32_t {
+ tensor_nil_int32,
+ tensor0_int32(int32),
+ tensor1_int32(Tensor[(?), int32]),
+ tensor2_int32(Tensor[(?, ?), int32]),
+ tensor3_int32(Tensor[(?, ?, ?), int32]),
+ tensor4_int32(Tensor[(?, ?, ?, ?), int32]),
+ tensor5_int32(Tensor[(?, ?, ?, ?, ?), int32]),
+ tensor6_int32(Tensor[(?, ?, ?, ?, ?, ?), int32]),
+ }
-.. GENERATED FROM PYTHON SOURCE LINES 99-104
+ type tensor_int64_t {
+ tensor_nil_int64,
+ tensor0_int64(int64),
+ tensor1_int64(Tensor[(?), int64]),
+ tensor2_int64(Tensor[(?, ?), int64]),
+ tensor3_int64(Tensor[(?, ?, ?), int64]),
+ tensor4_int64(Tensor[(?, ?, ?, ?), int64]),
+ tensor5_int64(Tensor[(?, ?, ?, ?, ?), int64]),
+ tensor6_int64(Tensor[(?, ?, ?, ?, ?, ?), int64]),
+ }
+
+ type tensor_int8_t {
+ tensor_nil_int8,
+ tensor0_int8(int8),
+ tensor1_int8(Tensor[(?), int8]),
+ tensor2_int8(Tensor[(?, ?), int8]),
+ tensor3_int8(Tensor[(?, ?, ?), int8]),
+ tensor4_int8(Tensor[(?, ?, ?, ?), int8]),
+ tensor5_int8(Tensor[(?, ?, ?, ?, ?), int8]),
+ tensor6_int8(Tensor[(?, ?, ?, ?, ?, ?), int8]),
+ }
+
+ type tensor_uint16_t {
+ tensor_nil_uint16,
+ tensor0_uint16(uint16),
+ tensor1_uint16(Tensor[(?), uint16]),
+ tensor2_uint16(Tensor[(?, ?), uint16]),
+ tensor3_uint16(Tensor[(?, ?, ?), uint16]),
+ tensor4_uint16(Tensor[(?, ?, ?, ?), uint16]),
+ tensor5_uint16(Tensor[(?, ?, ?, ?, ?), uint16]),
+ tensor6_uint16(Tensor[(?, ?, ?, ?, ?, ?), uint16]),
+ }
+
+ type tensor_uint8_t {
+ tensor_nil_uint8,
+ tensor0_uint8(uint8),
+ tensor1_uint8(Tensor[(?), uint8]),
+ tensor2_uint8(Tensor[(?, ?), uint8]),
+ tensor3_uint8(Tensor[(?, ?, ?), uint8]),
+ tensor4_uint8(Tensor[(?, ?, ?, ?), uint8]),
+ tensor5_uint8(Tensor[(?, ?, ?, ?, ?), uint8]),
+ tensor6_uint8(Tensor[(?, ?, ?, ?, ?, ?), uint8]),
+ }
+
+ def @main(%input: Tensor[(1, 3, 224, 224), float32] /* span=aten::_convolution_0.input:0:0 */, %aten::_convolution_0.weight: Tensor[(64, 3, 7, 7), float32] /* span=aten::_convolution_0.weight:0:0 */, %aten::batch_norm_0.weight: Tensor[(64), float32] /* span=aten::batch_norm_0.weight:0:0 */, %aten::batch_norm_0.bias: Tensor[(64), float32] /* span=aten::batch_norm_0.bias:0:0 */, %aten::batch_norm_0.running_mean: Tensor[(64), float32] /* span=aten::batch_norm_0.running_mean:0:0 */, %ate [...]
+ %0 = nn.conv2d(%input, %aten::_convolution_0.weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) /* span=aten::_convolution_0:0:0 */;
+ %1 = nn.batch_norm(%0, %aten::batch_norm_0.weight, %aten::batch_norm_0.bias, %aten::batch_norm_0.running_mean, %aten::batch_norm_0.running_var) /* span=aten::batch_norm_0:0:0 */;
+ %2 = %1.0 /* span=aten::batch_norm_0:0:0 */;
+ %3 = nn.relu(%2) /* span=aten::relu__0:0:0 */;
+ %4 = nn.max_pool2d(%3, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 1]) /* span=aten::max_pool2d_0:0:0 */;
+ %5 = nn.conv2d(%4, %aten::_convolution_1.weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* span=aten::_convolution_1:0:0 */;
+ %6 = nn.batch_norm(%5, %aten::batch_norm_1.weight, %aten::batch_norm_1.bias, %aten::batch_norm_1.running_mean, %aten::batch_norm_1.running_var) /* span=aten::batch_norm_1:0:0 */;
+ %7 = %6.0 /* span=aten::batch_norm_1:0:0 */;
+ %8 = nn.relu(%7) /* span=aten::relu__1:0:0 */;
+ %9 = nn.conv2d(%8, %aten::_convolution_2.weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* span=aten::_convolution_2:0:0 */;
+ %10 = nn.batch_norm(%9, %aten::batch_norm_2.weight, %aten::batch_norm_2.bias, %aten::batch_norm_2.running_mean, %aten::batch_norm_2.running_var) /* span=aten::batch_norm_2:0:0 */;
+ %11 = %10.0 /* span=aten::batch_norm_2:0:0 */;
+ %12 = nn.relu(%11) /* span=aten::relu__2:0:0 */;
+ %13 = nn.conv2d(%12, %aten::_convolution_3.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_3:0:0 */;
+ %14 = nn.batch_norm(%13, %aten::batch_norm_3.weight, %aten::batch_norm_3.bias, %aten::batch_norm_3.running_mean, %aten::batch_norm_3.running_var) /* span=aten::batch_norm_3:0:0 */;
+ %15 = nn.conv2d(%4, %aten::_convolution_4.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_4:0:0 */;
+ %16 = nn.batch_norm(%15, %aten::batch_norm_4.weight, %aten::batch_norm_4.bias, %aten::batch_norm_4.running_mean, %aten::batch_norm_4.running_var) /* span=aten::batch_norm_4:0:0 */;
+ %17 = %14.0 /* span=aten::batch_norm_3:0:0 */;
+ %18 = %16.0 /* span=aten::batch_norm_4:0:0 */;
+ %19 = add(%17, %18) /* span=aten::add__0:0:0 */;
+ %20 = nn.relu(%19) /* span=aten::relu__3:0:0 */;
+ %21 = nn.conv2d(%20, %aten::_convolution_5.weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* span=aten::_convolution_5:0:0 */;
+ %22 = nn.batch_norm(%21, %aten::batch_norm_5.weight, %aten::batch_norm_5.bias, %aten::batch_norm_5.running_mean, %aten::batch_norm_5.running_var) /* span=aten::batch_norm_5:0:0 */;
+ %23 = %22.0 /* span=aten::batch_norm_5:0:0 */;
+ %24 = nn.relu(%23) /* span=aten::relu__4:0:0 */;
+ %25 = nn.conv2d(%24, %aten::_convolution_6.weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* span=aten::_convolution_6:0:0 */;
+ %26 = nn.batch_norm(%25, %aten::batch_norm_6.weight, %aten::batch_norm_6.bias, %aten::batch_norm_6.running_mean, %aten::batch_norm_6.running_var) /* span=aten::batch_norm_6:0:0 */;
+ %27 = %26.0 /* span=aten::batch_norm_6:0:0 */;
+ %28 = nn.relu(%27) /* span=aten::relu__5:0:0 */;
+ %29 = nn.conv2d(%28, %aten::_convolution_7.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_7:0:0 */;
+ %30 = nn.batch_norm(%29, %aten::batch_norm_7.weight, %aten::batch_norm_7.bias, %aten::batch_norm_7.running_mean, %aten::batch_norm_7.running_var) /* span=aten::batch_norm_7:0:0 */;
+ %31 = %30.0 /* span=aten::batch_norm_7:0:0 */;
+ %32 = add(%31, %20) /* span=aten::add__1:0:0 */;
+ %33 = nn.relu(%32) /* span=aten::relu__6:0:0 */;
+ %34 = nn.conv2d(%33, %aten::_convolution_8.weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* span=aten::_convolution_8:0:0 */;
+ %35 = nn.batch_norm(%34, %aten::batch_norm_8.weight, %aten::batch_norm_8.bias, %aten::batch_norm_8.running_mean, %aten::batch_norm_8.running_var) /* span=aten::batch_norm_8:0:0 */;
+ %36 = %35.0 /* span=aten::batch_norm_8:0:0 */;
+ %37 = nn.relu(%36) /* span=aten::relu__7:0:0 */;
+ %38 = nn.conv2d(%37, %aten::_convolution_9.weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* span=aten::_convolution_9:0:0 */;
+ %39 = nn.batch_norm(%38, %aten::batch_norm_9.weight, %aten::batch_norm_9.bias, %aten::batch_norm_9.running_mean, %aten::batch_norm_9.running_var) /* span=aten::batch_norm_9:0:0 */;
+ %40 = %39.0 /* span=aten::batch_norm_9:0:0 */;
+ %41 = nn.relu(%40) /* span=aten::relu__8:0:0 */;
+ %42 = nn.conv2d(%41, %aten::_convolution_10.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_10:0:0 */;
+ %43 = nn.batch_norm(%42, %aten::batch_norm_10.weight, %aten::batch_norm_10.bias, %aten::batch_norm_10.running_mean, %aten::batch_norm_10.running_var) /* span=aten::batch_norm_10:0:0 */;
+ %44 = %43.0 /* span=aten::batch_norm_10:0:0 */;
+ %45 = add(%44, %33) /* span=aten::add__2:0:0 */;
+ %46 = nn.relu(%45) /* span=aten::relu__9:0:0 */;
+ %47 = nn.conv2d(%46, %aten::_convolution_11.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_11:0:0 */;
+ %48 = nn.batch_norm(%47, %aten::batch_norm_11.weight, %aten::batch_norm_11.bias, %aten::batch_norm_11.running_mean, %aten::batch_norm_11.running_var) /* span=aten::batch_norm_11:0:0 */;
+ %49 = %48.0 /* span=aten::batch_norm_11:0:0 */;
+ %50 = nn.relu(%49) /* span=aten::relu__10:0:0 */;
+ %51 = nn.conv2d(%50, %aten::_convolution_12.weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_12:0:0 */;
+ %52 = nn.batch_norm(%51, %aten::batch_norm_12.weight, %aten::batch_norm_12.bias, %aten::batch_norm_12.running_mean, %aten::batch_norm_12.running_var) /* span=aten::batch_norm_12:0:0 */;
+ %53 = %52.0 /* span=aten::batch_norm_12:0:0 */;
+ %54 = nn.relu(%53) /* span=aten::relu__11:0:0 */;
+ %55 = nn.conv2d(%54, %aten::_convolution_13.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_13:0:0 */;
+ %56 = nn.batch_norm(%55, %aten::batch_norm_13.weight, %aten::batch_norm_13.bias, %aten::batch_norm_13.running_mean, %aten::batch_norm_13.running_var) /* span=aten::batch_norm_13:0:0 */;
+ %57 = nn.conv2d(%46, %aten::_convolution_14.weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_14:0:0 */;
+ %58 = nn.batch_norm(%57, %aten::batch_norm_14.weight, %aten::batch_norm_14.bias, %aten::batch_norm_14.running_mean, %aten::batch_norm_14.running_var) /* span=aten::batch_norm_14:0:0 */;
+ %59 = %56.0 /* span=aten::batch_norm_13:0:0 */;
+ %60 = %58.0 /* span=aten::batch_norm_14:0:0 */;
+ %61 = add(%59, %60) /* span=aten::add__3:0:0 */;
+ %62 = nn.relu(%61) /* span=aten::relu__12:0:0 */;
+ %63 = nn.conv2d(%62, %aten::_convolution_15.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_15:0:0 */;
+ %64 = nn.batch_norm(%63, %aten::batch_norm_15.weight, %aten::batch_norm_15.bias, %aten::batch_norm_15.running_mean, %aten::batch_norm_15.running_var) /* span=aten::batch_norm_15:0:0 */;
+ %65 = %64.0 /* span=aten::batch_norm_15:0:0 */;
+ %66 = nn.relu(%65) /* span=aten::relu__13:0:0 */;
+ %67 = nn.conv2d(%66, %aten::_convolution_16.weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_16:0:0 */;
+ %68 = nn.batch_norm(%67, %aten::batch_norm_16.weight, %aten::batch_norm_16.bias, %aten::batch_norm_16.running_mean, %aten::batch_norm_16.running_var) /* span=aten::batch_norm_16:0:0 */;
+ %69 = %68.0 /* span=aten::batch_norm_16:0:0 */;
+ %70 = nn.relu(%69) /* span=aten::relu__14:0:0 */;
+ %71 = nn.conv2d(%70, %aten::_convolution_17.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_17:0:0 */;
+ %72 = nn.batch_norm(%71, %aten::batch_norm_17.weight, %aten::batch_norm_17.bias, %aten::batch_norm_17.running_mean, %aten::batch_norm_17.running_var) /* span=aten::batch_norm_17:0:0 */;
+ %73 = %72.0 /* span=aten::batch_norm_17:0:0 */;
+ %74 = add(%73, %62) /* span=aten::add__4:0:0 */;
+ %75 = nn.relu(%74) /* span=aten::relu__15:0:0 */;
+ %76 = nn.conv2d(%75, %aten::_convolution_18.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_18:0:0 */;
+ %77 = nn.batch_norm(%76, %aten::batch_norm_18.weight, %aten::batch_norm_18.bias, %aten::batch_norm_18.running_mean, %aten::batch_norm_18.running_var) /* span=aten::batch_norm_18:0:0 */;
+ %78 = %77.0 /* span=aten::batch_norm_18:0:0 */;
+ %79 = nn.relu(%78) /* span=aten::relu__16:0:0 */;
+ %80 = nn.conv2d(%79, %aten::_convolution_19.weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_19:0:0 */;
+ %81 = nn.batch_norm(%80, %aten::batch_norm_19.weight, %aten::batch_norm_19.bias, %aten::batch_norm_19.running_mean, %aten::batch_norm_19.running_var) /* span=aten::batch_norm_19:0:0 */;
+ %82 = %81.0 /* span=aten::batch_norm_19:0:0 */;
+ %83 = nn.relu(%82) /* span=aten::relu__17:0:0 */;
+ %84 = nn.conv2d(%83, %aten::_convolution_20.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_20:0:0 */;
+ %85 = nn.batch_norm(%84, %aten::batch_norm_20.weight, %aten::batch_norm_20.bias, %aten::batch_norm_20.running_mean, %aten::batch_norm_20.running_var) /* span=aten::batch_norm_20:0:0 */;
+ %86 = %85.0 /* span=aten::batch_norm_20:0:0 */;
+ %87 = add(%86, %75) /* span=aten::add__5:0:0 */;
+ %88 = nn.relu(%87) /* span=aten::relu__18:0:0 */;
+ %89 = nn.conv2d(%88, %aten::_convolution_21.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_21:0:0 */;
+ %90 = nn.batch_norm(%89, %aten::batch_norm_21.weight, %aten::batch_norm_21.bias, %aten::batch_norm_21.running_mean, %aten::batch_norm_21.running_var) /* span=aten::batch_norm_21:0:0 */;
+ %91 = %90.0 /* span=aten::batch_norm_21:0:0 */;
+ %92 = nn.relu(%91) /* span=aten::relu__19:0:0 */;
+ %93 = nn.conv2d(%92, %aten::_convolution_22.weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_22:0:0 */;
+ %94 = nn.batch_norm(%93, %aten::batch_norm_22.weight, %aten::batch_norm_22.bias, %aten::batch_norm_22.running_mean, %aten::batch_norm_22.running_var) /* span=aten::batch_norm_22:0:0 */;
+ %95 = %94.0 /* span=aten::batch_norm_22:0:0 */;
+ %96 = nn.relu(%95) /* span=aten::relu__20:0:0 */;
+ %97 = nn.conv2d(%96, %aten::_convolution_23.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_23:0:0 */;
+ %98 = nn.batch_norm(%97, %aten::batch_norm_23.weight, %aten::batch_norm_23.bias, %aten::batch_norm_23.running_mean, %aten::batch_norm_23.running_var) /* span=aten::batch_norm_23:0:0 */;
+ %99 = %98.0 /* span=aten::batch_norm_23:0:0 */;
+ %100 = add(%99, %88) /* span=aten::add__6:0:0 */;
+ %101 = nn.relu(%100) /* span=aten::relu__21:0:0 */;
+ %102 = nn.conv2d(%101, %aten::_convolution_24.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_24:0:0 */;
+ %103 = nn.batch_norm(%102, %aten::batch_norm_24.weight, %aten::batch_norm_24.bias, %aten::batch_norm_24.running_mean, %aten::batch_norm_24.running_var) /* span=aten::batch_norm_24:0:0 */;
+ %104 = %103.0 /* span=aten::batch_norm_24:0:0 */;
+ %105 = nn.relu(%104) /* span=aten::relu__22:0:0 */;
+ %106 = nn.conv2d(%105, %aten::_convolution_25.weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_25:0:0 */;
+ %107 = nn.batch_norm(%106, %aten::batch_norm_25.weight, %aten::batch_norm_25.bias, %aten::batch_norm_25.running_mean, %aten::batch_norm_25.running_var) /* span=aten::batch_norm_25:0:0 */;
+ %108 = %107.0 /* span=aten::batch_norm_25:0:0 */;
+ %109 = nn.relu(%108) /* span=aten::relu__23:0:0 */;
+ %110 = nn.conv2d(%109, %aten::_convolution_26.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_26:0:0 */;
+ %111 = nn.batch_norm(%110, %aten::batch_norm_26.weight, %aten::batch_norm_26.bias, %aten::batch_norm_26.running_mean, %aten::batch_norm_26.running_var) /* span=aten::batch_norm_26:0:0 */;
+ %112 = nn.conv2d(%101, %aten::_convolution_27.weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_27:0:0 */;
+ %113 = nn.batch_norm(%112, %aten::batch_norm_27.weight, %aten::batch_norm_27.bias, %aten::batch_norm_27.running_mean, %aten::batch_norm_27.running_var) /* span=aten::batch_norm_27:0:0 */;
+ %114 = %111.0 /* span=aten::batch_norm_26:0:0 */;
+ %115 = %113.0 /* span=aten::batch_norm_27:0:0 */;
+ %116 = add(%114, %115) /* span=aten::add__7:0:0 */;
+ %117 = nn.relu(%116) /* span=aten::relu__24:0:0 */;
+ %118 = nn.conv2d(%117, %aten::_convolution_28.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_28:0:0 */;
+ %119 = nn.batch_norm(%118, %aten::batch_norm_28.weight, %aten::batch_norm_28.bias, %aten::batch_norm_28.running_mean, %aten::batch_norm_28.running_var) /* span=aten::batch_norm_28:0:0 */;
+ %120 = %119.0 /* span=aten::batch_norm_28:0:0 */;
+ %121 = nn.relu(%120) /* span=aten::relu__25:0:0 */;
+ %122 = nn.conv2d(%121, %aten::_convolution_29.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_29:0:0 */;
+ %123 = nn.batch_norm(%122, %aten::batch_norm_29.weight, %aten::batch_norm_29.bias, %aten::batch_norm_29.running_mean, %aten::batch_norm_29.running_var) /* span=aten::batch_norm_29:0:0 */;
+ %124 = %123.0 /* span=aten::batch_norm_29:0:0 */;
+ %125 = nn.relu(%124) /* span=aten::relu__26:0:0 */;
+ %126 = nn.conv2d(%125, %aten::_convolution_30.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_30:0:0 */;
+ %127 = nn.batch_norm(%126, %aten::batch_norm_30.weight, %aten::batch_norm_30.bias, %aten::batch_norm_30.running_mean, %aten::batch_norm_30.running_var) /* span=aten::batch_norm_30:0:0 */;
+ %128 = %127.0 /* span=aten::batch_norm_30:0:0 */;
+ %129 = add(%128, %117) /* span=aten::add__8:0:0 */;
+ %130 = nn.relu(%129) /* span=aten::relu__27:0:0 */;
+ %131 = nn.conv2d(%130, %aten::_convolution_31.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_31:0:0 */;
+ %132 = nn.batch_norm(%131, %aten::batch_norm_31.weight, %aten::batch_norm_31.bias, %aten::batch_norm_31.running_mean, %aten::batch_norm_31.running_var) /* span=aten::batch_norm_31:0:0 */;
+ %133 = %132.0 /* span=aten::batch_norm_31:0:0 */;
+ %134 = nn.relu(%133) /* span=aten::relu__28:0:0 */;
+ %135 = nn.conv2d(%134, %aten::_convolution_32.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_32:0:0 */;
+ %136 = nn.batch_norm(%135, %aten::batch_norm_32.weight, %aten::batch_norm_32.bias, %aten::batch_norm_32.running_mean, %aten::batch_norm_32.running_var) /* span=aten::batch_norm_32:0:0 */;
+ %137 = %136.0 /* span=aten::batch_norm_32:0:0 */;
+ %138 = nn.relu(%137) /* span=aten::relu__29:0:0 */;
+ %139 = nn.conv2d(%138, %aten::_convolution_33.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_33:0:0 */;
+ %140 = nn.batch_norm(%139, %aten::batch_norm_33.weight, %aten::batch_norm_33.bias, %aten::batch_norm_33.running_mean, %aten::batch_norm_33.running_var) /* span=aten::batch_norm_33:0:0 */;
+ %141 = %140.0 /* span=aten::batch_norm_33:0:0 */;
+ %142 = add(%141, %130) /* span=aten::add__9:0:0 */;
+ %143 = nn.relu(%142) /* span=aten::relu__30:0:0 */;
+ %144 = nn.conv2d(%143, %aten::_convolution_34.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_34:0:0 */;
+ %145 = nn.batch_norm(%144, %aten::batch_norm_34.weight, %aten::batch_norm_34.bias, %aten::batch_norm_34.running_mean, %aten::batch_norm_34.running_var) /* span=aten::batch_norm_34:0:0 */;
+ %146 = %145.0 /* span=aten::batch_norm_34:0:0 */;
+ %147 = nn.relu(%146) /* span=aten::relu__31:0:0 */;
+ %148 = nn.conv2d(%147, %aten::_convolution_35.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_35:0:0 */;
+ %149 = nn.batch_norm(%148, %aten::batch_norm_35.weight, %aten::batch_norm_35.bias, %aten::batch_norm_35.running_mean, %aten::batch_norm_35.running_var) /* span=aten::batch_norm_35:0:0 */;
+ %150 = %149.0 /* span=aten::batch_norm_35:0:0 */;
+ %151 = nn.relu(%150) /* span=aten::relu__32:0:0 */;
+ %152 = nn.conv2d(%151, %aten::_convolution_36.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_36:0:0 */;
+ %153 = nn.batch_norm(%152, %aten::batch_norm_36.weight, %aten::batch_norm_36.bias, %aten::batch_norm_36.running_mean, %aten::batch_norm_36.running_var) /* span=aten::batch_norm_36:0:0 */;
+ %154 = %153.0 /* span=aten::batch_norm_36:0:0 */;
+ %155 = add(%154, %143) /* span=aten::add__10:0:0 */;
+ %156 = nn.relu(%155) /* span=aten::relu__33:0:0 */;
+ %157 = nn.conv2d(%156, %aten::_convolution_37.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_37:0:0 */;
+ %158 = nn.batch_norm(%157, %aten::batch_norm_37.weight, %aten::batch_norm_37.bias, %aten::batch_norm_37.running_mean, %aten::batch_norm_37.running_var) /* span=aten::batch_norm_37:0:0 */;
+ %159 = %158.0 /* span=aten::batch_norm_37:0:0 */;
+ %160 = nn.relu(%159) /* span=aten::relu__34:0:0 */;
+ %161 = nn.conv2d(%160, %aten::_convolution_38.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_38:0:0 */;
+ %162 = nn.batch_norm(%161, %aten::batch_norm_38.weight, %aten::batch_norm_38.bias, %aten::batch_norm_38.running_mean, %aten::batch_norm_38.running_var) /* span=aten::batch_norm_38:0:0 */;
+ %163 = %162.0 /* span=aten::batch_norm_38:0:0 */;
+ %164 = nn.relu(%163) /* span=aten::relu__35:0:0 */;
+ %165 = nn.conv2d(%164, %aten::_convolution_39.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_39:0:0 */;
+ %166 = nn.batch_norm(%165, %aten::batch_norm_39.weight, %aten::batch_norm_39.bias, %aten::batch_norm_39.running_mean, %aten::batch_norm_39.running_var) /* span=aten::batch_norm_39:0:0 */;
+ %167 = %166.0 /* span=aten::batch_norm_39:0:0 */;
+ %168 = add(%167, %156) /* span=aten::add__11:0:0 */;
+ %169 = nn.relu(%168) /* span=aten::relu__36:0:0 */;
+ %170 = nn.conv2d(%169, %aten::_convolution_40.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_40:0:0 */;
+ %171 = nn.batch_norm(%170, %aten::batch_norm_40.weight, %aten::batch_norm_40.bias, %aten::batch_norm_40.running_mean, %aten::batch_norm_40.running_var) /* span=aten::batch_norm_40:0:0 */;
+ %172 = %171.0 /* span=aten::batch_norm_40:0:0 */;
+ %173 = nn.relu(%172) /* span=aten::relu__37:0:0 */;
+ %174 = nn.conv2d(%173, %aten::_convolution_41.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_41:0:0 */;
+ %175 = nn.batch_norm(%174, %aten::batch_norm_41.weight, %aten::batch_norm_41.bias, %aten::batch_norm_41.running_mean, %aten::batch_norm_41.running_var) /* span=aten::batch_norm_41:0:0 */;
+ %176 = %175.0 /* span=aten::batch_norm_41:0:0 */;
+ %177 = nn.relu(%176) /* span=aten::relu__38:0:0 */;
+ %178 = nn.conv2d(%177, %aten::_convolution_42.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_42:0:0 */;
+ %179 = nn.batch_norm(%178, %aten::batch_norm_42.weight, %aten::batch_norm_42.bias, %aten::batch_norm_42.running_mean, %aten::batch_norm_42.running_var) /* span=aten::batch_norm_42:0:0 */;
+ %180 = %179.0 /* span=aten::batch_norm_42:0:0 */;
+ %181 = add(%180, %169) /* span=aten::add__12:0:0 */;
+ %182 = nn.relu(%181) /* span=aten::relu__39:0:0 */;
+ %183 = nn.conv2d(%182, %aten::_convolution_43.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_43:0:0 */;
+ %184 = nn.batch_norm(%183, %aten::batch_norm_43.weight, %aten::batch_norm_43.bias, %aten::batch_norm_43.running_mean, %aten::batch_norm_43.running_var) /* span=aten::batch_norm_43:0:0 */;
+ %185 = %184.0 /* span=aten::batch_norm_43:0:0 */;
+ %186 = nn.relu(%185) /* span=aten::relu__40:0:0 */;
+ %187 = nn.conv2d(%186, %aten::_convolution_44.weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* span=aten::_convolution_44:0:0 */;
+ %188 = nn.batch_norm(%187, %aten::batch_norm_44.weight, %aten::batch_norm_44.bias, %aten::batch_norm_44.running_mean, %aten::batch_norm_44.running_var) /* span=aten::batch_norm_44:0:0 */;
+ %189 = %188.0 /* span=aten::batch_norm_44:0:0 */;
+ %190 = nn.relu(%189) /* span=aten::relu__41:0:0 */;
+ %191 = nn.conv2d(%190, %aten::_convolution_45.weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_45:0:0 */;
+ %192 = nn.batch_norm(%191, %aten::batch_norm_45.weight, %aten::batch_norm_45.bias, %aten::batch_norm_45.running_mean, %aten::batch_norm_45.running_var) /* span=aten::batch_norm_45:0:0 */;
+ %193 = nn.conv2d(%182, %aten::_convolution_46.weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_46:0:0 */;
+ %194 = nn.batch_norm(%193, %aten::batch_norm_46.weight, %aten::batch_norm_46.bias, %aten::batch_norm_46.running_mean, %aten::batch_norm_46.running_var) /* span=aten::batch_norm_46:0:0 */;
+ %195 = %192.0 /* span=aten::batch_norm_45:0:0 */;
+ %196 = %194.0 /* span=aten::batch_norm_46:0:0 */;
+ %197 = add(%195, %196) /* span=aten::add__13:0:0 */;
+ %198 = nn.relu(%197) /* span=aten::relu__42:0:0 */;
+ %199 = nn.conv2d(%198, %aten::_convolution_47.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_47:0:0 */;
+ %200 = nn.batch_norm(%199, %aten::batch_norm_47.weight, %aten::batch_norm_47.bias, %aten::batch_norm_47.running_mean, %aten::batch_norm_47.running_var) /* span=aten::batch_norm_47:0:0 */;
+ %201 = %200.0 /* span=aten::batch_norm_47:0:0 */;
+ %202 = nn.relu(%201) /* span=aten::relu__43:0:0 */;
+ %203 = nn.conv2d(%202, %aten::_convolution_48.weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* span=aten::_convolution_48:0:0 */;
+ %204 = nn.batch_norm(%203, %aten::batch_norm_48.weight, %aten::batch_norm_48.bias, %aten::batch_norm_48.running_mean, %aten::batch_norm_48.running_var) /* span=aten::batch_norm_48:0:0 */;
+ %205 = %204.0 /* span=aten::batch_norm_48:0:0 */;
+ %206 = nn.relu(%205) /* span=aten::relu__44:0:0 */;
+ %207 = nn.conv2d(%206, %aten::_convolution_49.weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_49:0:0 */;
+ %208 = nn.batch_norm(%207, %aten::batch_norm_49.weight, %aten::batch_norm_49.bias, %aten::batch_norm_49.running_mean, %aten::batch_norm_49.running_var) /* span=aten::batch_norm_49:0:0 */;
+ %209 = %208.0 /* span=aten::batch_norm_49:0:0 */;
+ %210 = add(%209, %198) /* span=aten::add__14:0:0 */;
+ %211 = nn.relu(%210) /* span=aten::relu__45:0:0 */;
+ %212 = nn.conv2d(%211, %aten::_convolution_50.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_50:0:0 */;
+ %213 = nn.batch_norm(%212, %aten::batch_norm_50.weight, %aten::batch_norm_50.bias, %aten::batch_norm_50.running_mean, %aten::batch_norm_50.running_var) /* span=aten::batch_norm_50:0:0 */;
+ %214 = %213.0 /* span=aten::batch_norm_50:0:0 */;
+ %215 = nn.relu(%214) /* span=aten::relu__46:0:0 */;
+ %216 = nn.conv2d(%215, %aten::_convolution_51.weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* span=aten::_convolution_51:0:0 */;
+ %217 = nn.batch_norm(%216, %aten::batch_norm_51.weight, %aten::batch_norm_51.bias, %aten::batch_norm_51.running_mean, %aten::batch_norm_51.running_var) /* span=aten::batch_norm_51:0:0 */;
+ %218 = %217.0 /* span=aten::batch_norm_51:0:0 */;
+ %219 = nn.relu(%218) /* span=aten::relu__47:0:0 */;
+ %220 = nn.conv2d(%219, %aten::_convolution_52.weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_52:0:0 */;
+ %221 = nn.batch_norm(%220, %aten::batch_norm_52.weight, %aten::batch_norm_52.bias, %aten::batch_norm_52.running_mean, %aten::batch_norm_52.running_var) /* span=aten::batch_norm_52:0:0 */;
+ %222 = %221.0 /* span=aten::batch_norm_52:0:0 */;
+ %223 = add(%222, %211) /* span=aten::add__15:0:0 */;
+ %224 = nn.relu(%223) /* span=aten::relu__48:0:0 */;
+ %225 = nn.adaptive_avg_pool2d(%224, output_size=[1, 1]) /* span=aten::adaptive_avg_pool2d_0:0:0 */;
+ %226 = reshape(%225, newshape=[0, -1, 1, 1]) /* span=aten::flatten_0:0:0 */;
+ %227 = squeeze(%226, axis=[2, 3]) /* span=aten::flatten_0:0:0 */;
+ %228 = nn.dense(%227, %aten::linear_0.weight, units=None) /* span=aten::linear_0:0:0 */;
+ nn.bias_add(%228, %aten::linear_0.bias, axis=-1) /* span=aten::linear_0:0:0 */
+ }
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 113-118
AutoTuning
----------
@@ -458,7 +534,7 @@ Now, the below api can be used for autotuning the model for any target.
Tuning required RPC setup and please refer to
`Deploy to Adreno GPU <https://tvm.apache.org/docs/how_to/deploy/adreno.html>`_
-.. GENERATED FROM PYTHON SOURCE LINES 104-132
+.. GENERATED FROM PYTHON SOURCE LINES 118-146
.. code-block:: default
@@ -497,13 +573,13 @@ Tuning required RPC setup and please refer to
-.. GENERATED FROM PYTHON SOURCE LINES 133-136
+.. GENERATED FROM PYTHON SOURCE LINES 147-150
Compilation
-----------
Compilation to produce tvm artifacts
-.. GENERATED FROM PYTHON SOURCE LINES 136-178
+.. GENERATED FROM PYTHON SOURCE LINES 150-192
.. code-block:: default
@@ -532,7 +608,7 @@ Compilation to produce tvm artifacts
# Altrernatively, we can save the compilation output and save it as a TVMCPackage.
# This way avoids loading of compiled module without compiling again.
target = target + ", clml"
- pkg_path = tmp_path.relpath("keras-resnet50.tar")
+ pkg_path = tmp_path.relpath("torch-resnet50.tar")
tvmc.compile(
tvmc_model,
target=target,
@@ -556,14 +632,14 @@ Compilation to produce tvm artifacts
-.. GENERATED FROM PYTHON SOURCE LINES 179-183
+.. GENERATED FROM PYTHON SOURCE LINES 193-197
Deploy & Run
------------
Deploy and run the compiled model on RPC
Let tvmc fill inputs using random
-.. GENERATED FROM PYTHON SOURCE LINES 183-199
+.. GENERATED FROM PYTHON SOURCE LINES 197-213
.. code-block:: default
@@ -599,6 +675,11 @@ Let tvmc fill inputs using random
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 1.578 seconds)
+
+
.. _sphx_glr_download_how_to_deploy_models_deploy_model_on_adreno_tvmc.py:
.. only:: html
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index a167379458..1ef4a35bce 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.1731 16.1520 17.0675 15.6303 0.4532
+ 14.0161 13.8383 15.0339 13.6012 0.4710
diff --git a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
index 898a4815d4..fdc6550873 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -130,7 +130,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
- 0%| | 0.00/170M [00:00<?, ?B/s] 4%|4 | 6.88M/170M [00:00<00:02, 71.1MB/s] 8%|8 | 13.9M/170M [00:00<00:02, 72.2MB/s] 12%|#2 | 20.9M/170M [00:00<00:02, 72.0MB/s] 16%|#6 | 27.8M/170M [00:00<00:02, 67.9MB/s] 20%|## | 34.8M/170M [00:00<00:02, 69.7MB/s] 24%|##4 | 41.5M/170M [00:00<00:01, 68.3MB/s] 29%|##8 | 48.9M/170M [00:00<00:01, 70.5MB/s] 33%|###2 | 55.6M/170M [00:00<00:01, 70.3MB/s] 37%|###6 | 62.4M/170M [00:00<00:01, 68.8MB/s] 41%|#### | 69.0M/170M [00:01<00:01, 67.1MB/s] 45%|####4 | 76.1M/170M [00:01<00:01, 68.5MB/s] 49%|####8 | 82.8M/170M [00:01<00:01, 66.7MB/s] 53%|#####3 | 90.4M/170M [00:01<00:01, 70.3MB/s] 57%|#####7 | 97.1M/170M [00:01<00:01, 65.8MB/s] 61%|###### | 104M/170M [00:01<00:01, 61.1MB/s] 65%|######4 | 110M/170M [00:01<00:00, 63.1MB/s] 69%|######8 | 117M/170M [00:01<00:00, 65.1MB/s]
73%|#######2 | 123M/170M [00:01<00:00, 59.7MB/s] 76%|#######6 | 129M/170M [00:02<00:00, 58.6MB/s] 79%|#######9 | 135M/170M [00:02<00:00, 57.2MB/s] 83%|########2 | 140M/170M [00:02<00:00, 53.0MB/s] 86%|########6 | 146M/170M [00:02<00:00, 55.2MB/s] 90%|########9 | 152M/170M [00:02<00:00, 56.0MB/s] 94%|#########3| 159M/170M [00:02<00:00, 57.6MB/s] 97%|#########6| 164M/170M [00:02<00:00, 55.8MB/s] 100%|##########| 170M/170M [00:02<00:00, 62.0MB/s]
+ 0%| | 0.00/170M [00:00<?, ?B/s] 9%|8 | 14.5M/170M [00:00<00:01, 152MB/s] 21%|## | 35.1M/170M [00:00<00:00, 189MB/s] 33%|###2 | 55.8M/170M [00:00<00:00, 202MB/s] 45%|####5 | 76.5M/170M [00:00<00:00, 208MB/s] 57%|#####7 | 97.1M/170M [00:00<00:00, 211MB/s] 69%|######9 | 118M/170M [00:00<00:00, 213MB/s] 82%|########1 | 138M/170M [00:00<00:00, 214MB/s] 94%|#########3| 159M/170M [00:00<00:00, 214MB/s] 100%|##########| 170M/170M [00:00<00:00, 209MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/generalized_rcnn.py:75: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
for img in images:
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/transform.py:122: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
@@ -330,7 +330,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 51.992 seconds)
+ **Total running time of the script:** ( 3 minutes 27.234 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
index d6faa8dcc2..77b719975e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -227,7 +227,7 @@ training. Other models require a full post training calibration.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
- 0%| | 0.00/13.6M [00:00<?, ?B/s] 49%|####8 | 6.62M/13.6M [00:00<00:00, 68.8MB/s] 100%|#########9| 13.5M/13.6M [00:00<00:00, 70.5MB/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 70.2MB/s]
+ 0%| | 0.00/13.6M [00:00<?, ?B/s] 100%|##########| 13.6M/13.6M [00:00<00:00, 144MB/s]
@@ -404,7 +404,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.3791 89.2861 92.3924 88.8225 0.4546
+ 85.9349 85.8634 90.1120 85.4800 0.5474
@@ -452,7 +452,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 38.016 seconds)
+ **Total running time of the script:** ( 1 minutes 30.059 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
index 2559b8e000..30c75bb9ef 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -423,7 +423,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 111.5432 111.5283 112.3434 111.0003 0.2554
+ 106.3536 106.3171 107.2856 105.5390 0.2778
diff --git a/docs/_sources/how_to/deploy_models/index.rst.txt b/docs/_sources/how_to/deploy_models/index.rst.txt
index c668da15ab..438fdded9e 100644
--- a/docs/_sources/how_to/deploy_models/index.rst.txt
+++ b/docs/_sources/how_to/deploy_models/index.rst.txt
@@ -33,7 +33,7 @@ backends.
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno™.">
+ <div class="sphx-glr-thumbcontainer" tooltip="This article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 model on Adreno™.">
.. only:: html
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index 3692c6a946..7168ce3a4d 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**10:40.191** total execution time for **how_to_deploy_models** files:
+**10:00.368** total execution time for **how_to_deploy_models** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:51.992 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:27.234 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:38.016 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:30.059 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:22.353 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:18.100 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:56.185 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 01:01.578 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:55.541 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:51.737 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:51.657 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:51.487 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:32.408 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.120 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:32.032 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.046 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.008 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index 5842fc22f5..1d8bc8bee1 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:04.367** total execution time for **how_to_extend_tvm** files:
+**00:03.993** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:03.026 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.772 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.334 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.214 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 89f78986bf..caa3ad14e6 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
@@ -220,10 +220,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 25637us [25637us] (48.47%; 48.47%)
- FoldScaleAxis: 27253us [12us] (51.53%; 51.53%)
- FoldConstant: 27241us [1841us] (51.50%; 99.96%)
- InferType: 25400us [25400us] (48.02%; 93.24%)
+ InferType: 24544us [24544us] (48.85%; 48.85%)
+ FoldScaleAxis: 25702us [8us] (51.15%; 51.15%)
+ FoldConstant: 25693us [1770us] (51.14%; 99.97%)
+ InferType: 23923us [23923us] (47.61%; 93.11%)
@@ -262,10 +262,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 25760us [25760us] (48.44%; 48.44%)
- FoldScaleAxis: 27416us [11us] (51.56%; 51.56%)
- FoldConstant: 27405us [1932us] (51.54%; 99.96%)
- InferType: 25474us [25474us] (47.90%; 92.95%)
+ InferType: 23591us [23591us] (48.51%; 48.51%)
+ FoldScaleAxis: 25036us [7us] (51.49%; 51.49%)
+ FoldConstant: 25028us [1655us] (51.47%; 99.97%)
+ InferType: 23374us [23374us] (48.07%; 93.39%)
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index afd0cb2d91..341577a54d 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.372608 ms
+ Convolution: 33.674110 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
index 937d83048f..69fc7f6edf 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 11.598698 ms
+ conv2d with tensor core: 12.220508 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
index 42cec19896..bc4eea386c 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -134,8 +134,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018842
- Baseline: 3.443266
+ Numpy running time: 0.013893
+ Baseline: 3.261470
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.312580
+ Opt1: 0.280094
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.291567
+ Opt2: 0.264058
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.118675
+ Opt3: 0.110821
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.108288
+ Opt4: 0.105624
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.112028
+ Opt5: 0.095053
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.134272
+ Opt6: 0.113209
diff --git a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
index d39acef9c6..ca33916e25 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.890** total execution time for **how_to_optimize_operators** files:
+**00:31.360** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.497 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:28.306 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.104 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.950 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.289 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.104 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
index 859bf3d5ab..b44d552eb8 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
Computation times
=================
-**03:52.732** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:36.241** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:41.438 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:34.323 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:15.868 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:11.210 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:21.246 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:19.678 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:18.103 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:16.645 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.973 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.284 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.104 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.100 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index e184c37c59..65c1ae7782 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -766,7 +766,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.344 ms
+ Execution time of this operator: 0.341 ms
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index e376ef82d7..167127c3e0 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -632,7 +632,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.1938 3.1937 3.1940 3.1936 0.0002
+ 3.2027 3.2026 3.2036 3.2021 0.0006
@@ -659,7 +659,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 15.868 seconds)
+ **Total running time of the script:** ( 1 minutes 11.210 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_cuda.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
index 509295e676..034ad32d5e 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -654,7 +654,7 @@ so we can read the log file and load the best schedules.
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 764.3626 765.3355 766.1064 761.6459 1.9466
+ 704.1417 703.2458 707.1693 702.0101 2.1994
@@ -681,7 +681,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 41.438 seconds)
+ **Total running time of the script:** ( 1 minutes 34.323 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
diff --git a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
index 2f6b9de5bd..5a2eb7da7e 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**00:24.247** total execution time for **how_to_tune_with_autotvm** files:
+**00:22.616** total execution time for **how_to_tune_with_autotvm** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:24.210 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``) | 00:22.575 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.020 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``) | 00:00.024 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.006 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``) | 00:00.005 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 39c058c2ae..378a272a51 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
@@ -326,7 +326,7 @@ and measure running time.
Best config:
,None
- Time cost of this operator: 0.037180
+ Time cost of this operator: 0.037298
diff --git a/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt b/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt
index 4c53225189..47442b48c1 100644
--- a/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt
@@ -274,7 +274,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.209e-07 secs/op
+ 1.119e-07 secs/op
diff --git a/docs/_sources/how_to/tutorials/optimize_llm.rst.txt b/docs/_sources/how_to/tutorials/optimize_llm.rst.txt
index 9872f4b102..f157fce4f8 100644
--- a/docs/_sources/how_to/tutorials/optimize_llm.rst.txt
+++ b/docs/_sources/how_to/tutorials/optimize_llm.rst.txt
@@ -858,7 +858,7 @@ IRModule to generate the token.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.592 seconds)
+ **Total running time of the script:** ( 1 minutes 9.080 seconds)
.. _sphx_glr_download_how_to_tutorials_optimize_llm.py:
diff --git a/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt b/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt
index 290e4b54c2..f2935b9817 100644
--- a/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**01:22.958** total execution time for **how_to_tutorials** files:
+**01:13.212** total execution time for **how_to_tutorials** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tutorials_optimize_llm.py` (``optimize_llm.py``) | 01:18.592 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tutorials_optimize_llm.py` (``optimize_llm.py``) | 01:09.080 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tutorials_e2e_opt_model.py` (``e2e_opt_model.py``) | 00:02.682 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tutorials_e2e_opt_model.py` (``e2e_opt_model.py``) | 00:02.520 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tutorials_customize_opt.py` (``customize_opt.py``) | 00:00.865 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tutorials_customize_opt.py` (``customize_opt.py``) | 00:00.821 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.819 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.791 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
index 1b8be6581c..fa8eef333c 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -360,10 +360,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.0 98.734 (1, 2, 10, 10, 3) 2 1 [304.0]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.925 0.95 (1, 6, 10, 10) 1 1 [2.925]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.972 0.316 (1, 1, 10, 10, 3) 1 1 [0.972]
- Total_time - 307.896 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 296.2 98.745 (1, 2, 10, 10, 3) 2 1 [296.2]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.818 0.94 (1, 6, 10, 10) 1 1 [2.818]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.947 0.316 (1, 1, 10, 10, 3) 1 1 [0.947]
+ Total_time - 299.965 - - - - -
@@ -428,10 +428,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 138.1 98.111 (1, 6, 10, 10, 1) 2 1 [138.1]
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.795 1.275 (1, 6, 10, 10) 1 1 [1.795]
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.864 0.614 (1, 3, 10, 10, 1) 1 1 [0.864]
- Total_time - 140.76 - - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.0 97.408 (1, 6, 10, 10, 1) 2 1 [100.0]
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.72 1.675 (1, 6, 10, 10) 1 1 [1.72]
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.942 0.917 (1, 1, 10, 10, 3) 1 1 [0.942]
+ Total_time - 102.661 - - - - -
@@ -439,7 +439,7 @@ Timing the tuned program
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 29.606 seconds)
+ **Total running time of the script:** ( 1 minutes 23.393 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_autotune.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
index 2f8338ba4b..6662c6daf0 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
@@ -118,7 +118,7 @@ download a cat image and preprocess it to use as the model input.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:376: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
- 0%| | 0.00/3.42M [00:00<?, ?B/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 95.1MB/s]
+ 0%| | 0.00/3.42M [00:00<?, ?B/s] 100%|##########| 3.42M/3.42M [00:00<00:00, 86.9MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/nn/quantized/modules/__init__.py:98: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
return torch.quantize_per_tensor(X, float(self.scale),
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/nn/quantized/modules/__init__.py:99: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
@@ -324,7 +324,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 36.107 seconds)
+ **Total running time of the script:** ( 1 minutes 27.604 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_pytorch.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
index 36e390737e..323e3ac2c0 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -218,7 +218,7 @@ take about **2 minutes** to download the Stanford Cars, while COCO 2017 validati
.. code-block:: none
- '/tmp/tmp3bobr3n4/images/random'
+ '/tmp/tmpp8iwu5_u/images/random'
@@ -318,8 +318,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmp3bobr3n4/images/target contains 8144 images
- /tmp/tmp3bobr3n4/images/random contains 5000 images
+ /tmp/tmpp8iwu5_u/images/target contains 8144 images
+ /tmp/tmpp8iwu5_u/images/random contains 5000 images
@@ -494,13 +494,13 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 47s - loss: 0.2280 - accuracy: 0.9205 - val_loss: 0.1398 - val_accuracy: 0.9479 - 47s/epoch - 144ms/step
+ 328/328 - 43s - loss: 0.2146 - accuracy: 0.9255 - val_loss: 0.1379 - val_accuracy: 0.9554 - 43s/epoch - 131ms/step
Epoch 2/3
- 328/328 - 44s - loss: 0.1054 - accuracy: 0.9614 - val_loss: 0.1226 - val_accuracy: 0.9551 - 44s/epoch - 135ms/step
+ 328/328 - 40s - loss: 0.0961 - accuracy: 0.9620 - val_loss: 0.1508 - val_accuracy: 0.9437 - 40s/epoch - 121ms/step
Epoch 3/3
- 328/328 - 44s - loss: 0.0717 - accuracy: 0.9740 - val_loss: 0.0981 - val_accuracy: 0.9641 - 44s/epoch - 134ms/step
+ 328/328 - 39s - loss: 0.0626 - accuracy: 0.9758 - val_loss: 0.0933 - val_accuracy: 0.9675 - 39s/epoch - 120ms/step
- <keras.callbacks.History object at 0x7f465f88d490>
+ <keras.callbacks.History object at 0x7fadb53aae20>
@@ -861,7 +861,7 @@ Arduino tutorial for how to do that `on GitHub <https://github.com/guberti/tvm-a
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 59.469 seconds)
+ **Total running time of the script:** ( 5 minutes 3.563 seconds)
.. _sphx_glr_download_how_to_work_with_microtvm_micro_train.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
index 590bcac3b2..aacf25bbfb 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
Computation times
=================
-**08:37.187** total execution time for **how_to_work_with_microtvm** files:
+**08:24.267** total execution time for **how_to_work_with_microtvm** files:
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 04:59.469 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``) | 05:03.563 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:36.107 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``) | 01:27.604 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:29.606 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``) | 01:23.393 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:12.422 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``) | 00:11.495 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:11.032 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.188 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.551 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``) | 00:08.025 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``) | 00:00.000 | 0.0 MB |
+-----------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index 7ff952a633..0f64853510 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:41.547** total execution time for **how_to_work_with_relay** files:
+**00:39.752** total execution time for **how_to_work_with_relay** files:
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:35.625 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.849 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.977 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``) | 00:03.845 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:01.939 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``) | 00:02.052 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``) | 00:00.006 | 0.0 MB |
+----------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
index ddd23b750d..23934c560e 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
Computation times
=================
-**00:03.184** total execution time for **how_to_work_with_schedules** files:
+**00:03.227** total execution time for **how_to_work_with_schedules** files:
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.472 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``) | 00:01.583 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.704 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.683 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``) | 00:00.703 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``) | 00:00.671 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.134 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``) | 00:00.126 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.071 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.069 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.066 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``) | 00:00.064 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.034 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``) | 00:00.031 | 0.0 MB |
+------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 2c6eff8779..1a3949623e 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@ The compilation steps are:
/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
- yolov3-tiny inference graph built in 29.99s!
+ yolov3-tiny inference graph built in 27.65s!
@@ -447,7 +447,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 6.960 seconds)
+ **Total running time of the script:** ( 1 minutes 3.529 seconds)
.. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 6532c49c2a..d76ddcf7ad 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**01:06.960** total execution time for **topic_vta_tutorials_frontend** files:
+**01:03.529** total execution time for **topic_vta_tutorials_frontend** files:
+--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:06.960 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:03.529 | 0.0 MB |
+--------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
index bed608d2a3..ed4659ed53 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:03.354** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.200** total execution time for **topic_vta_tutorials_optimize** files:
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.854 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``) | 00:02.718 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.501 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.481 | 0.0 MB |
+--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
index b170dabf39..efd3506477 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:00.855** total execution time for **topic_vta_tutorials** files:
+**00:00.839** total execution time for **topic_vta_tutorials** files:
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.470 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.461 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.385 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.378 | 0.0 MB |
+---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index cd9dd7bfca..387cc0efbf 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -325,7 +325,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 92.744 ms
+ Execution time of this operator: 90.997 ms
@@ -441,7 +441,7 @@ operations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 43.786 seconds)
+ **Total running time of the script:** ( 1 minutes 45.074 seconds)
.. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index f70fa229b8..502c9963a6 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,16 +454,16 @@ reduce variance, we take 5 measurements and average them.
waiting for device...
device available
Get devices for measurement successfully!
- No: 1 GFLOPS: 11.26/11.26 result: MeasureResult(costs=(0.0238396768,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7623159885406494, timestamp=1731564136.839757) [('tile_y', [-1, 128]), ('tile_x', [-1, 256])],None,87
- No: 2 GFLOPS: 11.34/11.34 result: MeasureResult(costs=(0.023662184,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.74289870262146, timestamp=1731564137.4822106) [('tile_y', [-1, 64]), ('tile_x', [-1, 256])],None,86
- No: 3 GFLOPS: 7.35/11.34 result: MeasureResult(costs=(0.0365266352,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9317197799682617, timestamp=1731564138.3303134) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
- No: 4 GFLOPS: 2.02/11.34 result: MeasureResult(costs=(0.13290490279999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.456521511077881, timestamp=1731564140.7214527) [('tile_y', [-1, 64]), ('tile_x', [-1, 4])],None,26
- No: 5 GFLOPS: 11.90/11.90 result: MeasureResult(costs=(0.0225549626,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6778995990753174, timestamp=1731564141.5119245) [('tile_y', [-1, 8]), ('tile_x', [-1, 256])],None,83
- No: 6 GFLOPS: 2.02/11.90 result: MeasureResult(costs=(0.1330223358,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4114341735839844, timestamp=1731564143.9009666) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
- No: 7 GFLOPS: 4.93/11.90 result: MeasureResult(costs=(0.054451531799999994,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1396219730377197, timestamp=1731564145.0366194) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
- No: 8 GFLOPS: 11.33/11.90 result: MeasureResult(costs=(0.0236997724,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6722221374511719, timestamp=1731564145.6777785) [('tile_y', [-1, 128]), ('tile_x', [-1, 32])],None,57
- No: 9 GFLOPS: 3.09/11.90 result: MeasureResult(costs=(0.08696859679999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6241281032562256, timestamp=1731564147.4439936) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
- No: 10 GFLOPS: 11.94/11.94 result: MeasureResult(costs=(0.0224819448,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6275911331176758, timestamp=1731564148.0706666) [('tile_y', [-1, 16]), ('tile_x', [-1, 512])],None,94
+ No: 1 GFLOPS: 15.24/15.24 result: MeasureResult(costs=(0.0176096938,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.960632085800171, timestamp=1731598401.6571627) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
+ No: 2 GFLOPS: 7.81/15.24 result: MeasureResult(costs=(0.034386528,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.895888328552246, timestamp=1731598402.4670277) [('tile_y', [-1, 1]), ('tile_x', [-1, 8])],None,30
+ No: 3 GFLOPS: 10.76/15.24 result: MeasureResult(costs=(0.024942435000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9009666442871094, timestamp=1731598403.1309404) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
+ No: 4 GFLOPS: 10.96/15.24 result: MeasureResult(costs=(0.024503013799999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7547705173492432, timestamp=1731598403.7815816) [('tile_y', [-1, 8]), ('tile_x', [-1, 64])],None,63
+ No: 5 GFLOPS: 4.04/15.24 result: MeasureResult(costs=(0.066424105,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3502404689788818, timestamp=1731598405.26695) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+ No: 6 GFLOPS: 12.39/15.24 result: MeasureResult(costs=(0.0216712034,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6343398094177246, timestamp=1731598405.8801553) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+ No: 7 GFLOPS: 14.04/15.24 result: MeasureResult(costs=(0.019116380199999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6089017391204834, timestamp=1731598406.4480417) [('tile_y', [-1, 8]), ('tile_x', [-1, 256])],None,83
+ No: 8 GFLOPS: 2.96/15.24 result: MeasureResult(costs=(0.0907385344,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.728644609451294, timestamp=1731598408.158339) [('tile_y', [-1, 16]), ('tile_x', [-1, 2])],None,14
+ No: 9 GFLOPS: 9.06/15.24 result: MeasureResult(costs=(0.0296335848,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.744819164276123, timestamp=1731598409.0193412) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+ No: 10 GFLOPS: 13.11/15.24 result: MeasureResult(costs=(0.020468879000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6116936206817627, timestamp=1731598409.6105006) [('tile_y', [-1, 4]), ('tile_x', [-1, 256])],None,82
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 63adbc63c3..31a2f327b5 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -311,7 +311,7 @@ standard deviation.
.. code-block:: none
- {'mean': 497.4373029200001, 'median': 498.5680613499994, 'std': 2.3229567310236985}
+ {'mean': 467.6736101299912, 'median': 467.7150531000052, 'std': 0.7902913979128545}
@@ -582,31 +582,30 @@ the tuning data to.
.. code-block:: none
- [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 9.32/ 20.40 GFLOPS | Progress: (4/20) | 15.05 s [Task 1/25] Current/Best: 17.09/ 20.40 GFLOPS | Progress: (8/20) | 17.66 s [Task 1/25] Current/Best: 9.36/ 21.69 GFLOPS | Progress: (12/20) | 25.62 s [Task 1/25] Current/Best: 13.07/ 21.69 GFLOPS | Progress: (16/20) | 29.01 s [Task 1/25] Current/Best: 3.33/ 21.69 GFLOPS | Progress: (20/20) | 32.93 s Done.
- [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 15.34/ 15.34 GFLOPS | Progress: (4/20) | 4.49 s [Task 2/25] Current/Best: 12.05/ 15.34 GFLOPS | Progress: (8/20) | 7.30 s [Task 2/25] Current/Best: 6.92/ 15.34 GFLOPS | Progress: (12/20) | 8.76 s [Task 2/25] Current/Best: 7.24/ 18.03 GFLOPS | Progress: (16/20) | 10.14 s [Task 2/25] Current/Best: 5.43/ 18.03 GFLOPS | Progress: (20/20) | 11.96 s Done.
- [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 9.47/ 21.43 GFLOPS | Progress: (4/20) | 4.64 s [Task 3/25] Current/Best: 13.07/ 21.43 GFLOPS | Progress: (8/20) | 6.97 s [Task 3/25] Current/Best: 20.18/ 21.43 GFLOPS | Progress: (12/20) | 9.21 s [Task 3/25] Current/Best: 15.86/ 21.62 GFLOPS | Progress: (16/20) | 12.81 s [Task 3/25] Current/Best: 8.14/ 21.62 GFLOPS | Progress: (20/20) | 15.50 s Done.
- [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 13.08/ 13.73 GFLOPS | Progress: (4/20) | 5.73 s [Task 4/25] Current/Best: 15.98/ 15.98 GFLOPS | Progress: (8/20) | 7.81 s [Task 4/25] Current/Best: 9.33/ 15.98 GFLOPS | Progress: (12/20) | 13.54 s [Task 4/25] Current/Best: 3.33/ 18.59 GFLOPS | Progress: (16/20) | 15.63 s [Task 4/25] Current/Best: 16.81/ 18.59 GFLOPS | Progress: (20/20) | 17.52 s Done.
- [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 7.92/ 15.13 GFLOPS | Progress: (4/20) | 4.55 s [Task 5/25] Current/Best: 4.18/ 17.79 GFLOPS | Progress: (8/20) | 6.64 s [Task 5/25] Current/Best: 17.32/ 17.79 GFLOPS | Progress: (12/20) | 8.74 s [Task 5/25] Current/Best: 11.92/ 20.12 GFLOPS | Progress: (16/20) | 11.84 s [Task 5/25] Current/Best: 16.68/ 20.12 GFLOPS | Progress: (20/20) | 13.74 s Done.
- [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 14.26/ 14.26 GFLOPS | Progress: (4/20) | 5.18 s [Task 6/25] Current/Best: 12.57/ 18.34 GFLOPS | Progress: (8/20) | 7.87 s [Task 6/25] Current/Best: 10.08/ 18.51 GFLOPS | Progress: (12/20) | 11.06 s [Task 6/25] Current/Best: 17.69/ 21.26 GFLOPS | Progress: (16/20) | 13.04 s [Task 6/25] Current/Best: 12.45/ 21.26 GFLOPS | Progress: (20/20) | 15.42 s Done.
- [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 19.36/ 19.36 GFLOPS | Progress: (4/20) | 5.01 s [Task 7/25] Current/Best: 1.59/ 19.36 GFLOPS | Progress: (8/20) | 9.17 s [Task 7/25] Current/Best: 21.16/ 21.16 GFLOPS | Progress: (12/20) | 11.80 s [Task 7/25] Current/Best: 13.42/ 21.16 GFLOPS | Progress: (16/20) | 14.94 s [Task 7/25] Current/Best: 8.42/ 21.16 GFLOPS | Progress: (20/20) | 17.48 s Done.
- [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 11.79/ 12.55 GFLOPS | Progress: (4/20) | 9.52 s [Task 8/25] Current/Best: 8.93/ 12.87 GFLOPS | Progress: (8/20) | 13.47 s [Task 8/25] Current/Best: 5.41/ 13.77 GFLOPS | Progress: (12/20) | 16.74 s [Task 8/25] Current/Best: 8.74/ 13.77 GFLOPS | Progress: (16/20) | 25.53 s [Task 8/25] Current/Best: 5.03/ 19.14 GFLOPS | Progress: (20/20) | 28.24 s Done.
- [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 18.38/ 18.38 GFLOPS | Progress: (4/20) | 4.69 s [Task 9/25] Current/Best: 13.95/ 18.38 GFLOPS | Progress: (8/20) | 9.85 s [Task 9/25] Current/Best: 13.88/ 18.38 GFLOPS | Progress: (12/20) | 17.89 s [Task 9/25] Current/Best: 18.52/ 18.52 GFLOPS | Progress: (16/20) | 21.91 s [Task 9/25] Current/Best: 17.10/ 19.86 GFLOPS | Progress: (20/20) | 26.00 s Done.
- [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 13.95/ 16.35 GFLOPS | Progress: (4/20) | 4.50 s [Task 10/25] Current/Best: 5.37/ 16.35 GFLOPS | Progress: (8/20) | 6.61 s [Task 10/25] Current/Best: 11.63/ 16.35 GFLOPS | Progress: (12/20) | 9.01 s [Task 10/25] Current/Best: 16.23/ 16.35 GFLOPS | Progress: (16/20) | 11.00 s [Task 10/25] Current/Best: 13.31/ 16.35 GFLOPS | Progress: (20/20) | 13.21 s Done.
- [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 16.22/ 16.22 GFLOPS | Progress: (4/20) | 5.60 s [Task 11/25] Current/Best: 11.21/ 21.60 GFLOPS | Progress: (8/20) | 8.29 s [Task 11/25] Current/Best: 21.12/ 21.60 GFLOPS | Progress: (12/20) | 11.92 s [Task 11/25] Current/Best: 21.48/ 21.60 GFLOPS | Progress: (16/20) | 14.51 s [Task 11/25] Current/Best: 22.72/ 22.72 GFLOPS | Progress: (20/20) | 17.10 s Done.
- [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 11.27/ 17.14 GFLOPS | Progress: (4/20) | 5.64 s [Task 12/25] Current/Best: 14.03/ 17.14 GFLOPS | Progress: (8/20) | 9.50 s [Task 12/25] Current/Best: 12.33/ 17.14 GFLOPS | Progress: (12/20) | 11.52 s [Task 12/25] Current/Best: 15.93/ 17.14 GFLOPS | Progress: (16/20) | 16.39 s [Task 12/25] Current/Best: 12.83/ 17.14 GFLOPS | Progress: (20/20) | 19.44 s Done.
- [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 6.26/ 21.13 GFLOPS | Progress: (4/20) | 6.77 s [Task 13/25] Current/Best: 18.53/ 21.28 GFLOPS | Progress: (8/20) | 9.37 s [Task 13/25] Current/Best: 18.71/ 21.28 GFLOPS | Progress: (12/20) | 14.13 s [Task 13/25] Current/Best: 12.72/ 21.28 GFLOPS | Progress: (16/20) | 17.69 s [Task 13/25] Current/Best: 20.97/ 21.28 GFLOPS | Progress: (20/20) | 20.31 s Done.
- [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 7.24/ 18.00 GFLOPS | Progress: (4/20) | 13.24 s [Task 14/25] Current/Best: 4.30/ 18.00 GFLOPS | Progress: (8/20) | 21.91 s [Task 14/25] Current/Best: 3.91/ 21.57 GFLOPS | Progress: (12/20) | 30.92 s [Task 14/25] Current/Best: 16.23/ 21.57 GFLOPS | Progress: (16/20) | 36.46 s [Task 14/25] Current/Best: 13.68/ 21.57 GFLOPS | Progress: (20/20) | 47.84 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 6.33/ 18.76 GFLOPS | Progress: (4/20) | 5.96 s [Task 15/25] Current/Best: 8.47/ 20.78 GFLOPS | Progress: (8/20) | 12.24 s [Task 15/25] Current/Best: 6.42/ 20.78 GFLOPS | Progress: (12/20) | 19.31 s [Task 15/25] Current/Best: 13.45/ 22.73 GFLOPS | Progress: (16/20) | 25.01 s [Task 15/25] Current/Best: 1.72/ 22.73 GFLOPS | Progress: (20
/20) | 27.29 s Done.
- [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 20.34/ 20.34 GFLOPS | Progress: (4/20) | 4.22 s [Task 16/25] Current/Best: 19.03/ 20.34 GFLOPS | Progress: (8/20) | 6.04 s [Task 16/25] Current/Best: 9.24/ 20.34 GFLOPS | Progress: (12/20) | 8.30 s [Task 16/25] Current/Best: 18.79/ 20.34 GFLOPS | Progress: (16/20) | 11.17 s [Task 16/25] Current/Best: 19.73/ 20.34 GFLOPS | Progress: (20/20) | 13.96 s Done.
- [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 19.86/ 21.48 GFLOPS | Progress: (4/20) | 4.94 s [Task 17/25] Current/Best: 16.40/ 21.48 GFLOPS | Progress: (8/20) | 7.58 s [Task 17/25] Current/Best: 9.68/ 21.48 GFLOPS | Progress: (12/20) | 10.72 s [Task 17/25] Current/Best: 9.08/ 22.49 GFLOPS | Progress: (16/20) | 13.07 s [Task 17/25] Current/Best: 10.35/ 22.49 GFLOPS | Progress: (20/20) | 15.86 s Done.
- [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 14.16/ 20.18 GFLOPS | Progress: (4/20) | 5.23 s [Task 18/25] Current/Best: 7.07/ 20.93 GFLOPS | Progress: (8/20) | 10.68 s [Task 18/25] Current/Best: 17.90/ 20.93 GFLOPS | Progress: (12/20) | 16.72 s [Task 18/25] Current/Best: 17.19/ 20.93 GFLOPS | Progress: (16/20) | 20.93 s [Task 18/25] Current/Best: 14.42/ 22.33 GFLOPS | Progress: (20/20) | 23.11 s Done.
- [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 10.60/ 10.60 GFLOPS | Progress: (4/20) | 6.79 s [Task 19/25] Current/Best: 6.15/ 10.60 GFLOPS | Progress: (8/20) | 11.40 s [Task 19/25] Current/Best: 21.46/ 21.46 GFLOPS | Progress: (12/20) | 14.64 s [Task 19/25] Current/Best: 9.79/ 21.46 GFLOPS | Progress: (16/20) | 18.28 s [Task 19/25] Current/Best: 13.06/ 21.46 GFLOPS | Progress: (20/20) | 21.56 s Done.
- [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 11.06/ 16.42 GFLOPS | Progress: (4/20) | 5.39 s [Task 20/25] Current/Best: 5.03/ 16.42 GFLOPS | Progress: (8/20) | 17.18 s [Task 20/25] Current/Best: 15.52/ 16.42 GFLOPS | Progress: (12/20) | 25.14 s [Task 20/25] Current/Best: 4.69/ 17.74 GFLOPS | Progress: (16/20) | 29.54 s [Task 20/25] Current/Best: 1.58/ 17.74 GFLOPS | Progress: (20/20) | 33.59 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 21/25] Current/Best: 13.42/ 16.26 GFLOPS | Progress: (4/20) | 6.86 s Done.
+ [Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 1/25] Current/Best: 7.22/ 16.70 GFLOPS | Progress: (4/20) | 9.94 s [Task 1/25] Current/Best: 6.66/ 18.57 GFLOPS | Progress: (8/20) | 13.07 s [Task 1/25] Current/Best: 21.80/ 21.80 GFLOPS | Progress: (12/20) | 15.24 s [Task 1/25] Current/Best: 7.29/ 21.90 GFLOPS | Progress: (16/20) | 17.82 s [Task 1/25] Current/Best: 15.79/ 21.90 GFLOPS | Progress: (20/20) | 20.54 s Done.
+ [Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 2/25] Current/Best: 9.01/ 19.35 GFLOPS | Progress: (4/20) | 3.87 s [Task 2/25] Current/Best: 2.59/ 20.71 GFLOPS | Progress: (8/20) | 5.85 s [Task 2/25] Current/Best: 20.73/ 20.87 GFLOPS | Progress: (12/20) | 7.17 s [Task 2/25] Current/Best: 3.92/ 20.87 GFLOPS | Progress: (16/20) | 8.85 s [Task 2/25] Current/Best: 7.67/ 20.87 GFLOPS | Progress: (20/20) | 10.68 s Done.
+ [Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 3/25] Current/Best: 15.09/ 19.11 GFLOPS | Progress: (4/20) | 4.66 s [Task 3/25] Current/Best: 10.08/ 20.65 GFLOPS | Progress: (8/20) | 7.01 s [Task 3/25] Current/Best: 15.13/ 20.65 GFLOPS | Progress: (12/20) | 10.27 s [Task 3/25] Current/Best: 15.50/ 23.48 GFLOPS | Progress: (16/20) | 12.42 s [Task 3/25] Current/Best: 24.92/ 24.92 GFLOPS | Progress: (20/20) | 14.42 s Done.
+ [Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 4/25] Current/Best: 19.22/ 19.22 GFLOPS | Progress: (4/20) | 4.34 s [Task 4/25] Current/Best: 7.02/ 20.98 GFLOPS | Progress: (8/20) | 6.24 s [Task 4/25] Current/Best: 14.92/ 22.88 GFLOPS | Progress: (12/20) | 8.96 s [Task 4/25] Current/Best: 21.30/ 22.88 GFLOPS | Progress: (16/20) | 10.90 s [Task 4/25] Current/Best: 22.54/ 22.88 GFLOPS | Progress: (20/20) | 12.51 s Done.
+ [Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 5/25] Current/Best: 18.17/ 18.17 GFLOPS | Progress: (4/20) | 4.28 s [Task 5/25] Current/Best: 14.88/ 18.17 GFLOPS | Progress: (8/20) | 6.20 s [Task 5/25] Current/Best: 11.13/ 18.17 GFLOPS | Progress: (12/20) | 8.95 s [Task 5/25] Current/Best: 11.15/ 18.17 GFLOPS | Progress: (16/20) | 11.16 s [Task 5/25] Current/Best: 5.66/ 19.19 GFLOPS | Progress: (20/20) | 13.16 s Done.
+ [Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 6/25] Current/Best: 13.53/ 15.76 GFLOPS | Progress: (4/20) | 5.85 s [Task 6/25] Current/Best: 12.31/ 15.76 GFLOPS | Progress: (8/20) | 9.69 s [Task 6/25] Current/Best: 6.59/ 16.98 GFLOPS | Progress: (12/20) | 12.68 s [Task 6/25] Current/Best: 14.47/ 16.98 GFLOPS | Progress: (16/20) | 17.14 s [Task 6/25] Current/Best: 19.86/ 19.86 GFLOPS | Progress: (20/20) | 20.39 s Done.
+ [Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 7/25] Current/Best: 20.91/ 20.91 GFLOPS | Progress: (4/20) | 5.17 s [Task 7/25] Current/Best: 3.19/ 20.91 GFLOPS | Progress: (8/20) | 8.75 s [Task 7/25] Current/Best: 18.01/ 20.91 GFLOPS | Progress: (12/20) | 10.76 s [Task 7/25] Current/Best: 10.93/ 20.91 GFLOPS | Progress: (16/20) | 13.26 s [Task 7/25] Current/Best: 10.78/ 20.91 GFLOPS | Progress: (20/20) | 15.44 s Done.
+ [Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 8/25] Current/Best: 11.17/ 15.97 GFLOPS | Progress: (4/20) | 4.76 s [Task 8/25] Current/Best: 2.56/ 15.97 GFLOPS | Progress: (8/20) | 7.95 s [Task 8/25] Current/Best: 12.79/ 20.68 GFLOPS | Progress: (12/20) | 11.75 s [Task 8/25] Current/Best: 13.80/ 20.68 GFLOPS | Progress: (16/20) | 17.48 s [Task 8/25] Current/Best: 5.22/ 20.68 GFLOPS | Progress: (20/20) | 22.34 s Done.
+ [Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 9/25] Current/Best: 9.98/ 16.95 GFLOPS | Progress: (4/20) | 4.59 s [Task 9/25] Current/Best: 9.91/ 21.16 GFLOPS | Progress: (8/20) | 15.95 s [Task 9/25] Current/Best: 16.32/ 21.16 GFLOPS | Progress: (12/20) | 19.30 s [Task 9/25] Current/Best: 6.69/ 21.16 GFLOPS | Progress: (16/20) | 26.35 s [Task 9/25] Current/Best: 13.53/ 22.36 GFLOPS | Progress: (20/20) | 28.55 s Done.
+ [Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 10/25] Current/Best: 11.24/ 20.95 GFLOPS | Progress: (4/20) | 5.30 s [Task 10/25] Current/Best: 12.91/ 20.95 GFLOPS | Progress: (8/20) | 7.81 s [Task 10/25] Current/Best: 21.72/ 21.72 GFLOPS | Progress: (12/20) | 9.48 s [Task 10/25] Current/Best: 6.46/ 21.72 GFLOPS | Progress: (16/20) | 11.19 s [Task 10/25] Current/Best: 16.33/ 21.72 GFLOPS | Progress: (20/20) | 13.23 s Done.
+ [Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 11/25] Current/Best: 14.14/ 21.75 GFLOPS | Progress: (4/20) | 5.55 s [Task 11/25] Current/Best: 12.79/ 21.75 GFLOPS | Progress: (8/20) | 9.28 s [Task 11/25] Current/Best: 11.67/ 21.75 GFLOPS | Progress: (12/20) | 11.69 s [Task 11/25] Current/Best: 14.04/ 21.75 GFLOPS | Progress: (16/20) | 14.05 s [Task 11/25] Current/Best: 13.61/ 24.58 GFLOPS | Progress: (20/20) | 17.18 s Done.
+ [Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 12/25] Current/Best: 10.19/ 16.49 GFLOPS | Progress: (4/20) | 8.62 s [Task 12/25] Current/Best: 3.18/ 18.37 GFLOPS | Progress: (8/20) | 11.30 s [Task 12/25] Current/Best: 11.46/ 18.37 GFLOPS | Progress: (12/20) | 15.46 s [Task 12/25] Current/Best: 15.16/ 18.37 GFLOPS | Progress: (16/20) | 18.65 s [Task 12/25] Current/Best: 11.36/ 19.27 GFLOPS | Progress: (20/20) | 21.95 s Done.
+ [Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 13/25] Current/Best: 20.85/ 21.46 GFLOPS | Progress: (4/20) | 4.42 s [Task 13/25] Current/Best: 19.04/ 21.46 GFLOPS | Progress: (8/20) | 7.47 s [Task 13/25] Current/Best: 13.24/ 21.46 GFLOPS | Progress: (12/20) | 10.70 s [Task 13/25] Current/Best: 13.23/ 21.46 GFLOPS | Progress: (16/20) | 13.46 s [Task 13/25] Current/Best: 10.28/ 21.46 GFLOPS | Progress: (20/20) | 16.62 s Done.
+ [Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 14/25] Current/Best: 14.88/ 20.58 GFLOPS | Progress: (4/20) | 10.51 s [Task 14/25] Current/Best: 16.48/ 20.58 GFLOPS | Progress: (8/20) | 13.97 s [Task 14/25] Current/Best: 7.59/ 20.58 GFLOPS | Progress: (12/20) | 25.67 s [Task 14/25] Current/Best: 18.45/ 20.58 GFLOPS | Progress: (16/20) | 29.78 s [Task 14/25] Current/Best: 12.64/ 21.98 GFLOPS | Progress: (20/20) | 33.03 s [Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 15/25] Current/Best: 11.33/ 22.76 GFLOPS | Progress: (4/20) | 4.68 s [Task 15/25] Current/Best: 21.37/ 22.76 GFLOPS | Progress: (8/20) | 6.32 s [Task 15/25] Current/Best: 4.93/ 22.76 GFLOPS | Progress: (12/20) | 17.15 s [Task 15/25] Current/Best: 19.88/ 22.76 GFLOPS | Progress: (16/20) | 18.69 s [Task 15/25] Current/Best: 20.28/ 22.76 GFLOPS | Progress: (20/
20) | 20.22 s Done.
+ [Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 16/25] Current/Best: 9.02/ 11.06 GFLOPS | Progress: (4/20) | 6.28 s [Task 16/25] Current/Best: 11.26/ 19.18 GFLOPS | Progress: (8/20) | 7.87 s [Task 16/25] Current/Best: 20.37/ 20.37 GFLOPS | Progress: (12/20) | 9.99 s [Task 16/25] Current/Best: 5.44/ 20.37 GFLOPS | Progress: (16/20) | 12.51 s [Task 16/25] Current/Best: 6.97/ 20.37 GFLOPS | Progress: (20/20) | 14.72 s Done.
+ [Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 17/25] Current/Best: 18.49/ 21.55 GFLOPS | Progress: (4/20) | 5.74 s [Task 17/25] Current/Best: 21.09/ 24.13 GFLOPS | Progress: (8/20) | 8.82 s [Task 17/25] Current/Best: 10.57/ 24.13 GFLOPS | Progress: (12/20) | 13.05 s [Task 17/25] Current/Best: 12.47/ 24.13 GFLOPS | Progress: (16/20) | 15.45 s [Task 17/25] Current/Best: 17.51/ 24.13 GFLOPS | Progress: (20/20) | 18.81 s Done.
+ [Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 18/25] Current/Best: 6.81/ 15.95 GFLOPS | Progress: (4/20) | 4.76 s [Task 18/25] Current/Best: 12.53/ 18.09 GFLOPS | Progress: (8/20) | 8.17 s [Task 18/25] Current/Best: 17.50/ 18.09 GFLOPS | Progress: (12/20) | 12.43 s [Task 18/25] Current/Best: 11.84/ 20.27 GFLOPS | Progress: (16/20) | 17.92 s [Task 18/25] Current/Best: 14.21/ 20.27 GFLOPS | Progress: (20/20) | 20.16 s Done.
+ [Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 19/25] Current/Best: 12.14/ 12.14 GFLOPS | Progress: (4/20) | 6.65 s [Task 19/25] Current/Best: 17.66/ 17.66 GFLOPS | Progress: (8/20) | 11.05 s [Task 19/25] Current/Best: 9.37/ 20.79 GFLOPS | Progress: (12/20) | 14.54 s [Task 19/25] Current/Best: 16.67/ 20.79 GFLOPS | Progress: (16/20) | 18.07 s [Task 19/25] Current/Best: 12.38/ 20.79 GFLOPS | Progress: (20/20) | 21.07 s Done.
+ [Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 20/25] Current/Best: 18.88/ 23.94 GFLOPS | Progress: (4/20) | 4.48 s [Task 20/25] Current/Best: 10.16/ 23.94 GFLOPS | Progress: (8/20) | 16.15 s [Task 20/25] Current/Best: 8.21/ 23.94 GFLOPS | Progress: (12/20) | 28.00 s [Task 20/25] Current/Best: 12.54/ 23.94 GFLOPS | Progress: (16/20) | 34.37 s [Task 20/25] Current/Best: 19.65/ 23.94 GFLOPS | Progress: (20/20) | 37.24 s [Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
Done.
- [Task 21/25] Current/Best: 19.96/ 19.96 GFLOPS | Progress: (8/20) | 16.16 s [Task 21/25] Current/Best: 13.46/ 19.96 GFLOPS | Progress: (12/20) | 19.11 s [Task 21/25] Current/Best: 6.86/ 19.96 GFLOPS | Progress: (16/20) | 26.46 s [Task 21/25] Current/Best: 5.20/ 19.96 GFLOPS | Progress: (20/20) | 33.45 s Done.
- [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 16.18/ 16.18 GFLOPS | Progress: (4/20) | 4.74 s [Task 22/25] Current/Best: 19.13/ 19.13 GFLOPS | Progress: (8/20) | 6.36 s [Task 22/25] Current/Best: 16.93/ 19.13 GFLOPS | Progress: (12/20) | 8.79 s [Task 22/25] Current/Best: 17.40/ 19.13 GFLOPS | Progress: (16/20) | 10.66 s [Task 22/25] Current/Best: 10.53/ 20.71 GFLOPS | Progress: (20/20) | 14.15 s Done.
- [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 12.86/ 19.77 GFLOPS | Progress: (4/20) | 5.39 s [Task 23/25] Current/Best: 11.78/ 19.77 GFLOPS | Progress: (8/20) | 9.86 s [Task 23/25] Current/Best: 11.77/ 19.77 GFLOPS | Progress: (12/20) | 12.91 s [Task 23/25] Current/Best: 22.46/ 22.46 GFLOPS | Progress: (16/20) | 16.38 s [Task 23/25] Current/Best: 23.21/ 23.21 GFLOPS | Progress: (20/20) | 20.74 s Done.
- [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 5.38/ 10.19 GFLOPS | Progress: (4/20) | 13.53 s [Task 24/25] Current/Best: 3.40/ 10.19 GFLOPS | Progress: (8/20) | 25.99 s [Task 24/25] Current/Best: 3.26/ 10.19 GFLOPS | Progress: (12/20) | 38.21 s [Task 24/25] Current/Best: 3.19/ 10.19 GFLOPS | Progress: (16/20) | 50.74 s [Task 24/25] Current/Best: 5.54/ 10.19 GFLOPS | Progress: (20/20) | 54.82 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 5.63/ 8.49 GFLOPS | Progress: (4/20) | 13.26 s Done.
- [Task 25/25] Current/Best: 5.56/ 8.84 GFLOPS | Progress: (8/20) | 16.87 s [Task 25/25] Current/Best: 1.48/ 8.84 GFLOPS | Progress: (12/20) | 19.05 s [Task 25/25] Current/Best: 5.37/ 9.18 GFLOPS | Progress: (16/20) | 30.03 s [Task 25/25] Current/Best: 1.55/ 9.18 GFLOPS | Progress: (20/20) | 33.30 s
+ [Task 21/25] Current/Best: 8.72/ 8.72 GFLOPS | Progress: (4/20) | 13.83 s [Task 21/25] Current/Best: 16.93/ 16.93 GFLOPS | Progress: (8/20) | 25.00 s [Task 21/25] Current/Best: 18.86/ 18.86 GFLOPS | Progress: (12/20) | 29.16 s [Task 21/25] Current/Best: 10.14/ 18.86 GFLOPS | Progress: (16/20) | 34.30 s [Task 21/25] Current/Best: 23.54/ 23.54 GFLOPS | Progress: (20/20) | 45.38 s [Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 22/25] Current/Best: 14.70/ 20.56 GFLOPS | Progress: (4/20) | 5.86 s [Task 22/25] Current/Best: 22.56/ 22.56 GFLOPS | Progress: (8/20) | 8.95 s [Task 22/25] Current/Best: 8.17/ 22.56 GFLOPS | Progress: (12/20) | 11.50 s [Task 22/25] Current/Best: 9.71/ 22.56 GFLOPS | Progress: (16/20) | 13.41 s [Task 22/25] Current/Best: 19.93/ 22.56 GFLOPS | Progress: (20/20) | 15.41 s Done.
+ [Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 23/25] Current/Best: 23.48/ 23.48 GFLOPS | Progress: (4/20) | 5.96 s [Task 23/25] Current/Best: 9.98/ 23.48 GFLOPS | Progress: (8/20) | 8.93 s [Task 23/25] Current/Best: 1.60/ 23.48 GFLOPS | Progress: (12/20) | 12.96 s [Task 23/25] Current/Best: 21.60/ 23.48 GFLOPS | Progress: (16/20) | 16.06 s [Task 23/25] Current/Best: 20.45/ 23.48 GFLOPS | Progress: (20/20) | 18.91 s Done.
+ [Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 24/25] Current/Best: 10.49/ 10.49 GFLOPS | Progress: (4/20) | 13.07 s [Task 24/25] Current/Best: 2.87/ 10.49 GFLOPS | Progress: (8/20) | 24.08 s [Task 24/25] Current/Best: 3.08/ 10.49 GFLOPS | Progress: (12/20) | 27.85 s [Task 24/25] Current/Best: 7.88/ 10.49 GFLOPS | Progress: (16/20) | 33.98 s [Task 24/25] Current/Best: 2.20/ 10.49 GFLOPS | Progress: (20/20) | 44.73 s [Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s [Task 25/25] Current/Best: 5.72/ 9.23 GFLOPS | Progress: (4/20) | 15.14 s [Task 25/25] Current/Best: 1.60/ 9.23 GFLOPS | Progress: (8/20) | 19.90 s Done.
+ [Task 25/25] Current/Best: 3.12/ 9.60 GFLOPS | Progress: (12/20) | 22.56 s [Task 25/25] Current/Best: 7.39/ 9.60 GFLOPS | Progress: (16/20) | 26.78 s [Task 25/25] Current/Best: 9.08/ 10.54 GFLOPS | Progress: (20/20) | 37.75 s
@@ -675,6 +674,7 @@ model using optimized operators to speed up our computations.
.. code-block:: none
Done.
+ Done.
@@ -766,8 +766,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 427.23773420999805, 'median': 426.2538619499992, 'std': 1.9056924415046534}
- unoptimized: {'mean': 497.4373029200001, 'median': 498.5680613499994, 'std': 2.3229567310236985}
+ optimized: {'mean': 368.6606970599951, 'median': 368.54574569997567, 'std': 0.44749195546819753}
+ unoptimized: {'mean': 467.6736101299912, 'median': 467.7150531000052, 'std': 0.7902913979128545}
@@ -790,7 +790,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 14 minutes 17.714 seconds)
+ **Total running time of the script:** ( 13 minutes 25.316 seconds)
.. _sphx_glr_download_tutorial_autotvm_relay_x86.py:
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index cec8c9c34f..460e61678f 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -270,7 +270,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x1b9c9a50)), stage(b, placeholder(b, 0xb88acf0)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T. [...]
+ [stage(a, placeholder(a, 0x208bbf10)), stage(b, placeholder(b, 0x28cbe600)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index ee25e50012..2255d67148 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,28 +5,28 @@
Computation times
=================
-**18:09.424** total execution time for **tutorial** files:
+**17:08.962** total execution time for **tutorial** files:
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 14:17.714 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``) | 13:25.316 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:43.786 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:45.074 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 01:01.152 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``) | 00:58.242 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:45.458 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``) | 00:42.810 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:18.982 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``) | 00:15.910 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:01.432 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.864 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``) | 00:00.900 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``) | 00:00.745 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_uma.py` (``uma.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``) | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``) | 00:00.000 | 0.0 MB |
+------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 6e130ba434..627ce6bfd3 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -285,8 +285,8 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000008
- naive: 0.000008
+ Numpy running time: 0.000006
+ naive: 0.000006
@@ -389,7 +389,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000010
+ parallel: 0.000006
@@ -444,7 +444,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000040
+ vector: 0.000039
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -498,10 +498,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 7.735510000657087e-06 1.0
- naive 7.6943e-06 0.9946726200788847
- parallel 1.01776e-05 1.3156986416067553
- vector 3.9928799999999996e-05 5.161754040342301
+ numpy 6.213489996298449e-06 1.0
+ naive 5.6550999999999994e-06 0.9101326313181323
+ parallel 5.893e-06 0.9484202925426172
+ vector 3.89052e-05 6.261408648469206
@@ -922,7 +922,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.019047
+ Numpy running time: 0.013850
@@ -980,7 +980,7 @@ optimizations.
.. code-block:: none
- none: 3.448148
+ none: 3.352162
@@ -1080,7 +1080,7 @@ schedule.
.. code-block:: none
- blocking: 0.307210
+ blocking: 0.292476
@@ -1164,7 +1164,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.297649
+ vectorization: 0.270381
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1230,7 +1230,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.118966
+ loop permutation: 0.109763
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1321,7 +1321,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.106300
+ array packing: 0.103115
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1404,7 +1404,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.111623
+ block caching: 0.094788
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1478,7 +1478,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.133673
+ parallelization: 0.113398
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1548,13 +1548,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.4481480034 1.0
- blocking 0.30720981950000004 0.08909415117827886
- vectorization 0.297648947 0.0863213953422264
- loop permutation 0.11896635180000001 0.03450152130439147
- array packing 0.10630043630000001 0.030828269608840424
- block caching 0.1116226948 0.03237178180575078
- parallelization 0.13367337540000002 0.03876671629761634
+ none 3.3521616879 1.0
+ blocking 0.29247639039999995 0.0872500844621326
+ vectorization 0.2703805014 0.08065855008604401
+ loop permutation 0.10976311450000001 0.03274397977168052
+ array packing 0.103115292 0.030760834828524562
+ block caching 0.0947880493 0.028276693705482046
+ parallelization 0.11339750280000001 0.03382817219387743
@@ -1594,11 +1594,6 @@ operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 1 minutes 1.152 seconds)
-
-
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
.. only:: html
diff --git a/docs/commit_hash b/docs/commit_hash
index fc87f55c65..a5a9737916 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-370ec6a2c93a444e5b8eb09877567995dfa866fd
+4cfd1817f724066030676d3830ff4cc6649030c4
diff --git a/docs/deep_dive/relax/tutorials/sg_execution_times.html b/docs/deep_dive/relax/tutorials/sg_execution_times.html
index 8aa1768474..fdb4b7bf7c 100644
--- a/docs/deep_dive/relax/tutorials/sg_execution_times.html
+++ b/docs/deep_dive/relax/tutorials/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-deep-dive-relax-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.266</strong> total execution time for <strong>deep_dive_relax_tutorials</strong> files:</p>
+<p><strong>00:00.240</strong> total execution time for <strong>deep_dive_relax_tutorials</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -390,11 +390,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="relax_creation.html#sphx-glr-deep-dive-relax-tutorials-relax-creation-py"><span class="std std-ref">Relax Creation</span></a> (<code class="docutils literal notranslate"><span class="pre">relax_creation.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="relax_transformation.html#sphx-glr-deep-dive-relax-tutorials-relax-transformation-py"><span class="std std-ref">Transformation</span></a> (<code class="docutils literal notranslate"><span class="pre">relax_transformation.py</span></code>)</p></td>
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diff --git a/docs/deep_dive/tensor_ir/tutorials/sg_execution_times.html b/docs/deep_dive/tensor_ir/tutorials/sg_execution_times.html
index c65eacd31c..efdd071ffa 100644
--- a/docs/deep_dive/tensor_ir/tutorials/sg_execution_times.html
+++ b/docs/deep_dive/tensor_ir/tutorials/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-deep-dive-tensor-ir-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.582</strong> total execution time for <strong>deep_dive_tensor_ir_tutorials</strong> files:</p>
+<p><strong>00:00.536</strong> total execution time for <strong>deep_dive_tensor_ir_tutorials</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -390,11 +390,11 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="tir_transformation.html#sphx-glr-deep-dive-tensor-ir-tutorials-tir-transformation-py"><span class="std std-ref">Transformation</span></a> (<code class="docutils literal notranslate"><span class="pre">tir_transformation.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="tir_creation.html#sphx-glr-deep-dive-tensor-ir-tutorials-tir-creation-py"><span class="std std-ref">TensorIR Creation</span></a> (<code class="docutils literal notranslate"><span class="pre">tir_creation.py</span></code>)</p></td>
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</tbody>
diff --git a/docs/deep_dive/tensor_ir/tutorials/tir_creation.html b/docs/deep_dive/tensor_ir/tutorials/tir_creation.html
index dccbb45c65..af7dfc82d1 100644
--- a/docs/deep_dive/tensor_ir/tutorials/tir_creation.html
+++ b/docs/deep_dive/tensor_ir/tutorials/tir_creation.html
@@ -270,29 +270,9 @@
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/dev/index.html">Development Guides</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
-<ul class="current">
+<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../arch/index.html">Design and Architecture</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">TensorIR</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../abstraction.html">Tensor Program Abstraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../learning.html">Understand TensorIR Abstraction</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">TensorIR Creation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#create-tensorir-using-tvmscript">Create TensorIR using TVMScript</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#standard-format">Standard Format</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#concise-with-syntactic-sugar">Concise with Syntactic Sugar</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#interactive-with-python-variables">Interactive with Python Variables</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#tensorir-function-with-dynamic-shapes">TensorIR Function with Dynamic Shapes</a></li>
-</ul>
-</li>
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-<li class="toctree-l4"><a class="reference internal" href="#create-static-shape-functions">Create Static-Shape Functions</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#create-dynamic-shape-functions">Create Dynamic-Shape Functions</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tir_transformation.html">Transformation</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">TensorIR</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../relax/index.html">Relax</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">API Reference</span></p>
diff --git a/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html b/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html
index 5f29e72b60..f84968edbd 100644
--- a/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html
+++ b/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html
@@ -269,22 +269,9 @@
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/dev/index.html">Development Guides</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
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+<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../arch/index.html">Design and Architecture</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">TensorIR</a><ul class="current">
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-<li class="toctree-l2"><a class="reference internal" href="../learning.html">Understand TensorIR Abstraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tir_creation.html">TensorIR Creation</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="#trace-the-transformation">Trace the Transformation</a></li>
-</ul>
-</li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">TensorIR</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../relax/index.html">Relax</a></li>
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<p class="caption" role="heading"><span class="caption-text">API Reference</span></p>
@@ -470,7 +457,7 @@ original implementation.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 2.3499 2.3499 2.3499 2.3499 0.0000
+ 2.2376 2.2376 2.2376 2.2376 0.0000
</pre></div>
</div>
<div class="section" id="initialization-schedule">
@@ -566,7 +553,7 @@ class Module:
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.9641 0.9641 0.9641 0.9641 0.0000
+ 0.8585 0.8585 0.8585 0.8585 0.0000
</pre></div>
</div>
</div>
@@ -660,7 +647,7 @@ class Module:
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.3365 0.3365 0.3365 0.3365 0.0000
+ 0.3192 0.3192 0.3192 0.3192 0.0000
</pre></div>
</div>
</div>
diff --git a/docs/get_started/tutorials/ir_module.html b/docs/get_started/tutorials/ir_module.html
index 63b3fb87cb..7d55f56279 100644
--- a/docs/get_started/tutorials/ir_module.html
+++ b/docs/get_started/tutorials/ir_module.html
@@ -900,8 +900,8 @@ backends.</p>
<span class="nb">print</span><span class="p">(</span><span class="n">cpu_out</span><span class="p">)</span>
</pre></div>
</div>
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- 0.22715238 0.00922903 0.0203557 -0.12565452]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.2722235 0.3597607 0.05561743 -0.29528138 -0.13545823 -0.11075222
+ -0.1564103 0.05459877 -0.1148698 0.04708464]]
</pre></div>
</div>
</div>
@@ -935,8 +935,8 @@ the details of <code class="docutils literal notranslate"><span class="pre">DLig
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">cpu_out</span><span class="p">,</span> <span class="n">gpu_out</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.07704955 0.22635555 -0.02108881 0.14885929 -0.22873458 0.06792203
- 0.22715238 0.00922902 0.02035576 -0.12565461]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.27222347 0.3597607 0.05561745 -0.29528147 -0.13545828 -0.11075222
+ -0.15641028 0.0545988 -0.11486976 0.04708462]]
</pre></div>
</div>
</div>
diff --git a/docs/get_started/tutorials/sg_execution_times.html b/docs/get_started/tutorials/sg_execution_times.html
index 00f7b9bd46..5740c10118 100644
--- a/docs/get_started/tutorials/sg_execution_times.html
+++ b/docs/get_started/tutorials/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-get-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.113</strong> total execution time for <strong>get_started_tutorials</strong> files:</p>
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<table class="docutils align-default">
<colgroup>
<col style="width: 80%" />
@@ -390,11 +390,11 @@
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="ir_module.html#sphx-glr-get-started-tutorials-ir-module-py"><span class="std std-ref">IRModule</span></a> (<code class="docutils literal notranslate"><span class="pre">ir_module.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="quick_start.html#sphx-glr-get-started-tutorials-quick-start-py"><span class="std std-ref">Quick Start</span></a> (<code class="docutils literal notranslate"><span class="pre">quick_start.py</span></code>)</p></td>
-<td><p>00:00.215</p></td>
+<td><p>00:00.197</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 7a776f5d95..dc44c31bbb 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
@@ -624,7 +624,7 @@ class:['truck 0.9266'] left:471 top:83 right:689 bottom:169
class:['bicycle 0.9984'] left:111 top:113 right:577 bottom:447
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 39.739 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 35.639 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-darknet-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7716f96385bd5abb6e822041e285be54/from_darknet.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_darknet.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_keras.html b/docs/how_to/compile_models/from_keras.html
index 4590647878..abe9c25129 100644
--- a/docs/how_to/compile_models/from_keras.html
+++ b/docs/how_to/compile_models/from_keras.html
@@ -545,7 +545,7 @@ Tensorflow is also required since it’s used as the default backend of keras.</
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 285, class name: Egyptian cat
1/1 [==============================] - ETA: 0s
-1/1 [==============================] - 1s 971ms/step
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Keras top-1 id: 285, class name: Egyptian cat
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index 66b9869910..a0cf6b6017 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -488,13 +488,14 @@ Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdo
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
0%| | 0.00/41.5M [00:00<?, ?B/s]
- 19%|#9 | 7.99M/41.5M [00:00<00:00, 47.6MB/s]
- 35%|###4 | 14.3M/41.5M [00:00<00:00, 43.2MB/s]
- 44%|####4 | 18.4M/41.5M [00:00<00:00, 36.5MB/s]
- 58%|#####7 | 24.0M/41.5M [00:00<00:00, 29.3MB/s]
- 77%|#######7 | 32.0M/41.5M [00:00<00:00, 39.0MB/s]
- 92%|#########2| 38.3M/41.5M [00:01<00:00, 36.0MB/s]
-100%|##########| 41.5M/41.5M [00:01<00:00, 36.8MB/s]
+ 19%|#9 | 7.99M/41.5M [00:00<00:00, 49.5MB/s]
+ 35%|###4 | 14.3M/41.5M [00:00<00:00, 32.5MB/s]
+ 43%|####2 | 17.7M/41.5M [00:00<00:00, 32.0MB/s]
+ 54%|#####3 | 22.3M/41.5M [00:00<00:00, 30.4MB/s]
+ 61%|###### | 25.3M/41.5M [00:00<00:00, 27.1MB/s]
+ 77%|#######7 | 32.0M/41.5M [00:00<00:00, 35.7MB/s]
+ 92%|#########2| 38.3M/41.5M [00:01<00:00, 36.3MB/s]
+100%|##########| 41.5M/41.5M [00:01<00:00, 33.2MB/s]
</pre></div>
</div>
</div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index b066cd1adb..446af49ada 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -523,7 +523,7 @@ To begin, we’ll install PaddlePaddle>=2.1.3:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name: 282: 'tiger cat',
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 9.272 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 4.945 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 3273480369..16d6746876 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -471,17 +471,9 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
0%| | 0.00/44.7M [00:00<?, ?B/s]
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- 67%|######7 | 30.0M/44.7M [00:00<00:00, 37.9MB/s]
- 76%|#######6 | 34.0M/44.7M [00:00<00:00, 38.1MB/s]
- 86%|########6 | 38.5M/44.7M [00:00<00:00, 40.2MB/s]
- 97%|#########7| 43.4M/44.7M [00:01<00:00, 43.3MB/s]
-100%|##########| 44.7M/44.7M [00:01<00:00, 40.3MB/s]
+ 34%|###3 | 15.1M/44.7M [00:00<00:00, 158MB/s]
+ 80%|#######9 | 35.6M/44.7M [00:00<00:00, 191MB/s]
+100%|##########| 44.7M/44.7M [00:00<00:00, 191MB/s]
</pre></div>
</div>
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 84f191f5a9..4f1f839994 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -688,7 +688,7 @@ banana (score = 0.00022)
desk (score = 0.00019)
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 36.197 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 32.598 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index a3b9db6a55..78f2e7f926 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>07:10.069</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>06:46.016</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 81%" />
@@ -390,39 +390,39 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></td>
-<td><p>01:39.739</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></td>
-<td><p>01:36.197</p></td>
+<td><p>01:32.598</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></td>
-<td><p>01:09.272</p></td>
+<td><p>01:04.945</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_oneflow.html#sphx-glr-how-to-compile-models-from-oneflow-py"><span class="std std-ref">Compile OneFlow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_oneflow.py</span></code>)</p></td>
-<td><p>00:47.902</p></td>
+<td><p>00:44.648</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></td>
-<td><p>00:42.212</p></td>
+<td><p>00:39.243</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></td>
-<td><p>00:31.124</p></td>
+<td><p>00:27.235</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></td>
-<td><p>00:28.298</p></td>
+<td><p>00:26.539</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></td>
-<td><p>00:12.312</p></td>
+<td><p>00:12.370</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></td>
-<td><p>00:03.013</p></td>
+<td><p>00:02.800</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 16522d189e..f1ab9fb69c 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -864,10 +864,10 @@ Top5 predictions:
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 4121.0005 4120.2496 4127.3097 4118.5587 2.4621
+ 3991.8919 3991.3090 3995.4704 3988.8212 2.2194
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 22.353 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.100 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2387d8448da213eb625e6b3d916327d4/deploy_model_on_adreno.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index fa1a7edb7b..5ef4c01b5e 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
@@ -419,7 +419,7 @@
<div class="sphx-glr-example-title section" id="deploy-the-pretrained-model-on-adreno-with-tvmc-interface">
<span id="tutorial-deploy-model-on-adreno-tvmc"></span><span id="sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"></span><h1>Deploy the Pretrained Model on Adreno™ with tvmc Interface<a class="headerlink" href="#deploy-the-pretrained-model-on-adreno-with-tvmc-interface" title="Permalink to this headline">¶</a></h1>
<p><strong>Author</strong>: Siva Rama Krishna</p>
-<p>This article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno™.</p>
+<p>This article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 model on Adreno™.</p>
<p>Besides that, you should have TVM built for Android.
See the following instructions on how to build it and setup RPC environment.</p>
<p><a class="reference external" href="https://tvm.apache.org/docs/how_to/deploy/adreno.html">Deploy to Adreno GPU</a></p>
@@ -464,45 +464,40 @@ to run this tutorial with a real device over rpc.</p>
</pre></div>
</div>
</div>
-<div class="section" id="make-a-keras-resnet50-model">
-<h2>Make a Keras Resnet50 Model<a class="headerlink" href="#make-a-keras-resnet50-model" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.keras.applications.resnet50</span> <span class="kn">import</span> <span class="n">ResNet50</span>
+<div class="section" id="make-a-pytorch-resnet50-model">
+<h2>Make a PyTorch Resnet50 Model<a class="headerlink" href="#make-a-pytorch-resnet50-model" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
-<a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory" title="tvm.contrib.utils.TempDirectory" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmp_path</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.tempdir" title="tvm.contrib.utils.tempdir" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-function"><span cl [...]
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_file_name</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory.relpath" title="tvm.contrib.utils.TempDirectory.relpath" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-method"><span class="n">tmp_pat [...]
+<span class="c1"># Load the ResNet50 model pre-trained on ImageNet</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet50</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">ResNet50</span><span class="p">(</span><span class="n">include_top</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="s2">"imagenet"</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi [...]
-<span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_file_name</span></a><span class="p">)</span>
+<span class="c1"># Set the model to evaluation mode</span>
+<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+
+<span class="c1"># Define the input shape</span>
+<span class="n">dummy_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
+
+<span class="c1"># Trace the model</span>
+<span class="n">traced_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dummy_input</span><span class="p">)</span>
+
+<span class="c1"># Save the traced model</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_file_name</span></a> <span class="o">=</span> <span class="s2">"resnet50_traced.pt"</span>
+<span class="n">traced_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_file_name</span></a><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
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-102967424/102967424 [==============================] - 2s 0us/step
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+ warnings.warn(
+/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
+ warnings.warn(msg)
+Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /workspace/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
+
+ 0%| | 0.00/97.8M [00:00<?, ?B/s]
+ 17%|#6 | 16.5M/97.8M [00:00<00:00, 173MB/s]
+ 42%|####1 | 40.8M/97.8M [00:00<00:00, 220MB/s]
+ 66%|######5 | 64.2M/97.8M [00:00<00:00, 232MB/s]
+ 90%|########9 | 87.8M/97.8M [00:00<00:00, 237MB/s]
+100%|##########| 97.8M/97.8M [00:00<00:00, 231MB/s]
</pre></div>
</div>
</div>
@@ -510,299 +505,360 @@ to run this tutorial with a real device over rpc.</p>
<h2>Load Model<a class="headerlink" href="#load-model" title="Permalink to this headline">¶</a></h2>
<p>Convert a model from any framework to a tvm relay module.
tvmc.load supports models from any framework (like tensorflow saves_model, onnx, tflite ..etc) and auto detects the filetype.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tvmc_model</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_file_name</span></a><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_shape</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span cl [...]
+
+<span class="c1"># Load the TorchScript model with TVMC</span>
+<span class="n">tvmc_model</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_file_name</span></a><span class="p">,</span> <span class="n">shape_dict</span><span class="o">=</span><span class="p">{</span><s [...]
<span class="nb">print</span><span class="p">(</span><span class="n">tvmc_model</span><span class="o">.</span><span class="n">mod</span><span class="p">)</span>
<span class="c1"># tvmc_model consists of tvmc_mode.mod which is relay module and tvmc_model.params which parms of the module.</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>def @main(%input_2: Tensor[(1, 224, 224, 3), float32], %v_param_1: Tensor[(7, 7, 3, 64), float32], %v_param_2: Tensor[(64), float32], %v_param_3: Tensor[(64), float32], %v_param_4: Tensor[(64), float32], %v_param_5: Tensor[(64), float32], %v_param_6: Tensor[(64), float32], %v_param_19: Tensor[(1, 1, 64, 256), float32], %v_param_20: Tensor[(256), float32], %v_param_23: Tensor[(256), float3 [...]
- %0 = nn.pad(%input_2, 0, pad_width=[[0, 0], [3, 3], [3, 3], [0, 0]]);
- %1 = nn.conv2d(%0, %v_param_1, strides=[2, 2], padding=[0, 0, 0, 0], channels=64, kernel_size=[7, 7], data_layout="NHWC", kernel_layout="HWIO");
- %2 = nn.bias_add(%1, %v_param_2, axis=-1);
- %3 = nn.batch_norm(%2, %v_param_3, %v_param_4, %v_param_5, %v_param_6, axis=3, epsilon=1.001e-05f);
- %4 = %3.0;
- %5 = nn.relu(%4);
- %6 = nn.pad(%5, 0, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]);
- %7 = nn.max_pool2d(%6, pool_size=[3, 3], strides=[2, 2], padding=[0, 0, 0, 0], layout="NHWC");
- %8 = nn.conv2d(%7, %v_param_19, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %9 = nn.bias_add(%8, %v_param_20, axis=-1);
- %10 = nn.batch_norm(%9, %v_param_23, %v_param_24, %v_param_25, %v_param_26, axis=3, epsilon=1.001e-05f);
- %11 = nn.conv2d(%7, %v_param_7, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %12 = nn.bias_add(%11, %v_param_8, axis=-1);
- %13 = nn.batch_norm(%12, %v_param_9, %v_param_10, %v_param_11, %v_param_12, axis=3, epsilon=1.001e-05f);
- %14 = %13.0;
- %15 = nn.relu(%14);
- %16 = nn.conv2d(%15, %v_param_13, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %17 = nn.bias_add(%16, %v_param_14, axis=-1);
- %18 = nn.batch_norm(%17, %v_param_15, %v_param_16, %v_param_17, %v_param_18, axis=3, epsilon=1.001e-05f);
- %19 = %18.0;
- %20 = nn.relu(%19);
- %21 = nn.conv2d(%20, %v_param_21, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %22 = nn.bias_add(%21, %v_param_22, axis=-1);
- %23 = nn.batch_norm(%22, %v_param_27, %v_param_28, %v_param_29, %v_param_30, axis=3, epsilon=1.001e-05f);
- %24 = %10.0;
- %25 = %23.0;
- %26 = add(%24, %25);
- %27 = nn.relu(%26);
- %28 = nn.conv2d(%27, %v_param_31, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %29 = nn.bias_add(%28, %v_param_32, axis=-1);
- %30 = nn.batch_norm(%29, %v_param_33, %v_param_34, %v_param_35, %v_param_36, axis=3, epsilon=1.001e-05f);
- %31 = %30.0;
- %32 = nn.relu(%31);
- %33 = nn.conv2d(%32, %v_param_37, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %34 = nn.bias_add(%33, %v_param_38, axis=-1);
- %35 = nn.batch_norm(%34, %v_param_39, %v_param_40, %v_param_41, %v_param_42, axis=3, epsilon=1.001e-05f);
- %36 = %35.0;
- %37 = nn.relu(%36);
- %38 = nn.conv2d(%37, %v_param_43, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %39 = nn.bias_add(%38, %v_param_44, axis=-1);
- %40 = nn.batch_norm(%39, %v_param_45, %v_param_46, %v_param_47, %v_param_48, axis=3, epsilon=1.001e-05f);
- %41 = %40.0;
- %42 = add(%27, %41);
- %43 = nn.relu(%42);
- %44 = nn.conv2d(%43, %v_param_49, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %45 = nn.bias_add(%44, %v_param_50, axis=-1);
- %46 = nn.batch_norm(%45, %v_param_51, %v_param_52, %v_param_53, %v_param_54, axis=3, epsilon=1.001e-05f);
- %47 = %46.0;
- %48 = nn.relu(%47);
- %49 = nn.conv2d(%48, %v_param_55, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %50 = nn.bias_add(%49, %v_param_56, axis=-1);
- %51 = nn.batch_norm(%50, %v_param_57, %v_param_58, %v_param_59, %v_param_60, axis=3, epsilon=1.001e-05f);
- %52 = %51.0;
- %53 = nn.relu(%52);
- %54 = nn.conv2d(%53, %v_param_61, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %55 = nn.bias_add(%54, %v_param_62, axis=-1);
- %56 = nn.batch_norm(%55, %v_param_63, %v_param_64, %v_param_65, %v_param_66, axis=3, epsilon=1.001e-05f);
- %57 = %56.0;
- %58 = add(%43, %57);
- %59 = nn.relu(%58);
- %60 = nn.conv2d(%59, %v_param_79, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %61 = nn.bias_add(%60, %v_param_80, axis=-1);
- %62 = nn.batch_norm(%61, %v_param_83, %v_param_84, %v_param_85, %v_param_86, axis=3, epsilon=1.001e-05f);
- %63 = nn.conv2d(%59, %v_param_67, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %64 = nn.bias_add(%63, %v_param_68, axis=-1);
- %65 = nn.batch_norm(%64, %v_param_69, %v_param_70, %v_param_71, %v_param_72, axis=3, epsilon=1.001e-05f);
- %66 = %65.0;
- %67 = nn.relu(%66);
- %68 = nn.conv2d(%67, %v_param_73, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %69 = nn.bias_add(%68, %v_param_74, axis=-1);
- %70 = nn.batch_norm(%69, %v_param_75, %v_param_76, %v_param_77, %v_param_78, axis=3, epsilon=1.001e-05f);
- %71 = %70.0;
- %72 = nn.relu(%71);
- %73 = nn.conv2d(%72, %v_param_81, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %74 = nn.bias_add(%73, %v_param_82, axis=-1);
- %75 = nn.batch_norm(%74, %v_param_87, %v_param_88, %v_param_89, %v_param_90, axis=3, epsilon=1.001e-05f);
- %76 = %62.0;
- %77 = %75.0;
- %78 = add(%76, %77);
- %79 = nn.relu(%78);
- %80 = nn.conv2d(%79, %v_param_91, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %81 = nn.bias_add(%80, %v_param_92, axis=-1);
- %82 = nn.batch_norm(%81, %v_param_93, %v_param_94, %v_param_95, %v_param_96, axis=3, epsilon=1.001e-05f);
- %83 = %82.0;
- %84 = nn.relu(%83);
- %85 = nn.conv2d(%84, %v_param_97, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %86 = nn.bias_add(%85, %v_param_98, axis=-1);
- %87 = nn.batch_norm(%86, %v_param_99, %v_param_100, %v_param_101, %v_param_102, axis=3, epsilon=1.001e-05f);
- %88 = %87.0;
- %89 = nn.relu(%88);
- %90 = nn.conv2d(%89, %v_param_103, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %91 = nn.bias_add(%90, %v_param_104, axis=-1);
- %92 = nn.batch_norm(%91, %v_param_105, %v_param_106, %v_param_107, %v_param_108, axis=3, epsilon=1.001e-05f);
- %93 = %92.0;
- %94 = add(%79, %93);
- %95 = nn.relu(%94);
- %96 = nn.conv2d(%95, %v_param_109, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %97 = nn.bias_add(%96, %v_param_110, axis=-1);
- %98 = nn.batch_norm(%97, %v_param_111, %v_param_112, %v_param_113, %v_param_114, axis=3, epsilon=1.001e-05f);
- %99 = %98.0;
- %100 = nn.relu(%99);
- %101 = nn.conv2d(%100, %v_param_115, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %102 = nn.bias_add(%101, %v_param_116, axis=-1);
- %103 = nn.batch_norm(%102, %v_param_117, %v_param_118, %v_param_119, %v_param_120, axis=3, epsilon=1.001e-05f);
- %104 = %103.0;
- %105 = nn.relu(%104);
- %106 = nn.conv2d(%105, %v_param_121, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %107 = nn.bias_add(%106, %v_param_122, axis=-1);
- %108 = nn.batch_norm(%107, %v_param_123, %v_param_124, %v_param_125, %v_param_126, axis=3, epsilon=1.001e-05f);
- %109 = %108.0;
- %110 = add(%95, %109);
- %111 = nn.relu(%110);
- %112 = nn.conv2d(%111, %v_param_127, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %113 = nn.bias_add(%112, %v_param_128, axis=-1);
- %114 = nn.batch_norm(%113, %v_param_129, %v_param_130, %v_param_131, %v_param_132, axis=3, epsilon=1.001e-05f);
- %115 = %114.0;
- %116 = nn.relu(%115);
- %117 = nn.conv2d(%116, %v_param_133, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %118 = nn.bias_add(%117, %v_param_134, axis=-1);
- %119 = nn.batch_norm(%118, %v_param_135, %v_param_136, %v_param_137, %v_param_138, axis=3, epsilon=1.001e-05f);
- %120 = %119.0;
- %121 = nn.relu(%120);
- %122 = nn.conv2d(%121, %v_param_139, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %123 = nn.bias_add(%122, %v_param_140, axis=-1);
- %124 = nn.batch_norm(%123, %v_param_141, %v_param_142, %v_param_143, %v_param_144, axis=3, epsilon=1.001e-05f);
- %125 = %124.0;
- %126 = add(%111, %125);
- %127 = nn.relu(%126);
- %128 = nn.conv2d(%127, %v_param_157, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %129 = nn.bias_add(%128, %v_param_158, axis=-1);
- %130 = nn.batch_norm(%129, %v_param_161, %v_param_162, %v_param_163, %v_param_164, axis=3, epsilon=1.001e-05f);
- %131 = nn.conv2d(%127, %v_param_145, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %132 = nn.bias_add(%131, %v_param_146, axis=-1);
- %133 = nn.batch_norm(%132, %v_param_147, %v_param_148, %v_param_149, %v_param_150, axis=3, epsilon=1.001e-05f);
- %134 = %133.0;
- %135 = nn.relu(%134);
- %136 = nn.conv2d(%135, %v_param_151, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %137 = nn.bias_add(%136, %v_param_152, axis=-1);
- %138 = nn.batch_norm(%137, %v_param_153, %v_param_154, %v_param_155, %v_param_156, axis=3, epsilon=1.001e-05f);
- %139 = %138.0;
- %140 = nn.relu(%139);
- %141 = nn.conv2d(%140, %v_param_159, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %142 = nn.bias_add(%141, %v_param_160, axis=-1);
- %143 = nn.batch_norm(%142, %v_param_165, %v_param_166, %v_param_167, %v_param_168, axis=3, epsilon=1.001e-05f);
- %144 = %130.0;
- %145 = %143.0;
- %146 = add(%144, %145);
- %147 = nn.relu(%146);
- %148 = nn.conv2d(%147, %v_param_169, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %149 = nn.bias_add(%148, %v_param_170, axis=-1);
- %150 = nn.batch_norm(%149, %v_param_171, %v_param_172, %v_param_173, %v_param_174, axis=3, epsilon=1.001e-05f);
- %151 = %150.0;
- %152 = nn.relu(%151);
- %153 = nn.conv2d(%152, %v_param_175, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %154 = nn.bias_add(%153, %v_param_176, axis=-1);
- %155 = nn.batch_norm(%154, %v_param_177, %v_param_178, %v_param_179, %v_param_180, axis=3, epsilon=1.001e-05f);
- %156 = %155.0;
- %157 = nn.relu(%156);
- %158 = nn.conv2d(%157, %v_param_181, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %159 = nn.bias_add(%158, %v_param_182, axis=-1);
- %160 = nn.batch_norm(%159, %v_param_183, %v_param_184, %v_param_185, %v_param_186, axis=3, epsilon=1.001e-05f);
- %161 = %160.0;
- %162 = add(%147, %161);
- %163 = nn.relu(%162);
- %164 = nn.conv2d(%163, %v_param_187, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %165 = nn.bias_add(%164, %v_param_188, axis=-1);
- %166 = nn.batch_norm(%165, %v_param_189, %v_param_190, %v_param_191, %v_param_192, axis=3, epsilon=1.001e-05f);
- %167 = %166.0;
- %168 = nn.relu(%167);
- %169 = nn.conv2d(%168, %v_param_193, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %170 = nn.bias_add(%169, %v_param_194, axis=-1);
- %171 = nn.batch_norm(%170, %v_param_195, %v_param_196, %v_param_197, %v_param_198, axis=3, epsilon=1.001e-05f);
- %172 = %171.0;
- %173 = nn.relu(%172);
- %174 = nn.conv2d(%173, %v_param_199, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %175 = nn.bias_add(%174, %v_param_200, axis=-1);
- %176 = nn.batch_norm(%175, %v_param_201, %v_param_202, %v_param_203, %v_param_204, axis=3, epsilon=1.001e-05f);
- %177 = %176.0;
- %178 = add(%163, %177);
- %179 = nn.relu(%178);
- %180 = nn.conv2d(%179, %v_param_205, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %181 = nn.bias_add(%180, %v_param_206, axis=-1);
- %182 = nn.batch_norm(%181, %v_param_207, %v_param_208, %v_param_209, %v_param_210, axis=3, epsilon=1.001e-05f);
- %183 = %182.0;
- %184 = nn.relu(%183);
- %185 = nn.conv2d(%184, %v_param_211, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %186 = nn.bias_add(%185, %v_param_212, axis=-1);
- %187 = nn.batch_norm(%186, %v_param_213, %v_param_214, %v_param_215, %v_param_216, axis=3, epsilon=1.001e-05f);
- %188 = %187.0;
- %189 = nn.relu(%188);
- %190 = nn.conv2d(%189, %v_param_217, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %191 = nn.bias_add(%190, %v_param_218, axis=-1);
- %192 = nn.batch_norm(%191, %v_param_219, %v_param_220, %v_param_221, %v_param_222, axis=3, epsilon=1.001e-05f);
- %193 = %192.0;
- %194 = add(%179, %193);
- %195 = nn.relu(%194);
- %196 = nn.conv2d(%195, %v_param_223, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %197 = nn.bias_add(%196, %v_param_224, axis=-1);
- %198 = nn.batch_norm(%197, %v_param_225, %v_param_226, %v_param_227, %v_param_228, axis=3, epsilon=1.001e-05f);
- %199 = %198.0;
- %200 = nn.relu(%199);
- %201 = nn.conv2d(%200, %v_param_229, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %202 = nn.bias_add(%201, %v_param_230, axis=-1);
- %203 = nn.batch_norm(%202, %v_param_231, %v_param_232, %v_param_233, %v_param_234, axis=3, epsilon=1.001e-05f);
- %204 = %203.0;
- %205 = nn.relu(%204);
- %206 = nn.conv2d(%205, %v_param_235, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %207 = nn.bias_add(%206, %v_param_236, axis=-1);
- %208 = nn.batch_norm(%207, %v_param_237, %v_param_238, %v_param_239, %v_param_240, axis=3, epsilon=1.001e-05f);
- %209 = %208.0;
- %210 = add(%195, %209);
- %211 = nn.relu(%210);
- %212 = nn.conv2d(%211, %v_param_241, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %213 = nn.bias_add(%212, %v_param_242, axis=-1);
- %214 = nn.batch_norm(%213, %v_param_243, %v_param_244, %v_param_245, %v_param_246, axis=3, epsilon=1.001e-05f);
- %215 = %214.0;
- %216 = nn.relu(%215);
- %217 = nn.conv2d(%216, %v_param_247, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %218 = nn.bias_add(%217, %v_param_248, axis=-1);
- %219 = nn.batch_norm(%218, %v_param_249, %v_param_250, %v_param_251, %v_param_252, axis=3, epsilon=1.001e-05f);
- %220 = %219.0;
- %221 = nn.relu(%220);
- %222 = nn.conv2d(%221, %v_param_253, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %223 = nn.bias_add(%222, %v_param_254, axis=-1);
- %224 = nn.batch_norm(%223, %v_param_255, %v_param_256, %v_param_257, %v_param_258, axis=3, epsilon=1.001e-05f);
- %225 = %224.0;
- %226 = add(%211, %225);
- %227 = nn.relu(%226);
- %228 = nn.conv2d(%227, %v_param_271, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %229 = nn.bias_add(%228, %v_param_272, axis=-1);
- %230 = nn.batch_norm(%229, %v_param_275, %v_param_276, %v_param_277, %v_param_278, axis=3, epsilon=1.001e-05f);
- %231 = nn.conv2d(%227, %v_param_259, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %232 = nn.bias_add(%231, %v_param_260, axis=-1);
- %233 = nn.batch_norm(%232, %v_param_261, %v_param_262, %v_param_263, %v_param_264, axis=3, epsilon=1.001e-05f);
- %234 = %233.0;
- %235 = nn.relu(%234);
- %236 = nn.conv2d(%235, %v_param_265, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %237 = nn.bias_add(%236, %v_param_266, axis=-1);
- %238 = nn.batch_norm(%237, %v_param_267, %v_param_268, %v_param_269, %v_param_270, axis=3, epsilon=1.001e-05f);
- %239 = %238.0;
- %240 = nn.relu(%239);
- %241 = nn.conv2d(%240, %v_param_273, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %242 = nn.bias_add(%241, %v_param_274, axis=-1);
- %243 = nn.batch_norm(%242, %v_param_279, %v_param_280, %v_param_281, %v_param_282, axis=3, epsilon=1.001e-05f);
- %244 = %230.0;
- %245 = %243.0;
- %246 = add(%244, %245);
- %247 = nn.relu(%246);
- %248 = nn.conv2d(%247, %v_param_283, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %249 = nn.bias_add(%248, %v_param_284, axis=-1);
- %250 = nn.batch_norm(%249, %v_param_285, %v_param_286, %v_param_287, %v_param_288, axis=3, epsilon=1.001e-05f);
- %251 = %250.0;
- %252 = nn.relu(%251);
- %253 = nn.conv2d(%252, %v_param_289, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %254 = nn.bias_add(%253, %v_param_290, axis=-1);
- %255 = nn.batch_norm(%254, %v_param_291, %v_param_292, %v_param_293, %v_param_294, axis=3, epsilon=1.001e-05f);
- %256 = %255.0;
- %257 = nn.relu(%256);
- %258 = nn.conv2d(%257, %v_param_295, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %259 = nn.bias_add(%258, %v_param_296, axis=-1);
- %260 = nn.batch_norm(%259, %v_param_297, %v_param_298, %v_param_299, %v_param_300, axis=3, epsilon=1.001e-05f);
- %261 = %260.0;
- %262 = add(%247, %261);
- %263 = nn.relu(%262);
- %264 = nn.conv2d(%263, %v_param_301, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %265 = nn.bias_add(%264, %v_param_302, axis=-1);
- %266 = nn.batch_norm(%265, %v_param_303, %v_param_304, %v_param_305, %v_param_306, axis=3, epsilon=1.001e-05f);
- %267 = %266.0;
- %268 = nn.relu(%267);
- %269 = nn.conv2d(%268, %v_param_307, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
- %270 = nn.bias_add(%269, %v_param_308, axis=-1);
- %271 = nn.batch_norm(%270, %v_param_309, %v_param_310, %v_param_311, %v_param_312, axis=3, epsilon=1.001e-05f);
- %272 = %271.0;
- %273 = nn.relu(%272);
- %274 = nn.conv2d(%273, %v_param_313, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
- %275 = nn.bias_add(%274, %v_param_314, axis=-1);
- %276 = nn.batch_norm(%275, %v_param_315, %v_param_316, %v_param_317, %v_param_318, axis=3, epsilon=1.001e-05f);
- %277 = %276.0;
- %278 = add(%263, %277);
- %279 = nn.relu(%278);
- %280 = nn.global_avg_pool2d(%279, layout="NHWC");
- %281 = nn.batch_flatten(%280);
- %282 = nn.dense(%281, %v_param_319, units=1000);
- %283 = nn.bias_add(%282, %v_param_320);
- nn.softmax(%283)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>type List[A] {
+ Cons(A, List[A]),
+ Nil,
+}
+
+type Option[A] {
+ Some(A),
+ None,
+}
+
+type Tree[A] {
+ Rose(A, List[Tree[A]]),
+}
+
+type tensor_float16_t {
+ tensor_nil_float16,
+ tensor0_float16(float16),
+ tensor1_float16(Tensor[(?), float16]),
+ tensor2_float16(Tensor[(?, ?), float16]),
+ tensor3_float16(Tensor[(?, ?, ?), float16]),
+ tensor4_float16(Tensor[(?, ?, ?, ?), float16]),
+ tensor5_float16(Tensor[(?, ?, ?, ?, ?), float16]),
+ tensor6_float16(Tensor[(?, ?, ?, ?, ?, ?), float16]),
+}
+
+type tensor_float32_t {
+ tensor_nil_float32,
+ tensor0_float32(float32),
+ tensor1_float32(Tensor[(?), float32]),
+ tensor2_float32(Tensor[(?, ?), float32]),
+ tensor3_float32(Tensor[(?, ?, ?), float32]),
+ tensor4_float32(Tensor[(?, ?, ?, ?), float32]),
+ tensor5_float32(Tensor[(?, ?, ?, ?, ?), float32]),
+ tensor6_float32(Tensor[(?, ?, ?, ?, ?, ?), float32]),
+}
+
+type tensor_float64_t {
+ tensor_nil_float64,
+ tensor0_float64(float64),
+ tensor1_float64(Tensor[(?), float64]),
+ tensor2_float64(Tensor[(?, ?), float64]),
+ tensor3_float64(Tensor[(?, ?, ?), float64]),
+ tensor4_float64(Tensor[(?, ?, ?, ?), float64]),
+ tensor5_float64(Tensor[(?, ?, ?, ?, ?), float64]),
+ tensor6_float64(Tensor[(?, ?, ?, ?, ?, ?), float64]),
+}
+
+type tensor_int16_t {
+ tensor_nil_int16,
+ tensor0_int16(int16),
+ tensor1_int16(Tensor[(?), int16]),
+ tensor2_int16(Tensor[(?, ?), int16]),
+ tensor3_int16(Tensor[(?, ?, ?), int16]),
+ tensor4_int16(Tensor[(?, ?, ?, ?), int16]),
+ tensor5_int16(Tensor[(?, ?, ?, ?, ?), int16]),
+ tensor6_int16(Tensor[(?, ?, ?, ?, ?, ?), int16]),
+}
+
+type tensor_int32_t {
+ tensor_nil_int32,
+ tensor0_int32(int32),
+ tensor1_int32(Tensor[(?), int32]),
+ tensor2_int32(Tensor[(?, ?), int32]),
+ tensor3_int32(Tensor[(?, ?, ?), int32]),
+ tensor4_int32(Tensor[(?, ?, ?, ?), int32]),
+ tensor5_int32(Tensor[(?, ?, ?, ?, ?), int32]),
+ tensor6_int32(Tensor[(?, ?, ?, ?, ?, ?), int32]),
+}
+
+type tensor_int64_t {
+ tensor_nil_int64,
+ tensor0_int64(int64),
+ tensor1_int64(Tensor[(?), int64]),
+ tensor2_int64(Tensor[(?, ?), int64]),
+ tensor3_int64(Tensor[(?, ?, ?), int64]),
+ tensor4_int64(Tensor[(?, ?, ?, ?), int64]),
+ tensor5_int64(Tensor[(?, ?, ?, ?, ?), int64]),
+ tensor6_int64(Tensor[(?, ?, ?, ?, ?, ?), int64]),
+}
+
+type tensor_int8_t {
+ tensor_nil_int8,
+ tensor0_int8(int8),
+ tensor1_int8(Tensor[(?), int8]),
+ tensor2_int8(Tensor[(?, ?), int8]),
+ tensor3_int8(Tensor[(?, ?, ?), int8]),
+ tensor4_int8(Tensor[(?, ?, ?, ?), int8]),
+ tensor5_int8(Tensor[(?, ?, ?, ?, ?), int8]),
+ tensor6_int8(Tensor[(?, ?, ?, ?, ?, ?), int8]),
+}
+
+type tensor_uint16_t {
+ tensor_nil_uint16,
+ tensor0_uint16(uint16),
+ tensor1_uint16(Tensor[(?), uint16]),
+ tensor2_uint16(Tensor[(?, ?), uint16]),
+ tensor3_uint16(Tensor[(?, ?, ?), uint16]),
+ tensor4_uint16(Tensor[(?, ?, ?, ?), uint16]),
+ tensor5_uint16(Tensor[(?, ?, ?, ?, ?), uint16]),
+ tensor6_uint16(Tensor[(?, ?, ?, ?, ?, ?), uint16]),
+}
+
+type tensor_uint8_t {
+ tensor_nil_uint8,
+ tensor0_uint8(uint8),
+ tensor1_uint8(Tensor[(?), uint8]),
+ tensor2_uint8(Tensor[(?, ?), uint8]),
+ tensor3_uint8(Tensor[(?, ?, ?), uint8]),
+ tensor4_uint8(Tensor[(?, ?, ?, ?), uint8]),
+ tensor5_uint8(Tensor[(?, ?, ?, ?, ?), uint8]),
+ tensor6_uint8(Tensor[(?, ?, ?, ?, ?, ?), uint8]),
+}
+
+def @main(%input: Tensor[(1, 3, 224, 224), float32] /* span=aten::_convolution_0.input:0:0 */, %aten::_convolution_0.weight: Tensor[(64, 3, 7, 7), float32] /* span=aten::_convolution_0.weight:0:0 */, %aten::batch_norm_0.weight: Tensor[(64), float32] /* span=aten::batch_norm_0.weight:0:0 */, %aten::batch_norm_0.bias: Tensor[(64), float32] /* span=aten::batch_norm_0.bias:0:0 */, %aten::batch_norm_0.running_mean: Tensor[(64), float32] /* span=aten::batch_norm_0.running_mean:0:0 */, %aten::b [...]
+ %0 = nn.conv2d(%input, %aten::_convolution_0.weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) /* span=aten::_convolution_0:0:0 */;
+ %1 = nn.batch_norm(%0, %aten::batch_norm_0.weight, %aten::batch_norm_0.bias, %aten::batch_norm_0.running_mean, %aten::batch_norm_0.running_var) /* span=aten::batch_norm_0:0:0 */;
+ %2 = %1.0 /* span=aten::batch_norm_0:0:0 */;
+ %3 = nn.relu(%2) /* span=aten::relu__0:0:0 */;
+ %4 = nn.max_pool2d(%3, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 1]) /* span=aten::max_pool2d_0:0:0 */;
+ %5 = nn.conv2d(%4, %aten::_convolution_1.weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* span=aten::_convolution_1:0:0 */;
+ %6 = nn.batch_norm(%5, %aten::batch_norm_1.weight, %aten::batch_norm_1.bias, %aten::batch_norm_1.running_mean, %aten::batch_norm_1.running_var) /* span=aten::batch_norm_1:0:0 */;
+ %7 = %6.0 /* span=aten::batch_norm_1:0:0 */;
+ %8 = nn.relu(%7) /* span=aten::relu__1:0:0 */;
+ %9 = nn.conv2d(%8, %aten::_convolution_2.weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* span=aten::_convolution_2:0:0 */;
+ %10 = nn.batch_norm(%9, %aten::batch_norm_2.weight, %aten::batch_norm_2.bias, %aten::batch_norm_2.running_mean, %aten::batch_norm_2.running_var) /* span=aten::batch_norm_2:0:0 */;
+ %11 = %10.0 /* span=aten::batch_norm_2:0:0 */;
+ %12 = nn.relu(%11) /* span=aten::relu__2:0:0 */;
+ %13 = nn.conv2d(%12, %aten::_convolution_3.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_3:0:0 */;
+ %14 = nn.batch_norm(%13, %aten::batch_norm_3.weight, %aten::batch_norm_3.bias, %aten::batch_norm_3.running_mean, %aten::batch_norm_3.running_var) /* span=aten::batch_norm_3:0:0 */;
+ %15 = nn.conv2d(%4, %aten::_convolution_4.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_4:0:0 */;
+ %16 = nn.batch_norm(%15, %aten::batch_norm_4.weight, %aten::batch_norm_4.bias, %aten::batch_norm_4.running_mean, %aten::batch_norm_4.running_var) /* span=aten::batch_norm_4:0:0 */;
+ %17 = %14.0 /* span=aten::batch_norm_3:0:0 */;
+ %18 = %16.0 /* span=aten::batch_norm_4:0:0 */;
+ %19 = add(%17, %18) /* span=aten::add__0:0:0 */;
+ %20 = nn.relu(%19) /* span=aten::relu__3:0:0 */;
+ %21 = nn.conv2d(%20, %aten::_convolution_5.weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* span=aten::_convolution_5:0:0 */;
+ %22 = nn.batch_norm(%21, %aten::batch_norm_5.weight, %aten::batch_norm_5.bias, %aten::batch_norm_5.running_mean, %aten::batch_norm_5.running_var) /* span=aten::batch_norm_5:0:0 */;
+ %23 = %22.0 /* span=aten::batch_norm_5:0:0 */;
+ %24 = nn.relu(%23) /* span=aten::relu__4:0:0 */;
+ %25 = nn.conv2d(%24, %aten::_convolution_6.weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* span=aten::_convolution_6:0:0 */;
+ %26 = nn.batch_norm(%25, %aten::batch_norm_6.weight, %aten::batch_norm_6.bias, %aten::batch_norm_6.running_mean, %aten::batch_norm_6.running_var) /* span=aten::batch_norm_6:0:0 */;
+ %27 = %26.0 /* span=aten::batch_norm_6:0:0 */;
+ %28 = nn.relu(%27) /* span=aten::relu__5:0:0 */;
+ %29 = nn.conv2d(%28, %aten::_convolution_7.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_7:0:0 */;
+ %30 = nn.batch_norm(%29, %aten::batch_norm_7.weight, %aten::batch_norm_7.bias, %aten::batch_norm_7.running_mean, %aten::batch_norm_7.running_var) /* span=aten::batch_norm_7:0:0 */;
+ %31 = %30.0 /* span=aten::batch_norm_7:0:0 */;
+ %32 = add(%31, %20) /* span=aten::add__1:0:0 */;
+ %33 = nn.relu(%32) /* span=aten::relu__6:0:0 */;
+ %34 = nn.conv2d(%33, %aten::_convolution_8.weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* span=aten::_convolution_8:0:0 */;
+ %35 = nn.batch_norm(%34, %aten::batch_norm_8.weight, %aten::batch_norm_8.bias, %aten::batch_norm_8.running_mean, %aten::batch_norm_8.running_var) /* span=aten::batch_norm_8:0:0 */;
+ %36 = %35.0 /* span=aten::batch_norm_8:0:0 */;
+ %37 = nn.relu(%36) /* span=aten::relu__7:0:0 */;
+ %38 = nn.conv2d(%37, %aten::_convolution_9.weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* span=aten::_convolution_9:0:0 */;
+ %39 = nn.batch_norm(%38, %aten::batch_norm_9.weight, %aten::batch_norm_9.bias, %aten::batch_norm_9.running_mean, %aten::batch_norm_9.running_var) /* span=aten::batch_norm_9:0:0 */;
+ %40 = %39.0 /* span=aten::batch_norm_9:0:0 */;
+ %41 = nn.relu(%40) /* span=aten::relu__8:0:0 */;
+ %42 = nn.conv2d(%41, %aten::_convolution_10.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_10:0:0 */;
+ %43 = nn.batch_norm(%42, %aten::batch_norm_10.weight, %aten::batch_norm_10.bias, %aten::batch_norm_10.running_mean, %aten::batch_norm_10.running_var) /* span=aten::batch_norm_10:0:0 */;
+ %44 = %43.0 /* span=aten::batch_norm_10:0:0 */;
+ %45 = add(%44, %33) /* span=aten::add__2:0:0 */;
+ %46 = nn.relu(%45) /* span=aten::relu__9:0:0 */;
+ %47 = nn.conv2d(%46, %aten::_convolution_11.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_11:0:0 */;
+ %48 = nn.batch_norm(%47, %aten::batch_norm_11.weight, %aten::batch_norm_11.bias, %aten::batch_norm_11.running_mean, %aten::batch_norm_11.running_var) /* span=aten::batch_norm_11:0:0 */;
+ %49 = %48.0 /* span=aten::batch_norm_11:0:0 */;
+ %50 = nn.relu(%49) /* span=aten::relu__10:0:0 */;
+ %51 = nn.conv2d(%50, %aten::_convolution_12.weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_12:0:0 */;
+ %52 = nn.batch_norm(%51, %aten::batch_norm_12.weight, %aten::batch_norm_12.bias, %aten::batch_norm_12.running_mean, %aten::batch_norm_12.running_var) /* span=aten::batch_norm_12:0:0 */;
+ %53 = %52.0 /* span=aten::batch_norm_12:0:0 */;
+ %54 = nn.relu(%53) /* span=aten::relu__11:0:0 */;
+ %55 = nn.conv2d(%54, %aten::_convolution_13.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_13:0:0 */;
+ %56 = nn.batch_norm(%55, %aten::batch_norm_13.weight, %aten::batch_norm_13.bias, %aten::batch_norm_13.running_mean, %aten::batch_norm_13.running_var) /* span=aten::batch_norm_13:0:0 */;
+ %57 = nn.conv2d(%46, %aten::_convolution_14.weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_14:0:0 */;
+ %58 = nn.batch_norm(%57, %aten::batch_norm_14.weight, %aten::batch_norm_14.bias, %aten::batch_norm_14.running_mean, %aten::batch_norm_14.running_var) /* span=aten::batch_norm_14:0:0 */;
+ %59 = %56.0 /* span=aten::batch_norm_13:0:0 */;
+ %60 = %58.0 /* span=aten::batch_norm_14:0:0 */;
+ %61 = add(%59, %60) /* span=aten::add__3:0:0 */;
+ %62 = nn.relu(%61) /* span=aten::relu__12:0:0 */;
+ %63 = nn.conv2d(%62, %aten::_convolution_15.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_15:0:0 */;
+ %64 = nn.batch_norm(%63, %aten::batch_norm_15.weight, %aten::batch_norm_15.bias, %aten::batch_norm_15.running_mean, %aten::batch_norm_15.running_var) /* span=aten::batch_norm_15:0:0 */;
+ %65 = %64.0 /* span=aten::batch_norm_15:0:0 */;
+ %66 = nn.relu(%65) /* span=aten::relu__13:0:0 */;
+ %67 = nn.conv2d(%66, %aten::_convolution_16.weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_16:0:0 */;
+ %68 = nn.batch_norm(%67, %aten::batch_norm_16.weight, %aten::batch_norm_16.bias, %aten::batch_norm_16.running_mean, %aten::batch_norm_16.running_var) /* span=aten::batch_norm_16:0:0 */;
+ %69 = %68.0 /* span=aten::batch_norm_16:0:0 */;
+ %70 = nn.relu(%69) /* span=aten::relu__14:0:0 */;
+ %71 = nn.conv2d(%70, %aten::_convolution_17.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_17:0:0 */;
+ %72 = nn.batch_norm(%71, %aten::batch_norm_17.weight, %aten::batch_norm_17.bias, %aten::batch_norm_17.running_mean, %aten::batch_norm_17.running_var) /* span=aten::batch_norm_17:0:0 */;
+ %73 = %72.0 /* span=aten::batch_norm_17:0:0 */;
+ %74 = add(%73, %62) /* span=aten::add__4:0:0 */;
+ %75 = nn.relu(%74) /* span=aten::relu__15:0:0 */;
+ %76 = nn.conv2d(%75, %aten::_convolution_18.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_18:0:0 */;
+ %77 = nn.batch_norm(%76, %aten::batch_norm_18.weight, %aten::batch_norm_18.bias, %aten::batch_norm_18.running_mean, %aten::batch_norm_18.running_var) /* span=aten::batch_norm_18:0:0 */;
+ %78 = %77.0 /* span=aten::batch_norm_18:0:0 */;
+ %79 = nn.relu(%78) /* span=aten::relu__16:0:0 */;
+ %80 = nn.conv2d(%79, %aten::_convolution_19.weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_19:0:0 */;
+ %81 = nn.batch_norm(%80, %aten::batch_norm_19.weight, %aten::batch_norm_19.bias, %aten::batch_norm_19.running_mean, %aten::batch_norm_19.running_var) /* span=aten::batch_norm_19:0:0 */;
+ %82 = %81.0 /* span=aten::batch_norm_19:0:0 */;
+ %83 = nn.relu(%82) /* span=aten::relu__17:0:0 */;
+ %84 = nn.conv2d(%83, %aten::_convolution_20.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_20:0:0 */;
+ %85 = nn.batch_norm(%84, %aten::batch_norm_20.weight, %aten::batch_norm_20.bias, %aten::batch_norm_20.running_mean, %aten::batch_norm_20.running_var) /* span=aten::batch_norm_20:0:0 */;
+ %86 = %85.0 /* span=aten::batch_norm_20:0:0 */;
+ %87 = add(%86, %75) /* span=aten::add__5:0:0 */;
+ %88 = nn.relu(%87) /* span=aten::relu__18:0:0 */;
+ %89 = nn.conv2d(%88, %aten::_convolution_21.weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* span=aten::_convolution_21:0:0 */;
+ %90 = nn.batch_norm(%89, %aten::batch_norm_21.weight, %aten::batch_norm_21.bias, %aten::batch_norm_21.running_mean, %aten::batch_norm_21.running_var) /* span=aten::batch_norm_21:0:0 */;
+ %91 = %90.0 /* span=aten::batch_norm_21:0:0 */;
+ %92 = nn.relu(%91) /* span=aten::relu__19:0:0 */;
+ %93 = nn.conv2d(%92, %aten::_convolution_22.weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* span=aten::_convolution_22:0:0 */;
+ %94 = nn.batch_norm(%93, %aten::batch_norm_22.weight, %aten::batch_norm_22.bias, %aten::batch_norm_22.running_mean, %aten::batch_norm_22.running_var) /* span=aten::batch_norm_22:0:0 */;
+ %95 = %94.0 /* span=aten::batch_norm_22:0:0 */;
+ %96 = nn.relu(%95) /* span=aten::relu__20:0:0 */;
+ %97 = nn.conv2d(%96, %aten::_convolution_23.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_23:0:0 */;
+ %98 = nn.batch_norm(%97, %aten::batch_norm_23.weight, %aten::batch_norm_23.bias, %aten::batch_norm_23.running_mean, %aten::batch_norm_23.running_var) /* span=aten::batch_norm_23:0:0 */;
+ %99 = %98.0 /* span=aten::batch_norm_23:0:0 */;
+ %100 = add(%99, %88) /* span=aten::add__6:0:0 */;
+ %101 = nn.relu(%100) /* span=aten::relu__21:0:0 */;
+ %102 = nn.conv2d(%101, %aten::_convolution_24.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_24:0:0 */;
+ %103 = nn.batch_norm(%102, %aten::batch_norm_24.weight, %aten::batch_norm_24.bias, %aten::batch_norm_24.running_mean, %aten::batch_norm_24.running_var) /* span=aten::batch_norm_24:0:0 */;
+ %104 = %103.0 /* span=aten::batch_norm_24:0:0 */;
+ %105 = nn.relu(%104) /* span=aten::relu__22:0:0 */;
+ %106 = nn.conv2d(%105, %aten::_convolution_25.weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_25:0:0 */;
+ %107 = nn.batch_norm(%106, %aten::batch_norm_25.weight, %aten::batch_norm_25.bias, %aten::batch_norm_25.running_mean, %aten::batch_norm_25.running_var) /* span=aten::batch_norm_25:0:0 */;
+ %108 = %107.0 /* span=aten::batch_norm_25:0:0 */;
+ %109 = nn.relu(%108) /* span=aten::relu__23:0:0 */;
+ %110 = nn.conv2d(%109, %aten::_convolution_26.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_26:0:0 */;
+ %111 = nn.batch_norm(%110, %aten::batch_norm_26.weight, %aten::batch_norm_26.bias, %aten::batch_norm_26.running_mean, %aten::batch_norm_26.running_var) /* span=aten::batch_norm_26:0:0 */;
+ %112 = nn.conv2d(%101, %aten::_convolution_27.weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_27:0:0 */;
+ %113 = nn.batch_norm(%112, %aten::batch_norm_27.weight, %aten::batch_norm_27.bias, %aten::batch_norm_27.running_mean, %aten::batch_norm_27.running_var) /* span=aten::batch_norm_27:0:0 */;
+ %114 = %111.0 /* span=aten::batch_norm_26:0:0 */;
+ %115 = %113.0 /* span=aten::batch_norm_27:0:0 */;
+ %116 = add(%114, %115) /* span=aten::add__7:0:0 */;
+ %117 = nn.relu(%116) /* span=aten::relu__24:0:0 */;
+ %118 = nn.conv2d(%117, %aten::_convolution_28.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_28:0:0 */;
+ %119 = nn.batch_norm(%118, %aten::batch_norm_28.weight, %aten::batch_norm_28.bias, %aten::batch_norm_28.running_mean, %aten::batch_norm_28.running_var) /* span=aten::batch_norm_28:0:0 */;
+ %120 = %119.0 /* span=aten::batch_norm_28:0:0 */;
+ %121 = nn.relu(%120) /* span=aten::relu__25:0:0 */;
+ %122 = nn.conv2d(%121, %aten::_convolution_29.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_29:0:0 */;
+ %123 = nn.batch_norm(%122, %aten::batch_norm_29.weight, %aten::batch_norm_29.bias, %aten::batch_norm_29.running_mean, %aten::batch_norm_29.running_var) /* span=aten::batch_norm_29:0:0 */;
+ %124 = %123.0 /* span=aten::batch_norm_29:0:0 */;
+ %125 = nn.relu(%124) /* span=aten::relu__26:0:0 */;
+ %126 = nn.conv2d(%125, %aten::_convolution_30.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_30:0:0 */;
+ %127 = nn.batch_norm(%126, %aten::batch_norm_30.weight, %aten::batch_norm_30.bias, %aten::batch_norm_30.running_mean, %aten::batch_norm_30.running_var) /* span=aten::batch_norm_30:0:0 */;
+ %128 = %127.0 /* span=aten::batch_norm_30:0:0 */;
+ %129 = add(%128, %117) /* span=aten::add__8:0:0 */;
+ %130 = nn.relu(%129) /* span=aten::relu__27:0:0 */;
+ %131 = nn.conv2d(%130, %aten::_convolution_31.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_31:0:0 */;
+ %132 = nn.batch_norm(%131, %aten::batch_norm_31.weight, %aten::batch_norm_31.bias, %aten::batch_norm_31.running_mean, %aten::batch_norm_31.running_var) /* span=aten::batch_norm_31:0:0 */;
+ %133 = %132.0 /* span=aten::batch_norm_31:0:0 */;
+ %134 = nn.relu(%133) /* span=aten::relu__28:0:0 */;
+ %135 = nn.conv2d(%134, %aten::_convolution_32.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_32:0:0 */;
+ %136 = nn.batch_norm(%135, %aten::batch_norm_32.weight, %aten::batch_norm_32.bias, %aten::batch_norm_32.running_mean, %aten::batch_norm_32.running_var) /* span=aten::batch_norm_32:0:0 */;
+ %137 = %136.0 /* span=aten::batch_norm_32:0:0 */;
+ %138 = nn.relu(%137) /* span=aten::relu__29:0:0 */;
+ %139 = nn.conv2d(%138, %aten::_convolution_33.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_33:0:0 */;
+ %140 = nn.batch_norm(%139, %aten::batch_norm_33.weight, %aten::batch_norm_33.bias, %aten::batch_norm_33.running_mean, %aten::batch_norm_33.running_var) /* span=aten::batch_norm_33:0:0 */;
+ %141 = %140.0 /* span=aten::batch_norm_33:0:0 */;
+ %142 = add(%141, %130) /* span=aten::add__9:0:0 */;
+ %143 = nn.relu(%142) /* span=aten::relu__30:0:0 */;
+ %144 = nn.conv2d(%143, %aten::_convolution_34.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_34:0:0 */;
+ %145 = nn.batch_norm(%144, %aten::batch_norm_34.weight, %aten::batch_norm_34.bias, %aten::batch_norm_34.running_mean, %aten::batch_norm_34.running_var) /* span=aten::batch_norm_34:0:0 */;
+ %146 = %145.0 /* span=aten::batch_norm_34:0:0 */;
+ %147 = nn.relu(%146) /* span=aten::relu__31:0:0 */;
+ %148 = nn.conv2d(%147, %aten::_convolution_35.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_35:0:0 */;
+ %149 = nn.batch_norm(%148, %aten::batch_norm_35.weight, %aten::batch_norm_35.bias, %aten::batch_norm_35.running_mean, %aten::batch_norm_35.running_var) /* span=aten::batch_norm_35:0:0 */;
+ %150 = %149.0 /* span=aten::batch_norm_35:0:0 */;
+ %151 = nn.relu(%150) /* span=aten::relu__32:0:0 */;
+ %152 = nn.conv2d(%151, %aten::_convolution_36.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_36:0:0 */;
+ %153 = nn.batch_norm(%152, %aten::batch_norm_36.weight, %aten::batch_norm_36.bias, %aten::batch_norm_36.running_mean, %aten::batch_norm_36.running_var) /* span=aten::batch_norm_36:0:0 */;
+ %154 = %153.0 /* span=aten::batch_norm_36:0:0 */;
+ %155 = add(%154, %143) /* span=aten::add__10:0:0 */;
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+ %157 = nn.conv2d(%156, %aten::_convolution_37.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_37:0:0 */;
+ %158 = nn.batch_norm(%157, %aten::batch_norm_37.weight, %aten::batch_norm_37.bias, %aten::batch_norm_37.running_mean, %aten::batch_norm_37.running_var) /* span=aten::batch_norm_37:0:0 */;
+ %159 = %158.0 /* span=aten::batch_norm_37:0:0 */;
+ %160 = nn.relu(%159) /* span=aten::relu__34:0:0 */;
+ %161 = nn.conv2d(%160, %aten::_convolution_38.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_38:0:0 */;
+ %162 = nn.batch_norm(%161, %aten::batch_norm_38.weight, %aten::batch_norm_38.bias, %aten::batch_norm_38.running_mean, %aten::batch_norm_38.running_var) /* span=aten::batch_norm_38:0:0 */;
+ %163 = %162.0 /* span=aten::batch_norm_38:0:0 */;
+ %164 = nn.relu(%163) /* span=aten::relu__35:0:0 */;
+ %165 = nn.conv2d(%164, %aten::_convolution_39.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_39:0:0 */;
+ %166 = nn.batch_norm(%165, %aten::batch_norm_39.weight, %aten::batch_norm_39.bias, %aten::batch_norm_39.running_mean, %aten::batch_norm_39.running_var) /* span=aten::batch_norm_39:0:0 */;
+ %167 = %166.0 /* span=aten::batch_norm_39:0:0 */;
+ %168 = add(%167, %156) /* span=aten::add__11:0:0 */;
+ %169 = nn.relu(%168) /* span=aten::relu__36:0:0 */;
+ %170 = nn.conv2d(%169, %aten::_convolution_40.weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* span=aten::_convolution_40:0:0 */;
+ %171 = nn.batch_norm(%170, %aten::batch_norm_40.weight, %aten::batch_norm_40.bias, %aten::batch_norm_40.running_mean, %aten::batch_norm_40.running_var) /* span=aten::batch_norm_40:0:0 */;
+ %172 = %171.0 /* span=aten::batch_norm_40:0:0 */;
+ %173 = nn.relu(%172) /* span=aten::relu__37:0:0 */;
+ %174 = nn.conv2d(%173, %aten::_convolution_41.weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* span=aten::_convolution_41:0:0 */;
+ %175 = nn.batch_norm(%174, %aten::batch_norm_41.weight, %aten::batch_norm_41.bias, %aten::batch_norm_41.running_mean, %aten::batch_norm_41.running_var) /* span=aten::batch_norm_41:0:0 */;
+ %176 = %175.0 /* span=aten::batch_norm_41:0:0 */;
+ %177 = nn.relu(%176) /* span=aten::relu__38:0:0 */;
+ %178 = nn.conv2d(%177, %aten::_convolution_42.weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* span=aten::_convolution_42:0:0 */;
+ %179 = nn.batch_norm(%178, %aten::batch_norm_42.weight, %aten::batch_norm_42.bias, %aten::batch_norm_42.running_mean, %aten::batch_norm_42.running_var) /* span=aten::batch_norm_42:0:0 */;
+ %180 = %179.0 /* span=aten::batch_norm_42:0:0 */;
+ %181 = add(%180, %169) /* span=aten::add__12:0:0 */;
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+ %183 = nn.conv2d(%182, %aten::_convolution_43.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_43:0:0 */;
+ %184 = nn.batch_norm(%183, %aten::batch_norm_43.weight, %aten::batch_norm_43.bias, %aten::batch_norm_43.running_mean, %aten::batch_norm_43.running_var) /* span=aten::batch_norm_43:0:0 */;
+ %185 = %184.0 /* span=aten::batch_norm_43:0:0 */;
+ %186 = nn.relu(%185) /* span=aten::relu__40:0:0 */;
+ %187 = nn.conv2d(%186, %aten::_convolution_44.weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* span=aten::_convolution_44:0:0 */;
+ %188 = nn.batch_norm(%187, %aten::batch_norm_44.weight, %aten::batch_norm_44.bias, %aten::batch_norm_44.running_mean, %aten::batch_norm_44.running_var) /* span=aten::batch_norm_44:0:0 */;
+ %189 = %188.0 /* span=aten::batch_norm_44:0:0 */;
+ %190 = nn.relu(%189) /* span=aten::relu__41:0:0 */;
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+ %192 = nn.batch_norm(%191, %aten::batch_norm_45.weight, %aten::batch_norm_45.bias, %aten::batch_norm_45.running_mean, %aten::batch_norm_45.running_var) /* span=aten::batch_norm_45:0:0 */;
+ %193 = nn.conv2d(%182, %aten::_convolution_46.weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_46:0:0 */;
+ %194 = nn.batch_norm(%193, %aten::batch_norm_46.weight, %aten::batch_norm_46.bias, %aten::batch_norm_46.running_mean, %aten::batch_norm_46.running_var) /* span=aten::batch_norm_46:0:0 */;
+ %195 = %192.0 /* span=aten::batch_norm_45:0:0 */;
+ %196 = %194.0 /* span=aten::batch_norm_46:0:0 */;
+ %197 = add(%195, %196) /* span=aten::add__13:0:0 */;
+ %198 = nn.relu(%197) /* span=aten::relu__42:0:0 */;
+ %199 = nn.conv2d(%198, %aten::_convolution_47.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_47:0:0 */;
+ %200 = nn.batch_norm(%199, %aten::batch_norm_47.weight, %aten::batch_norm_47.bias, %aten::batch_norm_47.running_mean, %aten::batch_norm_47.running_var) /* span=aten::batch_norm_47:0:0 */;
+ %201 = %200.0 /* span=aten::batch_norm_47:0:0 */;
+ %202 = nn.relu(%201) /* span=aten::relu__43:0:0 */;
+ %203 = nn.conv2d(%202, %aten::_convolution_48.weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* span=aten::_convolution_48:0:0 */;
+ %204 = nn.batch_norm(%203, %aten::batch_norm_48.weight, %aten::batch_norm_48.bias, %aten::batch_norm_48.running_mean, %aten::batch_norm_48.running_var) /* span=aten::batch_norm_48:0:0 */;
+ %205 = %204.0 /* span=aten::batch_norm_48:0:0 */;
+ %206 = nn.relu(%205) /* span=aten::relu__44:0:0 */;
+ %207 = nn.conv2d(%206, %aten::_convolution_49.weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_49:0:0 */;
+ %208 = nn.batch_norm(%207, %aten::batch_norm_49.weight, %aten::batch_norm_49.bias, %aten::batch_norm_49.running_mean, %aten::batch_norm_49.running_var) /* span=aten::batch_norm_49:0:0 */;
+ %209 = %208.0 /* span=aten::batch_norm_49:0:0 */;
+ %210 = add(%209, %198) /* span=aten::add__14:0:0 */;
+ %211 = nn.relu(%210) /* span=aten::relu__45:0:0 */;
+ %212 = nn.conv2d(%211, %aten::_convolution_50.weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* span=aten::_convolution_50:0:0 */;
+ %213 = nn.batch_norm(%212, %aten::batch_norm_50.weight, %aten::batch_norm_50.bias, %aten::batch_norm_50.running_mean, %aten::batch_norm_50.running_var) /* span=aten::batch_norm_50:0:0 */;
+ %214 = %213.0 /* span=aten::batch_norm_50:0:0 */;
+ %215 = nn.relu(%214) /* span=aten::relu__46:0:0 */;
+ %216 = nn.conv2d(%215, %aten::_convolution_51.weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* span=aten::_convolution_51:0:0 */;
+ %217 = nn.batch_norm(%216, %aten::batch_norm_51.weight, %aten::batch_norm_51.bias, %aten::batch_norm_51.running_mean, %aten::batch_norm_51.running_var) /* span=aten::batch_norm_51:0:0 */;
+ %218 = %217.0 /* span=aten::batch_norm_51:0:0 */;
+ %219 = nn.relu(%218) /* span=aten::relu__47:0:0 */;
+ %220 = nn.conv2d(%219, %aten::_convolution_52.weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* span=aten::_convolution_52:0:0 */;
+ %221 = nn.batch_norm(%220, %aten::batch_norm_52.weight, %aten::batch_norm_52.bias, %aten::batch_norm_52.running_mean, %aten::batch_norm_52.running_var) /* span=aten::batch_norm_52:0:0 */;
+ %222 = %221.0 /* span=aten::batch_norm_52:0:0 */;
+ %223 = add(%222, %211) /* span=aten::add__15:0:0 */;
+ %224 = nn.relu(%223) /* span=aten::relu__48:0:0 */;
+ %225 = nn.adaptive_avg_pool2d(%224, output_size=[1, 1]) /* span=aten::adaptive_avg_pool2d_0:0:0 */;
+ %226 = reshape(%225, newshape=[0, -1, 1, 1]) /* span=aten::flatten_0:0:0 */;
+ %227 = squeeze(%226, axis=[2, 3]) /* span=aten::flatten_0:0:0 */;
+ %228 = nn.dense(%227, %aten::linear_0.weight, units=None) /* span=aten::linear_0:0:0 */;
+ nn.bias_add(%228, %aten::linear_0.bias, axis=-1) /* span=aten::linear_0:0:0 */
}
</pre></div>
</div>
@@ -868,7 +924,7 @@ Tuning required RPC setup and please refer to
<span class="c1"># Altrernatively, we can save the compilation output and save it as a TVMCPackage.</span>
<span class="c1"># This way avoids loading of compiled module without compiling again.</span>
<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">+</span> <span cl [...]
- <span class="n">pkg_path</span> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory.relpath" title="tvm.contrib.utils.TempDirectory.relpath" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-method"><span class="n">tmp_path</span><span class="o">.</span><span class="n">relpath</span></a><span class="p">(</span><span class="s2">"keras-resnet50.tar"</span><span class="p">)</span>
+ <span class="n">pkg_path</span> <span class="o">=</span> <span class="n">tmp_path</span><span class="o">.</span><span class="n">relpath</span><span class="p">(</span><span class="s2">"torch-resnet50.tar"</span><span class="p">)</span>
<span class="n">tvmc</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
<span class="n">tvmc_model</span><span class="p">,</span>
<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">,</span>
@@ -912,6 +968,7 @@ Output Names:
['output_0']
</pre></div>
</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.578 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-tvmc-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/f84e457773d8f292bfac905e2ed3307f/deploy_model_on_adreno_tvmc.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno_tvmc.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 7a41e785ee..44bf1117a7 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -702,7 +702,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.1731 16.1520 17.0675 15.6303 0.4532
+ 14.0161 13.8383 15.0339 13.6012 0.4710
</pre></div>
</div>
</div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index 5a14bd714c..82ffdc6e36 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -494,32 +494,15 @@ be unstable.</p>
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
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/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/generalized_rcnn.py:75: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
for img in images:
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/transform.py:122: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
@@ -648,7 +631,7 @@ torchvision rcnn models.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 51.992 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 27.234 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index d639142f49..65d7f9f080 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -535,9 +535,7 @@ training. Other models require a full post training calibration.</p>
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
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-100%|#########9| 13.5M/13.6M [00:00<00:00, 70.5MB/s]
-100%|##########| 13.6M/13.6M [00:00<00:00, 70.2MB/s]
+100%|##########| 13.6M/13.6M [00:00<00:00, 144MB/s]
</pre></div>
</div>
</div>
@@ -626,7 +624,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 89.3791 89.2861 92.3924 88.8225 0.4546
+ 85.9349 85.8634 90.1120 85.4800 0.5474
</pre></div>
</div>
<div class="admonition note">
@@ -665,7 +663,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
<div class="section" id="deploy-a-quantized-tflite-model">
<h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
<p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 38.016 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 30.059 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/fb8217c13f4351224c6cf3aacf1a87fc/deploy_prequantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized_tflite.html b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
index 9b00388d36..a93877a7b4 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -620,7 +620,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 111.5432 111.5283 112.3434 111.0003 0.2554
+ 106.3536 106.3171 107.2856 105.5390 0.2778
</pre></div>
</div>
<div class="admonition note">
diff --git a/docs/how_to/deploy_models/index.html b/docs/how_to/deploy_models/index.html
index 57d4e1a9fd..064f4ca5ef 100644
--- a/docs/how_to/deploy_models/index.html
+++ b/docs/how_to/deploy_models/index.html
@@ -416,7 +416,7 @@ backends.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This article is a step-by-step tutorial to deploy pretrained Pytorch ResNet-18 model on Adreno ..."><img alt="Deploy the Pretrained Model on Adreno™" src="../../_images/sphx_glr_deploy_model_on_adreno_thumb.png" />
<p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a></p>
<div class="sphx-glr-thumbnail-title">Deploy the Pretrained Model on Adreno™</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno™."><img alt="Deploy the Pretrained Model on Adreno™ with tvmc Interface" src="../../_images/sphx_glr_deploy_model_on_adreno_tvmc_thumb.png" />
+</div><div class="sphx-glr-thumbcontainer" tooltip="This article is a step-by-step tutorial to deploy pretrained PyTorch resnet50 model on Adreno™."><img alt="Deploy the Pretrained Model on Adreno™ with tvmc Interface" src="../../_images/sphx_glr_deploy_model_on_adreno_tvmc_thumb.png" />
<p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a></p>
<div class="sphx-glr-thumbnail-title">Deploy the Pretrained Model on Adreno™ with tvmc Interface</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example of using Relay to compile a keras model and deploy it on Android device."><img alt="Deploy the Pretrained Model on Android" src="../../_images/sphx_glr_deploy_model_on_android_thumb.png" />
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 990be635b1..b40e27d97a 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:40.191</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:00.368</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 86%" />
@@ -390,35 +390,35 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:51.992</p></td>
+<td><p>03:27.234</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></td>
-<td><p>01:38.016</p></td>
+<td><p>01:30.059</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:22.353</p></td>
+<td><p>01:18.100</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:56.185</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
+<td><p>01:01.578</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:55.541</p></td>
+<td><p>00:51.737</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:51.657</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
+<td><p>00:51.487</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:32.408</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
+<td><p>00:30.120</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:32.032</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
+<td><p>00:30.046</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index e02bca4476..2479221dfb 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:04.367</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:03.993</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -390,15 +390,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></td>
-<td><p>00:03.026</p></td>
+<td><p>00:02.772</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></td>
-<td><p>00:01.334</p></td>
+<td><p>00:01.214</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
-<td><p>00:00.008</p></td>
+<td><p>00:00.007</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 004776086f..6cf7e922e3 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -565,10 +565,10 @@ profile the execution time of each passes.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 25637us [25637us] (48.47%; 48.47%)
-FoldScaleAxis: 27253us [12us] (51.53%; 51.53%)
- FoldConstant: 27241us [1841us] (51.50%; 99.96%)
- InferType: 25400us [25400us] (48.02%; 93.24%)
+InferType: 24544us [24544us] (48.85%; 48.85%)
+FoldScaleAxis: 25702us [8us] (51.15%; 51.15%)
+ FoldConstant: 25693us [1770us] (51.14%; 99.97%)
+ InferType: 23923us [23923us] (47.61%; 93.11%)
</pre></div>
</div>
</div>
@@ -590,10 +590,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 25760us [25760us] (48.44%; 48.44%)
-FoldScaleAxis: 27416us [11us] (51.56%; 51.56%)
- FoldConstant: 27405us [1932us] (51.54%; 99.96%)
- InferType: 25474us [25474us] (47.90%; 92.95%)
+InferType: 23591us [23591us] (48.51%; 48.51%)
+FoldScaleAxis: 25036us [7us] (51.49%; 51.49%)
+ FoldConstant: 25028us [1655us] (51.47%; 99.97%)
+ InferType: 23374us [23374us] (48.07%; 93.39%)
</pre></div>
</div>
<p>Register empty list to clear existing instruments.</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index 60350a8485..6dac7bfcbc 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -615,7 +615,7 @@ latency of convolution.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Convolution: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span cl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.372608 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 33.674110 ms
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-optimize-operators-opt-conv-cuda-py">
diff --git a/docs/how_to/optimize_operators/opt_conv_tensorcore.html b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
index a675637fcc..56aba75723 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -897,7 +897,7 @@ be able to run on our build server</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms"</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">* [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.598698 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.220508 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/optimize_operators/opt_gemm.html b/docs/how_to/optimize_operators/opt_gemm.html
index 874e25a0c0..5741592610 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -512,8 +512,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
<span class="nb">print</span><span class="p">(</span><span class="s2">"Baseline: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018842
-Baseline: 3.443266
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013893
+Baseline: 3.261470
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -572,7 +572,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt1: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.312580
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.280094
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -629,7 +629,7 @@ vastly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt2: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.291567
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.264058
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -684,7 +684,7 @@ the access pattern for A matrix is more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt3: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.118675
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.110821
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -761,7 +761,7 @@ flattening.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt4: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.108288
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.105624
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -839,7 +839,7 @@ write to C when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt5: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.112028
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.095053
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -919,7 +919,7 @@ class Module:
<span class="nb">print</span><span class="p">(</span><span class="s2">"Opt6: </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.134272
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.113209
</pre></div>
</div>
<p>Here is the generated IR after parallelization.</p>
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index 7882684a98..9397f540d9 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.890</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:31.360</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -390,15 +390,15 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></td>
-<td><p>00:31.497</p></td>
+<td><p>00:28.306</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></td>
-<td><p>00:01.289</p></td>
+<td><p>00:01.104</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 124c5da9ce..05568d9248 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>03:52.732</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:36.241</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 85%" />
@@ -390,27 +390,27 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:41.438</p></td>
+<td><p>01:34.323</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:15.868</p></td>
+<td><p>01:11.210</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:21.246</p></td>
+<td><p>00:19.678</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:18.103</p></td>
+<td><p>00:16.645</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:15.973</p></td>
+<td><p>00:14.284</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.104</p></td>
+<td><p>00:00.100</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index d9dbf092fc..7113008c87 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -1052,7 +1052,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.344 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.341 ms
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index 9fd32e9e40..97690f5d99 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -942,7 +942,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3.1938 3.1937 3.1940 3.1936 0.0002
+ 3.2027 3.2026 3.2036 3.2021 0.0006
</pre></div>
</div>
</div>
@@ -964,7 +964,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 15.868 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 11.210 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index b53e7a020f..48ec38b557 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -964,7 +964,7 @@ so we can read the log file and load the best schedules.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 764.3626 765.3355 766.1064 761.6459 1.9466
+ 704.1417 703.2458 707.1693 702.0101 2.1994
</pre></div>
</div>
</div>
@@ -986,7 +986,7 @@ to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
</ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 41.438 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 34.323 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index 807a9b2d27..9e9938e6e6 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:24.247</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:22.616</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -390,11 +390,11 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:24.210</p></td>
+<td><p>00:22.575</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.020</p></td>
+<td><p>00:00.024</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
@@ -402,11 +402,11 @@
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
-<td><p>00:00.006</p></td>
+<td><p>00:00.005</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index 1a74ebf1b5..6e5665571c 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -650,7 +650,7 @@ and measure running time.</p>
Best config:
,None
-Time cost of this operator: 0.037180
+Time cost of this operator: 0.037298
</pre></div>
</div>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/tutorials/cross_compilation_and_rpc.html b/docs/how_to/tutorials/cross_compilation_and_rpc.html
index bb459bdd78..2a5aaf1fcf 100644
--- a/docs/how_to/tutorials/cross_compilation_and_rpc.html
+++ b/docs/how_to/tutorials/cross_compilation_and_rpc.html
@@ -561,7 +561,7 @@ device and returns the measured cost. Network overhead is excluded.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%g</span><span class="s2"> secs/op"</span> <span class="o">%</span> <span class="n">cost</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.209e-07 secs/op
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.119e-07 secs/op
</pre></div>
</div>
</div>
diff --git a/docs/how_to/tutorials/optimize_llm.html b/docs/how_to/tutorials/optimize_llm.html
index f8f6cfc136..79086f2b19 100644
--- a/docs/how_to/tutorials/optimize_llm.html
+++ b/docs/how_to/tutorials/optimize_llm.html
@@ -985,7 +985,7 @@ IRModule to generate the token.</p>
<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">output_tokens</span><span class="p">))</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 18.592 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 9.080 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tutorials-optimize-llm-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/fb98884d1e98b239c77e2b86f43d4ac2/optimize_llm.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">optimize_llm.py</span></code></a></p>
diff --git a/docs/how_to/tutorials/sg_execution_times.html b/docs/how_to/tutorials/sg_execution_times.html
index 61bc5a1194..02605e5eaa 100644
--- a/docs/how_to/tutorials/sg_execution_times.html
+++ b/docs/how_to/tutorials/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>01:22.958</strong> total execution time for <strong>how_to_tutorials</strong> files:</p>
+<p><strong>01:13.212</strong> total execution time for <strong>how_to_tutorials</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 84%" />
@@ -390,19 +390,19 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="optimize_llm.html#sphx-glr-how-to-tutorials-optimize-llm-py"><span class="std std-ref">Optimize Large Language Model</span></a> (<code class="docutils literal notranslate"><span class="pre">optimize_llm.py</span></code>)</p></td>
-<td><p>01:18.592</p></td>
+<td><p>01:09.080</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="e2e_opt_model.html#sphx-glr-how-to-tutorials-e2e-opt-model-py"><span class="std std-ref">End-to-End Optimize Model</span></a> (<code class="docutils literal notranslate"><span class="pre">e2e_opt_model.py</span></code>)</p></td>
-<td><p>00:02.682</p></td>
+<td><p>00:02.520</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="customize_opt.html#sphx-glr-how-to-tutorials-customize-opt-py"><span class="std std-ref">Customize Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">customize_opt.py</span></code>)</p></td>
-<td><p>00:00.865</p></td>
+<td><p>00:00.821</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-how-to-tutorials-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></td>
-<td><p>00:00.819</p></td>
+<td><p>00:00.791</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 7f72464691..6b33024621 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -684,10 +684,10 @@ the tuned operator.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 304.0 98.734 (1, 2, 10, 10, 3) 2 1 [304.0]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.925 0.95 (1, 6, 10, 10) 1 1 [2.925]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.972 0.316 (1, 1, 10, 10, 3) 1 1 [0.972]
-Total_time - 307.896 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 296.2 98.745 (1, 2, 10, 10, 3) 2 1 [296.2]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 2.818 0.94 (1, 6, 10, 10) 1 1 [2.818]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.947 0.316 (1, 1, 10, 10, 3) 1 1 [0.947]
+Total_time - 299.965 - - - - -
</pre></div>
</div>
</div>
@@ -739,13 +739,13 @@ Total_time -
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 138.1 98.111 (1, 6, 10, 10, 1) 2 1 [138.1]
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.795 1.275 (1, 6, 10, 10) 1 1 [1.795]
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.864 0.614 (1, 3, 10, 10, 1) 1 1 [0.864]
-Total_time - 140.76 - - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 100.0 97.408 (1, 6, 10, 10, 1) 2 1 [100.0]
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.72 1.675 (1, 6, 10, 10) 1 1 [1.72]
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.942 0.917 (1, 1, 10, 10, 3) 1 1 [0.942]
+Total_time - 102.661 - - - - -
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 29.606 seconds)</p>
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<p><a class="reference download internal" download="" href="../../_downloads/9ccca8fd489a1486ac71b55a55c320c5/micro_autotune.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_autotune.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_pytorch.html b/docs/how_to/work_with_microtvm/micro_pytorch.html
index b6ebff58d8..2e6167699e 100644
--- a/docs/how_to/work_with_microtvm/micro_pytorch.html
+++ b/docs/how_to/work_with_microtvm/micro_pytorch.html
@@ -495,7 +495,7 @@ download a cat image and preprocess it to use as the model input.</p>
Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth
0%| | 0.00/3.42M [00:00<?, ?B/s]
-100%|##########| 3.42M/3.42M [00:00<00:00, 95.1MB/s]
+100%|##########| 3.42M/3.42M [00:00<00:00, 86.9MB/s]
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/nn/quantized/modules/__init__.py:98: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
return torch.quantize_per_tensor(X, float(self.scale),
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/nn/quantized/modules/__init__.py:99: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
@@ -621,7 +621,7 @@ via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as
Torch top-1 id: 282, class name: tiger cat
</pre></div>
</div>
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<p><a class="reference download internal" download="" href="../../_downloads/12b9ecc04c41abaa12022061771821d1/micro_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">micro_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 9b731b4e80..82526503d3 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -564,7 +564,7 @@ take about <strong>2 minutes</strong> to download the Stanford Cars, while COCO
<a href="https://docs.python.org/3/library/shutil.html#shutil.move" title="shutil.move" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">move</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-typ [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'/tmp/tmp3bobr3n4/images/random'
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</pre></div>
</div>
</div>
@@ -624,8 +624,8 @@ objects to other stuff? We can display some examples from our datasets using <co
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmp3bobr3n4/images/target contains 8144 images
-/tmp/tmp3bobr3n4/images/random contains 5000 images
+<img src="../../_images/sphx_glr_micro_train_001.png" srcset="../../_images/sphx_glr_micro_train_001.png" alt="[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpp8iwu5_u/images/target contains 8144 images
+/tmp/tmpp8iwu5_u/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -737,13 +737,13 @@ the time on our validation set).</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 47s - loss: 0.2280 - accuracy: 0.9205 - val_loss: 0.1398 - val_accuracy: 0.9479 - 47s/epoch - 144ms/step
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Epoch 2/3
-328/328 - 44s - loss: 0.1054 - accuracy: 0.9614 - val_loss: 0.1226 - val_accuracy: 0.9551 - 44s/epoch - 135ms/step
+328/328 - 40s - loss: 0.0961 - accuracy: 0.9620 - val_loss: 0.1508 - val_accuracy: 0.9437 - 40s/epoch - 121ms/step
Epoch 3/3
-328/328 - 44s - loss: 0.0717 - accuracy: 0.9740 - val_loss: 0.0981 - val_accuracy: 0.9641 - 44s/epoch - 134ms/step
+328/328 - 39s - loss: 0.0626 - accuracy: 0.9758 - val_loss: 0.0933 - val_accuracy: 0.9675 - 39s/epoch - 120ms/step
-<keras.callbacks.History object at 0x7f465f88d490>
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</pre></div>
</div>
</div>
@@ -1007,7 +1007,7 @@ as intended.</p>
<p>From here, we could modify the model to read live images from the camera - we have another
Arduino tutorial for how to do that <a class="reference external" href="https://github.com/guberti/tvm-arduino-demos/tree/master/examples/person_detection">on GitHub</a>. Alternatively, we could also
<a class="reference external" href="https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_autotune.html">use TVM’s autotuning capabilities</a> to dramatically improve the model’s performance.</p>
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+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
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index e6da867361..444191dec6 100644
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+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
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@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
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@@ -1668,7 +1668,7 @@ history states as starting point to perform Evolutionary Search).</p></li>
<dl class="py class">
<dt class="sig sig-object py" id="tvm.auto_scheduler.SketchPolicy">
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+<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
<dd><p>The search policy that searches in a hierarchical search space defined by sketches.
The policy randomly samples programs from the space defined by sketches and use evolutionary
search to fine-tune them.</p>
@@ -1952,7 +1952,7 @@ Candidates:
<dl class="py function">
<dt class="sig sig-object py" id="tvm.auto_scheduler.auto_schedule">
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<dd><p>THIS API IS DEPRECATED.</p>
<p>Run auto scheduling search for a task.</p>
<dl class="field-list simple">
diff --git a/docs/reference/api/typedoc/classes/ArtifactCache.html b/docs/reference/api/typedoc/classes/ArtifactCache.html
index 8e10d2cfdd..d34513e09e 100644
--- a/docs/reference/api/typedoc/classes/ArtifactCache.html
+++ b/docs/reference/api/typedoc/classes/ArtifactCache.html
@@ -27,7 +27,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/ArtifactCacheTemplate.html" class="tsd-signature-type tsd-kind-interface">ArtifactCacheTemplate</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L90">artifact_cache.ts:90</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L90">artifact_cache.ts:90</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -59,7 +59,7 @@
<h5><span class="tsd-kind-parameter">scope</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="ArtifactCache.html" class="tsd-signature-type tsd-kind-class">ArtifactCache</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L94">artifact_cache.ts:94</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L94">artifact_cache.ts:94</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="addToCache" class="tsd-anchor"></a>
@@ -83,7 +83,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#addToCache">addToCache</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L132">artifact_cache.ts:132</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L132">artifact_cache.ts:132</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="deleteInCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>delete<wbr/>In<wbr/>Cache</span><a href="#deleteInCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -101,7 +101,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#deleteInCache">deleteInCache</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L162">artifact_cache.ts:162</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L162">artifact_cache.ts:162</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="fetchWithCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>fetch<wbr/>With<wbr/>Cache</span><a href="#fetchWithCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -128,7 +128,7 @@
<aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#fetchWithCache">fetchWithCache</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L121">artifact_cache.ts:121</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L121">artifact_cache.ts:121</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="hasAllKeys" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>has<wbr/>All<wbr/>Keys</span><a href="#hasAllKeys" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -147,7 +147,7 @@
<aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#hasAllKeys">hasAllKeys</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L148">artifact_cache.ts:148</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L148">artifact_cache.ts:148</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="responseTostoretype" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>response<wbr/>Tostoretype</span><a href="#responseTostoretype" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -164,7 +164,7 @@
<h5><code class="tsd-tag ts-flagOptional">Optional</code> <span class="tsd-kind-parameter">storetype</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L101">artifact_cache.ts:101</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L101">artifact_cache.ts:101</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/ArtifactIndexedDBCache.html b/docs/reference/api/typedoc/classes/ArtifactIndexedDBCache.html
index 5e20a39e0c..6aeb80b84b 100644
--- a/docs/reference/api/typedoc/classes/ArtifactIndexedDBCache.html
+++ b/docs/reference/api/typedoc/classes/ArtifactIndexedDBCache.html
@@ -27,7 +27,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/ArtifactCacheTemplate.html" class="tsd-signature-type tsd-kind-interface">ArtifactCacheTemplate</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L173">artifact_cache.ts:173</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L173">artifact_cache.ts:173</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -60,7 +60,7 @@
<h5><span class="tsd-kind-parameter">dbName</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="ArtifactIndexedDBCache.html" class="tsd-signature-type tsd-kind-class">ArtifactIndexedDBCache</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L178">artifact_cache.ts:178</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L178">artifact_cache.ts:178</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="addToCache" class="tsd-anchor"></a>
@@ -84,7 +84,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#addToCache">addToCache</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L288">artifact_cache.ts:288</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L288">artifact_cache.ts:288</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="addToIndexedDB" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>add<wbr/>To<wbr/>IndexedDB</span><a href="#addToIndexedDB" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -101,7 +101,7 @@
<h5><code class="tsd-tag ts-flagOptional">Optional</code> <span class="tsd-kind-parameter">storetype</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L263">artifact_cache.ts:263</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L263">artifact_cache.ts:263</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="asyncGetHelper" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>async<wbr/>Get<wbr/>Helper</span><a href="#asyncGetHelper" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -114,7 +114,7 @@
<h5><span class="tsd-kind-parameter">url</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L230">artifact_cache.ts:230</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L230">artifact_cache.ts:230</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="deleteInCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>delete<wbr/>In<wbr/>Cache</span><a href="#deleteInCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -132,7 +132,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#deleteInCache">deleteInCache</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L339">artifact_cache.ts:339</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L339">artifact_cache.ts:339</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="fetchWithCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>fetch<wbr/>With<wbr/>Cache</span><a href="#fetchWithCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -156,7 +156,7 @@ cache, fetch the data and then add to cache.</p>
<aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#fetchWithCache">fetchWithCache</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L247">artifact_cache.ts:247</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L247">artifact_cache.ts:247</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="hasAllKeys" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>has<wbr/>All<wbr/>Keys</span><a href="#hasAllKeys" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -174,7 +174,7 @@ cache, fetch the data and then add to cache.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/ArtifactCacheTemplate.html">ArtifactCacheTemplate</a>.<a href="../interfaces/ArtifactCacheTemplate.html#hasAllKeys">hasAllKeys</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L307">artifact_cache.ts:307</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L307">artifact_cache.ts:307</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/DLDataType.html b/docs/reference/api/typedoc/classes/DLDataType.html
index ee90930723..db86a62b3a 100644
--- a/docs/reference/api/typedoc/classes/DLDataType.html
+++ b/docs/reference/api/typedoc/classes/DLDataType.html
@@ -23,7 +23,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">DLDataType</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L425">runtime.ts:425</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L425">runtime.ts:425</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -62,7 +62,7 @@
<h5><span class="tsd-kind-parameter">lanes</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="DLDataType.html" class="tsd-signature-type tsd-kind-class">DLDataType</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L433">runtime.ts:433</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L433">runtime.ts:433</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="bits" class="tsd-anchor"></a>
@@ -71,21 +71,21 @@
<div class="tsd-comment tsd-typography"><p>Number of bits in the data type.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L429">runtime.ts:429</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L429">runtime.ts:429</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="code" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>code</span><a href="#code" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">code</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<div class="tsd-comment tsd-typography"><p>The type code</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L427">runtime.ts:427</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L427">runtime.ts:427</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="lanes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>lanes</span><a href="#lanes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">lanes</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<div class="tsd-comment tsd-typography"><p>Number of vector lanes.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L431">runtime.ts:431</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L431">runtime.ts:431</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="numStorageBytes" class="tsd-anchor"></a>
@@ -95,7 +95,7 @@
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L448">runtime.ts:448</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L448">runtime.ts:448</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toString" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>to<wbr/>String</span><a href="#toString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -103,7 +103,7 @@
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L439">runtime.ts:439</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L439">runtime.ts:439</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/DLDevice.html b/docs/reference/api/typedoc/classes/DLDevice.html
index 58801c78bb..b1044c7d2b 100644
--- a/docs/reference/api/typedoc/classes/DLDevice.html
+++ b/docs/reference/api/typedoc/classes/DLDevice.html
@@ -23,7 +23,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">DLDevice</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L365">runtime.ts:365</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L365">runtime.ts:365</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -61,7 +61,7 @@
<h5><span class="tsd-kind-parameter">lib</span>: <a href="_internal_.FFILibrary.html" class="tsd-signature-type tsd-kind-class">FFILibrary</a></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="DLDevice.html" class="tsd-signature-type tsd-kind-class">DLDevice</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L373">runtime.ts:373</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L373">runtime.ts:373</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="deviceId" class="tsd-anchor"></a>
@@ -70,14 +70,14 @@
<div class="tsd-comment tsd-typography"><p>The device index.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L369">runtime.ts:369</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L369">runtime.ts:369</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="deviceType" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>device<wbr/>Type</span><a href="#deviceType" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">device<wbr/>Type</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<div class="tsd-comment tsd-typography"><p>The device type code of the device.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L367">runtime.ts:367</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L367">runtime.ts:367</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="sync" class="tsd-anchor"></a>
@@ -89,7 +89,7 @@
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L392">runtime.ts:392</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L392">runtime.ts:392</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toString" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>to<wbr/>String</span><a href="#toString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -97,7 +97,7 @@
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L399">runtime.ts:399</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L399">runtime.ts:399</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/Instance.html b/docs/reference/api/typedoc/classes/Instance.html
index 5e016fde10..ae7f9ef376 100644
--- a/docs/reference/api/typedoc/classes/Instance.html
+++ b/docs/reference/api/typedoc/classes/Instance.html
@@ -40,7 +40,7 @@ are not tracked through JS native garbage collection mechanism.</p>
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1027">runtime.ts:1027</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1027">runtime.ts:1027</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -146,24 +146,24 @@ a WASI object, or an object containing wasmLibraryProvider field.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="Instance.html" class="tsd-signature-type tsd-kind-class">Instance</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1059">runtime.ts:1059</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1059">runtime.ts:1059</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="cacheMetadata" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>cache<wbr/>Metadata</span><a href="#cacheMetadata" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">cache<wbr/>Metadata</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type ">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">></span><span class="tsd-signature-symbol"> = {}</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1030">runtime.ts:1030</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1030">runtime.ts:1030</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="exports" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>exports</span><a href="#exports" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">exports</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type ">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type ">Function</span><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1029">runtime.ts:1029</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1029">runtime.ts:1029</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="memory" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>memory</span><a href="#memory" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">memory</span><span class="tsd-signature-symbol">:</span> <a href="_internal_.Memory.html" class="tsd-signature-type tsd-kind-class">Memory</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1028">runtime.ts:1028</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1028">runtime.ts:1028</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="applyPresenceAndFrequencyPenalty" class="tsd-anchor"></a>
@@ -199,7 +199,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">any</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1833">runtime.ts:1833</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1833">runtime.ts:1833</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="applyRepetitionPenalty" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>apply<wbr/>Repetition<wbr/>Penalty</span><a href="#applyRepetitionPenalty" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -224,7 +224,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">any</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1820">runtime.ts:1820</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1820">runtime.ts:1820</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="applySoftmaxWithTemperature" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>apply<wbr/>Softmax<wbr/>With<wbr/>Temperature</span><a href="#applySoftmaxWithTemperature" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -245,7 +245,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">any</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1850">runtime.ts:1850</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1850">runtime.ts:1850</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="asyncLoadWebGPUPipelines" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>async<wbr/>Load<wbr/>WebGPUPipelines</span><a href="#asyncLoadWebGPUPipelines" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -262,7 +262,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L2087">runtime.ts:2087</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2087">runtime.ts:2087</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="asyncifyEnabled" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>asyncify<wbr/>Enabled</span><a href="#asyncifyEnabled" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -273,7 +273,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">boolean</span></h4><p>The asynctify mode toggle</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1127">runtime.ts:1127</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1127">runtime.ts:1127</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="attachToCurrentScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>attach<wbr/>To<wbr/>Current<wbr/>Scope</span><a href="#attachToCurrentScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -300,7 +300,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1195">runtime.ts:1195</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1195">runtime.ts:1195</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="beginScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>begin<wbr/>Scope</span><a href="#beginScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -310,7 +310,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1154">runtime.ts:1154</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1154">runtime.ts:1154</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="benchmark" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>benchmark</span><a href="#benchmark" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -345,7 +345,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
<h5><span class="tsd-kind-parameter">repeat</span>: <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> = 1</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1102">runtime.ts:1102</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1102">runtime.ts:1102</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="bindCanvas" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>bind<wbr/>Canvas</span><a href="#bindCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -362,7 +362,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1858">runtime.ts:1858</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1858">runtime.ts:1858</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="clearCanvas" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>clear<wbr/>Canvas</span><a href="#clearCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -372,7 +372,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1889">runtime.ts:1889</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1889">runtime.ts:1889</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="concatEmbeddings" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>concat<wbr/>Embeddings</span><a href="#concatEmbeddings" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -388,7 +388,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
<h4 class="tsd-returns-title">Returns <a href="NDArray.html" class="tsd-signature-type tsd-kind-class">NDArray</a></h4><p>An NDArray of shape (\sum_{i} {m}, hidden_size)</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1926">runtime.ts:1926</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1926">runtime.ts:1926</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="cpu" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>cpu</span><a href="#cpu" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -405,7 +405,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="DLDevice.html" class="tsd-signature-type tsd-kind-class">DLDevice</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1700">runtime.ts:1700</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1700">runtime.ts:1700</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="createVirtualMachine" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>create<wbr/>Virtual<wbr/>Machine</span><a href="#createVirtualMachine" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -423,7 +423,7 @@ token_freqs[i] is the frequency of token_ids[i], for all i. And all token_freqs
<h4 class="tsd-returns-title">Returns <a href="VirtualMachine.html" class="tsd-signature-type tsd-kind-class">VirtualMachine</a></h4><p>The created virtual machime.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1373">runtime.ts:1373</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1373">runtime.ts:1373</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="detachFromCurrentScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>detach<wbr/>From<wbr/>Current<wbr/>Scope</span><a href="#detachFromCurrentScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -449,7 +449,7 @@ so it won't be released via auto-release during endscope.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1222">runtime.ts:1222</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1222">runtime.ts:1222</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="device" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>device</span><a href="#device" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -471,7 +471,7 @@ so it won't be released via auto-release during endscope.</p>
<h4 class="tsd-returns-title">Returns <a href="DLDevice.html" class="tsd-signature-type tsd-kind-class">DLDevice</a></h4><p>The created device.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1692">runtime.ts:1692</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1692">runtime.ts:1692</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -484,7 +484,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1131">runtime.ts:1131</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1131">runtime.ts:1131</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="empty" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>empty</span><a href="#empty" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -510,7 +510,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <a href="NDArray.html" class="tsd-signature-type tsd-kind-class">NDArray</a></h4><p>The created ndarray.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1720">runtime.ts:1720</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1720">runtime.ts:1720</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="endScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>end<wbr/>Scope</span><a href="#endScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -523,7 +523,7 @@ a value to parent scope.</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1165">runtime.ts:1165</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1165">runtime.ts:1165</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="fetchNDArrayCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>fetchNDArray<wbr/>Cache</span><a href="#fetchNDArrayCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -557,7 +557,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">></span></h4><p>The meta data</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1462">runtime.ts:1462</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1462">runtime.ts:1462</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getGlobalFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Global<wbr/>Func</span><a href="#getGlobalFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -575,7 +575,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></h4><p>The result function.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1310">runtime.ts:1310</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1310">runtime.ts:1310</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getParamsFromCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Params<wbr/>From<wbr/>Cache</span><a href="#getParamsFromCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -596,7 +596,7 @@ a value to parent scope.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="TVMObject.html" class="tsd-signature-type tsd-kind-class">TVMObject</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1401">runtime.ts:1401</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1401">runtime.ts:1401</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getParamsFromCacheByName" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Params<wbr/>From<wbr/>Cache<wbr/>By<wbr/>Name</span><a href="#getParamsFromCacheByName" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -614,7 +614,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <a href="TVMObject.html" class="tsd-signature-type tsd-kind-class">TVMObject</a></h4><p>Parameters read.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1412">runtime.ts:1412</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1412">runtime.ts:1412</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="initWebGPU" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>init<wbr/>WebGPU</span><a href="#initWebGPU" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -631,7 +631,7 @@ a value to parent scope.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L2150">runtime.ts:2150</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2150">runtime.ts:2150</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="isPackedFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>is<wbr/>Packed<wbr/>Func</span><a href="#isPackedFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -649,7 +649,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">boolean</span></h4><p>The check result.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1345">runtime.ts:1345</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1345">runtime.ts:1345</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="listGlobalFuncNames" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>list<wbr/>Global<wbr/>Func<wbr/>Names</span><a href="#listGlobalFuncNames" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -660,7 +660,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h4><p>The name list.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1238">runtime.ts:1238</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1238">runtime.ts:1238</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="makeShapeTuple" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>make<wbr/>Shape<wbr/>Tuple</span><a href="#makeShapeTuple" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -678,7 +678,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <a href="TVMObject.html" class="tsd-signature-type tsd-kind-class">TVMObject</a></h4><p>The created shape tuple.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1960">runtime.ts:1960</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1960">runtime.ts:1960</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="makeString" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>make<wbr/>String</span><a href="#makeString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -696,7 +696,7 @@ a value to parent scope.</p>
<h4 class="tsd-returns-title">Returns <a href="_internal_.TVMString.html" class="tsd-signature-type tsd-kind-class">TVMString</a></h4><p>The result TVMString.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1951">runtime.ts:1951</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1951">runtime.ts:1951</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="makeTVMArray" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>makeTVMArray</span><a href="#makeTVMArray" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -716,7 +716,7 @@ and needs to b explicitly disposed.</p>
<h4 class="tsd-returns-title">Returns <a href="TVMArray.html" class="tsd-signature-type tsd-kind-class">TVMArray</a></h4><p>The result array.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1902">runtime.ts:1902</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1902">runtime.ts:1902</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="moveToParentScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>move<wbr/>To<wbr/>Parent<wbr/>Scope</span><a href="#moveToParentScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -741,7 +741,7 @@ alive when exit the current scope.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><p>The input obj.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1208">runtime.ts:1208</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1208">runtime.ts:1208</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="ndarrayCacheClear" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ndarray<wbr/>Cache<wbr/>Clear</span><a href="#ndarrayCacheClear" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -751,7 +751,7 @@ alive when exit the current scope.</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1448">runtime.ts:1448</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1448">runtime.ts:1448</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="ndarrayCacheGet" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ndarray<wbr/>Cache<wbr/>Get</span><a href="#ndarrayCacheGet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -769,7 +769,7 @@ alive when exit the current scope.</p>
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1421">runtime.ts:1421</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1421">runtime.ts:1421</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="ndarrayCacheRemove" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ndarray<wbr/>Cache<wbr/>Remove</span><a href="#ndarrayCacheRemove" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -787,7 +787,7 @@ alive when exit the current scope.</p>
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1430">runtime.ts:1430</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1430">runtime.ts:1430</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="ndarrayCacheUpdate" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ndarray<wbr/>Cache<wbr/>Update</span><a href="#ndarrayCacheUpdate" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -810,7 +810,7 @@ alive when exit the current scope.</p>
<h5><span class="tsd-kind-parameter">override</span>: <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = false</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1439">runtime.ts:1439</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1439">runtime.ts:1439</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="registerAsyncServerFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>register<wbr/>Async<wbr/>Server<wbr/>Func</span><a href="#registerAsyncServerFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -838,7 +838,7 @@ These functions contains explicit continuation</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L2060">runtime.ts:2060</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2060">runtime.ts:2060</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="registerAsyncifyFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>register<wbr/>Asyncify<wbr/>Func</span><a href="#registerAsyncifyFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -877,7 +877,7 @@ The wasm needs to be compiled with Asynctify</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L2041">runtime.ts:2041</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2041">runtime.ts:2041</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="registerFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>register<wbr/>Func</span><a href="#registerFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -900,7 +900,7 @@ The wasm needs to be compiled with Asynctify</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1277">runtime.ts:1277</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1277">runtime.ts:1277</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="registerInitProgressCallback" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>register<wbr/>Init<wbr/>Progress<wbr/>Callback</span><a href="#registerInitProgressCallback" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -917,7 +917,7 @@ The wasm needs to be compiled with Asynctify</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1390">runtime.ts:1390</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1390">runtime.ts:1390</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="registerObjectConstructor" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>register<wbr/>Object<wbr/>Constructor</span><a href="#registerObjectConstructor" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -942,7 +942,7 @@ The wasm needs to be compiled with Asynctify</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1997">runtime.ts:1997</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1997">runtime.ts:1997</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="runtimeStatsText" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>runtime<wbr/>Stats<wbr/>Text</span><a href="#runtimeStatsText" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -952,7 +952,7 @@ The wasm needs to be compiled with Asynctify</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1143">runtime.ts:1143</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1143">runtime.ts:1143</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="sampleTopPFromLogits" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sample<wbr/>TopPFrom<wbr/>Logits</span><a href="#sampleTopPFromLogits" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -978,7 +978,7 @@ The wasm needs to be compiled with Asynctify</p>
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1799">runtime.ts:1799</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1799">runtime.ts:1799</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="sampleTopPFromProb" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sample<wbr/>TopPFrom<wbr/>Prob</span><a href="#sampleTopPFromProb" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1000,7 +1000,7 @@ The wasm needs to be compiled with Asynctify</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The sampled index.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1810">runtime.ts:1810</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1810">runtime.ts:1810</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="scalar" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>scalar</span><a href="#scalar" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1022,7 +1022,7 @@ The wasm needs to be compiled with Asynctify</p>
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1682">runtime.ts:1682</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1682">runtime.ts:1682</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="setPackedArguments" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>set<wbr/>Packed<wbr/>Arguments</span><a href="#setPackedArguments" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1052,7 +1052,7 @@ Allocate new temporary space from the stack if necessary.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L2300">runtime.ts:2300</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2300">runtime.ts:2300</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="setSeed" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>set<wbr/>Seed</span><a href="#setSeed" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1067,7 +1067,7 @@ Allocate new temporary space from the stack if necessary.</p>
<h5><span class="tsd-kind-parameter">seed</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1787">runtime.ts:1787</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1787">runtime.ts:1787</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="showImage" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>show<wbr/>Image</span><a href="#showImage" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1084,7 +1084,7 @@ Allocate new temporary space from the stack if necessary.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1867">runtime.ts:1867</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1867">runtime.ts:1867</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="systemLib" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>system<wbr/>Lib</span><a href="#systemLib" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1096,7 +1096,7 @@ System lib is a global module that contains self register functions in startup.<
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1231">runtime.ts:1231</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1231">runtime.ts:1231</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toDLDataType" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>toDLData<wbr/>Type</span><a href="#toDLDataType" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1114,7 +1114,7 @@ System lib is a global module that contains self register functions in startup.<
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1636">runtime.ts:1636</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1636">runtime.ts:1636</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toPackedFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>to<wbr/>Packed<wbr/>Func</span><a href="#toPackedFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1132,7 +1132,7 @@ System lib is a global module that contains self register functions in startup.<
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1356">runtime.ts:1356</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1356">runtime.ts:1356</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="typeKey2Index" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Key2<wbr/>Index</span><a href="#typeKey2Index" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1150,7 +1150,7 @@ System lib is a global module that contains self register functions in startup.<
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The corresponding type index.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1969">runtime.ts:1969</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1969">runtime.ts:1969</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="uniform" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>uniform</span><a href="#uniform" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1180,7 +1180,7 @@ System lib is a global module that contains self register functions in startup.<
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1766">runtime.ts:1766</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1766">runtime.ts:1766</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="webgpu" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>webgpu</span><a href="#webgpu" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1197,7 +1197,7 @@ System lib is a global module that contains self register functions in startup.<
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="DLDevice.html" class="tsd-signature-type tsd-kind-class">DLDevice</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1708">runtime.ts:1708</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1708">runtime.ts:1708</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="withNewScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>with<wbr/>New<wbr/>Scope</span><a href="#withNewScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1230,7 +1230,7 @@ System lib is a global module that contains self register functions in startup.<
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><p>The result value.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1179">runtime.ts:1179</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1179">runtime.ts:1179</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="wrapAsyncifyPackedFunc" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>wrap<wbr/>Asyncify<wbr/>Packed<wbr/>Func</span><a href="#wrapAsyncifyPackedFunc" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -1253,7 +1253,7 @@ to make sure you use await when calling the funciton.</p>
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<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L2024">runtime.ts:2024</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2024">runtime.ts:2024</a></li></ul></aside></li></ul></section></section></div>
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diff --git a/docs/reference/api/typedoc/classes/LinearCongruentialGenerator.html b/docs/reference/api/typedoc/classes/LinearCongruentialGenerator.html
index 2ae673bd0b..647888e120 100644
--- a/docs/reference/api/typedoc/classes/LinearCongruentialGenerator.html
+++ b/docs/reference/api/typedoc/classes/LinearCongruentialGenerator.html
@@ -26,7 +26,7 @@ sepcification in <a href="https://en.cppreference.com/w/cpp/numeric/random/linea
<ul class="tsd-hierarchy">
<li><span class="target">LinearCongruentialGenerator</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L88">support.ts:88</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L88">support.ts:88</a></li></ul></aside>
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<section class="tsd-panel tsd-index-panel">
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@@ -59,24 +59,24 @@ sepcification in <a href="https://en.cppreference.com/w/cpp/numeric/random/linea
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<h4 class="tsd-returns-title">Returns <a href="LinearCongruentialGenerator.html" class="tsd-signature-type tsd-kind-class">LinearCongruentialGenerator</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L98">support.ts:98</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L98">support.ts:98</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="increment" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>increment</span><a href="#increment" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">increment</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L91">support.ts:91</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L91">support.ts:91</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="modulus" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>modulus</span><a href="#modulus" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">modulus</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L89">support.ts:89</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L89">support.ts:89</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="multiplier" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>multiplier</span><a href="#multiplier" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">multiplier</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L90">support.ts:90</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L90">support.ts:90</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="nextInt" class="tsd-anchor"></a>
@@ -89,7 +89,7 @@ sepcification in <a href="https://en.cppreference.com/w/cpp/numeric/random/linea
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L127">support.ts:127</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L127">support.ts:127</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="randomFloat" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>random<wbr/>Float</span><a href="#randomFloat" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -100,7 +100,7 @@ sepcification in <a href="https://en.cppreference.com/w/cpp/numeric/random/linea
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L141">support.ts:141</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L141">support.ts:141</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="setSeed" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>set<wbr/>Seed</span><a href="#setSeed" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -118,7 +118,7 @@ sepcification in <a href="https://en.cppreference.com/w/cpp/numeric/random/linea
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/support.ts#L111">support.ts:111</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L111">support.ts:111</a></li></ul></aside></li></ul></section></section></div>
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<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/Module.html b/docs/reference/api/typedoc/classes/Module.html
index 17991146fb..a818a792dc 100644
--- a/docs/reference/api/typedoc/classes/Module.html
+++ b/docs/reference/api/typedoc/classes/Module.html
@@ -27,7 +27,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L727">runtime.ts:727</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L727">runtime.ts:727</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -73,7 +73,7 @@
<h4 class="tsd-returns-title">Returns <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></h4></li></ul></li></ul></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="Module.html" class="tsd-signature-type tsd-kind-class">Module</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L732">runtime.ts:732</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L732">runtime.ts:732</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
@@ -88,7 +88,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L742">runtime.ts:742</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L742">runtime.ts:742</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getFunction" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Function</span><a href="#getFunction" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -110,7 +110,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></h4><p>The result function.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L770">runtime.ts:770</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L770">runtime.ts:770</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getHandle" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Handle</span><a href="#getHandle" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -128,7 +128,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The handle.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L757">runtime.ts:757</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L757">runtime.ts:757</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="importModule" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>import<wbr/>Module</span><a href="#importModule" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -145,7 +145,7 @@ only the first call will take effect.</p>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L804">runtime.ts:804</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L804">runtime.ts:804</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/NDArray.html b/docs/reference/api/typedoc/classes/NDArray.html
index 0e99edaefa..aad43eded2 100644
--- a/docs/reference/api/typedoc/classes/NDArray.html
+++ b/docs/reference/api/typedoc/classes/NDArray.html
@@ -27,7 +27,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L456">runtime.ts:456</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L456">runtime.ts:456</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
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<h4 class="tsd-returns-title">Returns <a href="NDArray.html" class="tsd-signature-type tsd-kind-class">NDArray</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L476">runtime.ts:476</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L476">runtime.ts:476</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="device" class="tsd-anchor"></a>
@@ -85,35 +85,35 @@
<div class="tsd-comment tsd-typography"><p>Device of the array.</p>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L466">runtime.ts:466</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L466">runtime.ts:466</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="dtype" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dtype</span><a href="#dtype" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">dtype</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
<div class="tsd-comment tsd-typography"><p>Data type of the array.</p>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L462">runtime.ts:462</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L462">runtime.ts:462</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="isView" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>is<wbr/>View</span><a href="#isView" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">is<wbr/>View</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
<div class="tsd-comment tsd-typography"><p>Whether it is a temporary view that can become invalid after the call.</p>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L468">runtime.ts:468</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L468">runtime.ts:468</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="ndim" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ndim</span><a href="#ndim" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">ndim</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<div class="tsd-comment tsd-typography"><p>Number of dimensions.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L460">runtime.ts:460</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L460">runtime.ts:460</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="shape" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>shape</span><a href="#shape" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">shape</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div>
<div class="tsd-comment tsd-typography"><p>Shape of the array.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L464">runtime.ts:464</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L464">runtime.ts:464</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="copyFrom" class="tsd-anchor"></a>
@@ -134,7 +134,7 @@ The number of elements must match.</p>
<h4 class="tsd-returns-title">Returns <a href="NDArray.html" class="tsd-signature-type tsd-kind-class">NDArray</a></h4><p>this</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L585">runtime.ts:585</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L585">runtime.ts:585</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="copyFromRawBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>copy<wbr/>From<wbr/>Raw<wbr/>Bytes</span><a href="#copyFromRawBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -152,7 +152,7 @@ The number of elements must match.</p>
<h4 class="tsd-returns-title">Returns <a href="NDArray.html" class="tsd-signature-type tsd-kind-class">NDArray</a></h4><p>this</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L634">runtime.ts:634</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L634">runtime.ts:634</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -165,7 +165,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L570">runtime.ts:570</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L570">runtime.ts:570</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getDataPtr" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Data<wbr/>Ptr</span><a href="#getDataPtr" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -176,7 +176,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The handle.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L563">runtime.ts:563</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L563">runtime.ts:563</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getHandle" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Handle</span><a href="#getHandle" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -194,7 +194,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The handle.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L551">runtime.ts:551</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L551">runtime.ts:551</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toArray" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>to<wbr/>Array</span><a href="#toArray" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -206,7 +206,7 @@ the dtype of the NDArray.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type ">Int32Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type ">Float32Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type ">Float64Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type ">Int8Array</span></h4><p>The resu [...]
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L700">runtime.ts:700</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L700">runtime.ts:700</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toRawBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>to<wbr/>Raw<wbr/>Bytes</span><a href="#toRawBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -217,7 +217,7 @@ the dtype of the NDArray.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Uint8Array</span></h4><p>The result array.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L669">runtime.ts:669</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L669">runtime.ts:669</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="view" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>view</span><a href="#view" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -239,7 +239,7 @@ the dtype of the NDArray.</p>
<h4 class="tsd-returns-title">Returns <a href="NDArray.html" class="tsd-signature-type tsd-kind-class">NDArray</a></h4><p>The new sliced ndarray.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L532">runtime.ts:532</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L532">runtime.ts:532</a></li></ul></aside></li></ul></section></section></div>
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diff --git a/docs/reference/api/typedoc/classes/RPCServer.html b/docs/reference/api/typedoc/classes/RPCServer.html
index b7242dbfa5..673ee0d9d0 100644
--- a/docs/reference/api/typedoc/classes/RPCServer.html
+++ b/docs/reference/api/typedoc/classes/RPCServer.html
@@ -23,7 +23,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">RPCServer</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
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<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -98,7 +98,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4></li></ul></li></ul></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="RPCServer.html" class="tsd-signature-type tsd-kind-class">RPCServer</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L100">rpc_server.ts:100</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L100">rpc_server.ts:100</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="getImports" class="tsd-anchor"></a>
@@ -113,12 +113,12 @@
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">unknown</span><span class="tsd-signature-symbol">></span></h4></li></ul></li></ul></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L83">rpc_server.ts:83</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L83">rpc_server.ts:83</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="key" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>key</span><a href="#key" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">key</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="logger" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>logger</span><a href="#logger" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">logger</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span><span class="tsd-kind-parameter">msg</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">)</span></div>
@@ -136,22 +136,22 @@
<h5><span class="tsd-kind-parameter">msg</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></li></ul></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="socket" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>socket</span><a href="#socket" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">socket</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type ">WebSocket</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="state" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>state</span><a href="#state" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">state</span><span class="tsd-signature-symbol">:</span> <a href="../enums/_internal_.RPCServerState.html" class="tsd-signature-type tsd-kind-enum">RPCServerState</a><span class="tsd-signature-symbol"> = RPCServerState.InitHeader</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="url" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>url</span><a href="#url" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">url</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li></ul></aside></section></section></div>
<div class="col-sidebar">
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<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/Scalar.html b/docs/reference/api/typedoc/classes/Scalar.html
index 6ce85a55c5..686d0503e5 100644
--- a/docs/reference/api/typedoc/classes/Scalar.html
+++ b/docs/reference/api/typedoc/classes/Scalar.html
@@ -24,7 +24,7 @@ argument to PackedFunc calls.</p>
<ul class="tsd-hierarchy">
<li><span class="target">Scalar</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L303">runtime.ts:303</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L303">runtime.ts:303</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
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<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -55,7 +55,7 @@ argument to PackedFunc calls.</p>
<h5><span class="tsd-kind-parameter">dtype</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="Scalar.html" class="tsd-signature-type tsd-kind-class">Scalar</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L309">runtime.ts:309</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L309">runtime.ts:309</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="dtype" class="tsd-anchor"></a>
@@ -64,14 +64,14 @@ argument to PackedFunc calls.</p>
<div class="tsd-comment tsd-typography"><p>The data type of the scalar.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L307">runtime.ts:307</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L307">runtime.ts:307</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="value" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>value</span><a href="#value" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">value</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
<div class="tsd-comment tsd-typography"><p>The value.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L305">runtime.ts:305</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L305">runtime.ts:305</a></li></ul></aside></section></section></div>
<div class="col-sidebar">
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diff --git a/docs/reference/api/typedoc/classes/TVMArray.html b/docs/reference/api/typedoc/classes/TVMArray.html
index 0612e5d344..48faad6a46 100644
--- a/docs/reference/api/typedoc/classes/TVMArray.html
+++ b/docs/reference/api/typedoc/classes/TVMArray.html
@@ -25,7 +25,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">TVMArray</span></li></ul></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L901">runtime.ts:901</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L901">runtime.ts:901</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -63,7 +63,7 @@
<h4 class="tsd-returns-title">Returns <a href="TVMArray.html" class="tsd-signature-type tsd-kind-class">TVMArray</a></h4><aside class="tsd-sources">
<p>Overrides <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#constructor">constructor</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L902">runtime.ts:902</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L902">runtime.ts:902</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="dispose" class="tsd-anchor"></a>
@@ -78,7 +78,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L832">runtime.ts:832</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L832">runtime.ts:832</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="get" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get</span><a href="#get" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -96,7 +96,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <a href="../types/_internal_.TVMObjectBase.html" class="tsd-signature-type tsd-kind-type-alias">TVMObjectBase</a></h4><p>The element.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L921">runtime.ts:921</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L921">runtime.ts:921</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="getHandle" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Handle</span><a href="#getHandle" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures tsd-is-inherited">
@@ -115,7 +115,7 @@ only the first call will take effect.</p>
<aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#getHandle">getHandle</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L847">runtime.ts:847</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L847">runtime.ts:847</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="size" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>size</span><a href="#size" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -125,7 +125,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>the size of the array.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L913">runtime.ts:913</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L913">runtime.ts:913</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="typeIndex" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Index</span><a href="#typeIndex" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures tsd-is-inherited">
@@ -136,7 +136,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#typeIndex">typeIndex</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L855">runtime.ts:855</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L855">runtime.ts:855</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="typeKey" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Key</span><a href="#typeKey" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures tsd-is-inherited">
@@ -147,7 +147,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#typeKey">typeKey</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L875">runtime.ts:875</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L875">runtime.ts:875</a></li></ul></aside></li></ul></section></section></div>
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<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/TVMObject.html b/docs/reference/api/typedoc/classes/TVMObject.html
index a0f8a0bcec..ffb73f14d7 100644
--- a/docs/reference/api/typedoc/classes/TVMObject.html
+++ b/docs/reference/api/typedoc/classes/TVMObject.html
@@ -30,7 +30,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L817">runtime.ts:817</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L817">runtime.ts:817</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -65,7 +65,7 @@
<h5><span class="tsd-kind-parameter">ctx</span>: <a href="_internal_.RuntimeContext.html" class="tsd-signature-type tsd-kind-class">RuntimeContext</a></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="TVMObject.html" class="tsd-signature-type tsd-kind-class">TVMObject</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L822">runtime.ts:822</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L822">runtime.ts:822</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
@@ -80,7 +80,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L832">runtime.ts:832</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L832">runtime.ts:832</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getHandle" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Handle</span><a href="#getHandle" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -98,7 +98,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The handle.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L847">runtime.ts:847</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L847">runtime.ts:847</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="typeIndex" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Index</span><a href="#typeIndex" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -108,7 +108,7 @@ only the first call will take effect.</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L855">runtime.ts:855</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L855">runtime.ts:855</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="typeKey" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Key</span><a href="#typeKey" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -118,7 +118,7 @@ only the first call will take effect.</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L875">runtime.ts:875</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L875">runtime.ts:875</a></li></ul></aside></li></ul></section></section></div>
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<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/VirtualMachine.html b/docs/reference/api/typedoc/classes/VirtualMachine.html
index 1d480401a6..fb1ecaa1e4 100644
--- a/docs/reference/api/typedoc/classes/VirtualMachine.html
+++ b/docs/reference/api/typedoc/classes/VirtualMachine.html
@@ -30,7 +30,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L956">runtime.ts:956</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L956">runtime.ts:956</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -68,7 +68,7 @@
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="VirtualMachine.html" class="tsd-signature-type tsd-kind-class">VirtualMachine</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L963">runtime.ts:963</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L963">runtime.ts:963</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
@@ -83,7 +83,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L976">runtime.ts:976</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L976">runtime.ts:976</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getFunction" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Function</span><a href="#getFunction" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -101,7 +101,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></h4><p>The result function.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L984">runtime.ts:984</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L984">runtime.ts:984</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getInternalModule" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Internal<wbr/>Module</span><a href="#getInternalModule" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -111,7 +111,7 @@ only the first call will take effect.</p>
</div>
<h4 class="tsd-returns-title">Returns <a href="Module.html" class="tsd-signature-type tsd-kind-class">Module</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L991">runtime.ts:991</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L991">runtime.ts:991</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.AsyncifyHandler.html b/docs/reference/api/typedoc/classes/_internal_.AsyncifyHandler.html
index 2b3c4d2a73..cb4ac9b984 100644
--- a/docs/reference/api/typedoc/classes/_internal_.AsyncifyHandler.html
+++ b/docs/reference/api/typedoc/classes/_internal_.AsyncifyHandler.html
@@ -24,7 +24,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">AsyncifyHandler</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L44">asyncify.ts:44</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L44">asyncify.ts:44</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -57,7 +57,7 @@
<h5><span class="tsd-kind-parameter">memory</span>: <span class="tsd-signature-type ">Memory</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.AsyncifyHandler.html" class="tsd-signature-type tsd-kind-class">AsyncifyHandler</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L58">asyncify.ts:58</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L58">asyncify.ts:58</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="enabled" class="tsd-anchor"></a>
@@ -70,7 +70,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">boolean</span></h4><p>Whether the wasm enables asynctify</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L69">asyncify.ts:69</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L69">asyncify.ts:69</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getState" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>State</span><a href="#getState" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -81,7 +81,7 @@
<h4 class="tsd-returns-title">Returns <a href="../enums/_internal_.AsyncifyStateKind.html" class="tsd-signature-type tsd-kind-enum">AsyncifyStateKind</a></h4><p>The current asynctify state</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L78">asyncify.ts:78</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L78">asyncify.ts:78</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="wrapExport" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>wrap<wbr/>Export</span><a href="#wrapExport" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -124,7 +124,7 @@
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</li></ul></li></ul><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L140">asyncify.ts:140</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L140">asyncify.ts:140</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="wrapImport" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>wrap<wbr/>Import</span><a href="#wrapImport" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -167,7 +167,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">any</span></h4><p>The wrapped function that can be registered to the system</p>
</li></ul></li></ul><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L88">asyncify.ts:88</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L88">asyncify.ts:88</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.CachedCallStack.html b/docs/reference/api/typedoc/classes/_internal_.CachedCallStack.html
index 8575f45311..c8a9e41cd4 100644
--- a/docs/reference/api/typedoc/classes/_internal_.CachedCallStack.html
+++ b/docs/reference/api/typedoc/classes/_internal_.CachedCallStack.html
@@ -36,7 +36,7 @@ can still call into storeXX</li>
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L206">memory.ts:206</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L206">memory.ts:206</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -86,7 +86,7 @@ can still call into storeXX</li>
<h5><span class="tsd-kind-parameter">freeSpace</span>: <a href="../types/_internal_.FTVMWasmFreeSpace.html" class="tsd-signature-type tsd-kind-type-alias">FTVMWasmFreeSpace</a></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.CachedCallStack.html" class="tsd-signature-type tsd-kind-class">CachedCallStack</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L225">memory.ts:225</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L225">memory.ts:225</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="tempArgs" class="tsd-anchor"></a>
@@ -95,7 +95,7 @@ can still call into storeXX</li>
<div class="tsd-comment tsd-typography"><p>List of temporay arguments that can be disposed during reset.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L208">memory.ts:208</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L208">memory.ts:208</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="allocPtrArray" class="tsd-anchor"></a>
@@ -115,7 +115,7 @@ can still call into storeXX</li>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><p>The allocated pointer array.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L312">memory.ts:312</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L312">memory.ts:312</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="allocRawBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>alloc<wbr/>Raw<wbr/>Bytes</span><a href="#allocRawBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -134,7 +134,7 @@ can still call into storeXX</li>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L284">memory.ts:284</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L284">memory.ts:284</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="allocThenSetArgBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>alloc<wbr/>Then<wbr/>Set<wbr/>Arg<wbr/>Bytes</span><a href="#allocThenSetArgBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -156,7 +156,7 @@ Allocate new temporary space for bytes.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L390">memory.ts:390</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L390">memory.ts:390</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="allocThenSetArgString" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>alloc<wbr/>Then<wbr/>Set<wbr/>Arg<wbr/>String</span><a href="#allocThenSetArgString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -180,7 +180,7 @@ and will be filled when we commit the data.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L377">memory.ts:377</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L377">memory.ts:377</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="commitToWasmMemory" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>commit<wbr/>To<wbr/>Wasm<wbr/>Memory</span><a href="#commitToWasmMemory" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -199,7 +199,7 @@ No further store function should be called.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L267">memory.ts:267</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L267">memory.ts:267</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -212,7 +212,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L243">memory.ts:243</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L243">memory.ts:243</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="ptrFromOffset" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ptr<wbr/>From<wbr/>Offset</span><a href="#ptrFromOffset" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -230,7 +230,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L321">memory.ts:321</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L321">memory.ts:321</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="reset" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>reset</span><a href="#reset" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -240,7 +240,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L252">memory.ts:252</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L252">memory.ts:252</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeF64" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>F64</span><a href="#storeF64" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -255,7 +255,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">value</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L360">memory.ts:360</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L360">memory.ts:360</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeI32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>I32</span><a href="#storeI32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -270,7 +270,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">value</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L342">memory.ts:342</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L342">memory.ts:342</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeI64" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>I64</span><a href="#storeI64" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -285,7 +285,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">value</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L350">memory.ts:350</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L350">memory.ts:350</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storePtr" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>Ptr</span><a href="#storePtr" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -300,7 +300,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">value</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L326">memory.ts:326</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L326">memory.ts:326</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeRawBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>Raw<wbr/>Bytes</span><a href="#storeRawBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -315,7 +315,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">bytes</span>: <span class="tsd-signature-type ">Uint8Array</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L364">memory.ts:364</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L364">memory.ts:364</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeU32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>U32</span><a href="#storeU32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -330,7 +330,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">value</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L346">memory.ts:346</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L346">memory.ts:346</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeUSize" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>storeUSize</span><a href="#storeUSize" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -345,7 +345,7 @@ Note that the returned value becomes obsolete if alloc is called on the stack.</
<h5><span class="tsd-kind-parameter">value</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L334">memory.ts:334</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L334">memory.ts:334</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.CanvasRenderManager.html b/docs/reference/api/typedoc/classes/_internal_.CanvasRenderManager.html
index 116ab672a4..34623a59fb 100644
--- a/docs/reference/api/typedoc/classes/_internal_.CanvasRenderManager.html
+++ b/docs/reference/api/typedoc/classes/_internal_.CanvasRenderManager.html
@@ -29,7 +29,7 @@ which needs to be explicitly disposed.</p>
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L209">webgpu.ts:209</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L209">webgpu.ts:209</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
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@@ -61,7 +61,7 @@ which needs to be explicitly disposed.</p>
<h5><span class="tsd-kind-parameter">canvas</span>: <span class="tsd-signature-type ">HTMLCanvasElement</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.CanvasRenderManager.html" class="tsd-signature-type tsd-kind-class">CanvasRenderManager</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L218">webgpu.ts:218</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L218">webgpu.ts:218</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="clear" class="tsd-anchor"></a>
@@ -71,7 +71,7 @@ which needs to be explicitly disposed.</p>
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L292">webgpu.ts:292</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L292">webgpu.ts:292</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -84,7 +84,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L368">webgpu.ts:368</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L368">webgpu.ts:368</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="draw" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>draw</span><a href="#draw" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -101,7 +101,7 @@ only the first call will take effect.</p>
<h5><span class="tsd-kind-parameter">width</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L318">webgpu.ts:318</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L318">webgpu.ts:318</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.Environment.html b/docs/reference/api/typedoc/classes/_internal_.Environment.html
index b6b9ffb5f2..ee827424a4 100644
--- a/docs/reference/api/typedoc/classes/_internal_.Environment.html
+++ b/docs/reference/api/typedoc/classes/_internal_.Environment.html
@@ -28,7 +28,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/LibraryProvider.html" class="tsd-signature-type tsd-kind-interface">LibraryProvider</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L68">environment.ts:68</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L68">environment.ts:68</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -76,7 +76,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></li></ul></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.Environment.html" class="tsd-signature-type tsd-kind-class">Environment</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L88">environment.ts:88</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L88">environment.ts:88</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="imports" class="tsd-anchor"></a>
@@ -86,7 +86,7 @@
</div><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/LibraryProvider.html">LibraryProvider</a>.<a href="../interfaces/LibraryProvider.html#imports">imports</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L70">environment.ts:70</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L70">environment.ts:70</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="logger" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>logger</span><a href="#logger" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">logger</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span><span class="tsd-kind-parameter">msg</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">)</span></div>
@@ -104,7 +104,7 @@
<h5><span class="tsd-kind-parameter">msg</span>: <span class="tsd-signature-type">string</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></li></ul></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L69">environment.ts:69</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L69">environment.ts:69</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="packedCFuncTable" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>packedCFunc<wbr/>Table</span><a href="#packedCFuncTable" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">packedCFunc<wbr/>Table</span><span class="tsd-signature-symbol">:</span> <a href="../types/_internal_.FTVMWasmPackedCFunc.html" class="tsd-signature-type tsd-kind-type-alias">FTVMWasmPackedCFunc</a><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> = ...</span></div>
@@ -114,14 +114,14 @@ can call via TVMWasmPackedCFunc.</p>
of functions that do not maps to the address space.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L78">environment.ts:78</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L78">environment.ts:78</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="packedCFuncTableFreeId" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>packedCFunc<wbr/>Table<wbr/>Free<wbr/>Id</span><a href="#packedCFuncTableFreeId" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">packedCFunc<wbr/>Table<wbr/>Free<wbr/>Id</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> = []</span></div>
<div class="tsd-comment tsd-typography"><p>Free table index that can be recycled.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L84">environment.ts:84</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L84">environment.ts:84</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="start" class="tsd-anchor"></a>
@@ -139,7 +139,7 @@ of functions that do not maps to the address space.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/LibraryProvider.html">LibraryProvider</a>.<a href="../interfaces/LibraryProvider.html#start">start</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/environment.ts#L105">environment.ts:105</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/environment.ts#L105">environment.ts:105</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.FFILibrary.html b/docs/reference/api/typedoc/classes/_internal_.FFILibrary.html
index 0f73fc746b..9d0e62c941 100644
--- a/docs/reference/api/typedoc/classes/_internal_.FFILibrary.html
+++ b/docs/reference/api/typedoc/classes/_internal_.FFILibrary.html
@@ -28,7 +28,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L56">runtime.ts:56</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L56">runtime.ts:56</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -69,29 +69,29 @@
<h5><span class="tsd-kind-parameter">imports</span>: <span class="tsd-signature-type ">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">></span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.FFILibrary.html" class="tsd-signature-type tsd-kind-class">FFILibrary</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L64">runtime.ts:64</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L64">runtime.ts:64</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="exports" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>exports</span><a href="#exports" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">exports</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type ">Record</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type ">Function</span><span class="tsd-signature-symbol">></span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L59">runtime.ts:59</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L59">runtime.ts:59</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="memory" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>memory</span><a href="#memory" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">memory</span><span class="tsd-signature-symbol">:</span> <a href="_internal_.Memory.html" class="tsd-signature-type tsd-kind-class">Memory</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L58">runtime.ts:58</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L58">runtime.ts:58</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="wasm32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>wasm32</span><a href="#wasm32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">wasm32</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L57">runtime.ts:57</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L57">runtime.ts:57</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="webGPUContext" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>webGPUContext</span><a href="#webGPUContext" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">webGPUContext</span><span class="tsd-signature-symbol">?:</span> <a href="_internal_.WebGPUContext.html" class="tsd-signature-type tsd-kind-class">WebGPUContext</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L60">runtime.ts:60</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L60">runtime.ts:60</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="checkCall" class="tsd-anchor"></a>
@@ -106,7 +106,7 @@
<h5><span class="tsd-kind-parameter">code</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L90">runtime.ts:90</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L90">runtime.ts:90</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -119,7 +119,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L79">runtime.ts:79</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L79">runtime.ts:79</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getOrAllocCallStack" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Or<wbr/>Alloc<wbr/>Call<wbr/>Stack</span><a href="#getOrAllocCallStack" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -127,7 +127,7 @@ only the first call will take effect.</p>
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <a href="_internal_.CachedCallStack.html" class="tsd-signature-type tsd-kind-class">CachedCallStack</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L98">runtime.ts:98</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L98">runtime.ts:98</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="recycleCallStack" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>recycle<wbr/>Call<wbr/>Stack</span><a href="#recycleCallStack" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -140,7 +140,7 @@ only the first call will take effect.</p>
<h5><span class="tsd-kind-parameter">callstack</span>: <a href="_internal_.CachedCallStack.html" class="tsd-signature-type tsd-kind-class">CachedCallStack</a></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L109">runtime.ts:109</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L109">runtime.ts:109</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="sizeofPtr" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sizeof<wbr/>Ptr</span><a href="#sizeofPtr" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -148,7 +148,7 @@ only the first call will take effect.</p>
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L86">runtime.ts:86</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L86">runtime.ts:86</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.Memory.html b/docs/reference/api/typedoc/classes/_internal_.Memory.html
index 1fe98d356b..c8f1a202ac 100644
--- a/docs/reference/api/typedoc/classes/_internal_.Memory.html
+++ b/docs/reference/api/typedoc/classes/_internal_.Memory.html
@@ -24,7 +24,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">Memory</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L31">memory.ts:31</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L31">memory.ts:31</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -70,19 +70,19 @@
<h5><span class="tsd-kind-parameter">memory</span>: <span class="tsd-signature-type ">Memory</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.Memory.html" class="tsd-signature-type tsd-kind-class">Memory</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L42">memory.ts:42</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L42">memory.ts:42</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="memory" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>memory</span><a href="#memory" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">memory</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type ">Memory</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L32">memory.ts:32</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L32">memory.ts:32</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="wasm32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>wasm32</span><a href="#wasm32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">wasm32</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L33">memory.ts:33</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L33">memory.ts:33</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="loadCString" class="tsd-anchor"></a>
@@ -101,7 +101,7 @@
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L154">memory.ts:154</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L154">memory.ts:154</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadF32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>F32</span><a href="#loadF32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -114,7 +114,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L90">memory.ts:90</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L90">memory.ts:90</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadF64" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>F64</span><a href="#loadF64" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -127,7 +127,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L97">memory.ts:97</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L97">memory.ts:97</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadI32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>I32</span><a href="#loadI32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -140,7 +140,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L74">memory.ts:74</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L74">memory.ts:74</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadI64" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>I64</span><a href="#loadI64" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -153,7 +153,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L81">memory.ts:81</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L81">memory.ts:81</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadPointer" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>Pointer</span><a href="#loadPointer" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -166,7 +166,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L104">memory.ts:104</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L104">memory.ts:104</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadRawBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>Raw<wbr/>Bytes</span><a href="#loadRawBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -187,7 +187,7 @@
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Uint8Array</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L132">memory.ts:132</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L132">memory.ts:132</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadTVMBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>loadTVMBytes</span><a href="#loadTVMBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -204,7 +204,7 @@
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Uint8Array</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L145">memory.ts:145</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L145">memory.ts:145</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadU16" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>U16</span><a href="#loadU16" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -217,7 +217,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L60">memory.ts:60</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L60">memory.ts:60</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadU32" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>U32</span><a href="#loadU32" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -230,7 +230,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L67">memory.ts:67</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L67">memory.ts:67</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadU8" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>load<wbr/>U8</span><a href="#loadU8" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -243,7 +243,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L53">memory.ts:53</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L53">memory.ts:53</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="loadUSize" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>loadUSize</span><a href="#loadUSize" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -256,7 +256,7 @@
<h5><span class="tsd-kind-parameter">ptr</span>: <span class="tsd-signature-type">number</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L114">memory.ts:114</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L114">memory.ts:114</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="sizeofPtr" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sizeof<wbr/>Ptr</span><a href="#sizeofPtr" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -264,7 +264,7 @@
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L124">memory.ts:124</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L124">memory.ts:124</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="storeRawBytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>store<wbr/>Raw<wbr/>Bytes</span><a href="#storeRawBytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -285,7 +285,7 @@
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/memory.ts#L175">memory.ts:175</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/memory.ts#L175">memory.ts:175</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.PackedFuncCell.html b/docs/reference/api/typedoc/classes/_internal_.PackedFuncCell.html
index b41db19063..2945ff9c2d 100644
--- a/docs/reference/api/typedoc/classes/_internal_.PackedFuncCell.html
+++ b/docs/reference/api/typedoc/classes/_internal_.PackedFuncCell.html
@@ -28,7 +28,7 @@
<ul class="tsd-hierarchy">
<li><a href="../interfaces/Disposable.html" class="tsd-signature-type tsd-kind-interface">Disposable</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L318">runtime.ts:318</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L318">runtime.ts:318</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -59,7 +59,7 @@
<h5><span class="tsd-kind-parameter">lib</span>: <a href="_internal_.FFILibrary.html" class="tsd-signature-type tsd-kind-class">FFILibrary</a></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.PackedFuncCell.html" class="tsd-signature-type tsd-kind-class">PackedFuncCell</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L322">runtime.ts:322</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L322">runtime.ts:322</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
@@ -74,7 +74,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L327">runtime.ts:327</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L327">runtime.ts:327</a></li></ul></aside></li></ul></section>
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<h3 class="tsd-anchor-link"><span>get<wbr/>Handle</span><a href="#getHandle" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L336">runtime.ts:336</a></li></ul></aside></li></ul></section></section></div>
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diff --git a/docs/reference/api/typedoc/classes/_internal_.RuntimeContext.html b/docs/reference/api/typedoc/classes/_internal_.RuntimeContext.html
index d9d8554bb3..9a53b344f6 100644
--- a/docs/reference/api/typedoc/classes/_internal_.RuntimeContext.html
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-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L155">runtime.ts:155</a></li></ul></aside>
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<h4 class="tsd-returns-title">Returns <a href="_internal_.RuntimeContext.html" class="tsd-signature-type tsd-kind-class">RuntimeContext</a></h4><aside class="tsd-sources">
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L181">runtime.ts:181</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="applyPresenceAndFrequencyPenalty" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>apply<wbr/>Presence<wbr/>And<wbr/>Frequency<wbr/>Penalty</span><a href="#applyPresenceAndFrequencyPenalty" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">apply<wbr/>Presence<wbr/>And<wbr/>Frequency<wbr/>Penalty</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L175">runtime.ts:175</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L175">runtime.ts:175</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="applyRepetitionPenalty" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>apply<wbr/>Repetition<wbr/>Penalty</span><a href="#applyRepetitionPenalty" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">apply<wbr/>Repetition<wbr/>Penalty</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L174">runtime.ts:174</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L174">runtime.ts:174</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="applySoftmaxWithTemperature" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>apply<wbr/>Softmax<wbr/>With<wbr/>Temperature</span><a href="#applySoftmaxWithTemperature" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">apply<wbr/>Softmax<wbr/>With<wbr/>Temperature</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L176">runtime.ts:176</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L176">runtime.ts:176</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayCacheClear" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>array<wbr/>Cache<wbr/>Clear</span><a href="#arrayCacheClear" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">array<wbr/>Cache<wbr/>Clear</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L166">runtime.ts:166</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L166">runtime.ts:166</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayCacheGet" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>array<wbr/>Cache<wbr/>Get</span><a href="#arrayCacheGet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">array<wbr/>Cache<wbr/>Get</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L163">runtime.ts:163</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L163">runtime.ts:163</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayCacheRemove" class="tsd-anchor"></a>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L165">runtime.ts:165</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L165">runtime.ts:165</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayCacheUpdate" class="tsd-anchor"></a>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L164">runtime.ts:164</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L164">runtime.ts:164</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayConcat" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>array<wbr/>Concat</span><a href="#arrayConcat" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">array<wbr/>Concat</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L159">runtime.ts:159</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L159">runtime.ts:159</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayDecodeStorage" class="tsd-anchor"></a>
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<div class="tsd-signature"><span class="tsd-kind-property">array<wbr/>Decode<wbr/>Storage</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L167">runtime.ts:167</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L167">runtime.ts:167</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayGetItem" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>array<wbr/>Get<wbr/>Item</span><a href="#arrayGetItem" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">array<wbr/>Get<wbr/>Item</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L156">runtime.ts:156</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L156">runtime.ts:156</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayGetSize" class="tsd-anchor"></a>
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<div class="tsd-signature"><span class="tsd-kind-property">array<wbr/>Get<wbr/>Size</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L157">runtime.ts:157</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L157">runtime.ts:157</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="arrayMake" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>array<wbr/>Make</span><a href="#arrayMake" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L158">runtime.ts:158</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L158">runtime.ts:158</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="concatEmbeddings" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>concat<wbr/>Embeddings</span><a href="#concatEmbeddings" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">concat<wbr/>Embeddings</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L177">runtime.ts:177</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L177">runtime.ts:177</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="getFFIString" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>getFFIString</span><a href="#getFFIString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">getFFIString</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L161">runtime.ts:161</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L161">runtime.ts:161</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="getSysLib" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Sys<wbr/>Lib</span><a href="#getSysLib" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">get<wbr/>Sys<wbr/>Lib</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L162">runtime.ts:162</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="makeShapeTuple" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>make<wbr/>Shape<wbr/>Tuple</span><a href="#makeShapeTuple" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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<section class="tsd-panel tsd-member"><a id="ndarrayCreateView" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>ndarray<wbr/>Create<wbr/>View</span><a href="#ndarrayCreateView" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L171">runtime.ts:171</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="paramModuleFromCache" class="tsd-anchor"></a>
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<div class="tsd-signature"><span class="tsd-kind-property">param<wbr/>Module<wbr/>From<wbr/>Cache</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L168">runtime.ts:168</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L168">runtime.ts:168</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="paramModuleFromCacheByName" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>param<wbr/>Module<wbr/>From<wbr/>Cache<wbr/>By<wbr/>Name</span><a href="#paramModuleFromCacheByName" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">param<wbr/>Module<wbr/>From<wbr/>Cache<wbr/>By<wbr/>Name</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L169">runtime.ts:169</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L169">runtime.ts:169</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="sampleTopPFromLogits" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sample<wbr/>TopPFrom<wbr/>Logits</span><a href="#sampleTopPFromLogits" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">sample<wbr/>TopPFrom<wbr/>Logits</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L172">runtime.ts:172</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L172">runtime.ts:172</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="sampleTopPFromProb" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sample<wbr/>TopPFrom<wbr/>Prob</span><a href="#sampleTopPFromProb" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">sample<wbr/>TopPFrom<wbr/>Prob</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L173">runtime.ts:173</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L173">runtime.ts:173</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="stringMake" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>string<wbr/>Make</span><a href="#stringMake" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">string<wbr/>Make</span><span class="tsd-signature-symbol">:</span> <a href="../types/PackedFunc.html" class="tsd-signature-type tsd-kind-type-alias">PackedFunc</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L160">runtime.ts:160</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L160">runtime.ts:160</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="attachToCurrentScope" class="tsd-anchor"></a>
@@ -237,7 +237,7 @@
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><p>the same object.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L261">runtime.ts:261</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L261">runtime.ts:261</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="beginScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>begin<wbr/>Scope</span><a href="#beginScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -245,7 +245,7 @@
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L235">runtime.ts:235</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L235">runtime.ts:235</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="detachFromCurrentScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>detach<wbr/>From<wbr/>Current<wbr/>Scope</span><a href="#detachFromCurrentScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -263,7 +263,7 @@
<h5><span class="tsd-kind-parameter">obj</span>: <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L280">runtime.ts:280</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L280">runtime.ts:280</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -276,7 +276,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Implementation of <a href="../interfaces/Disposable.html">Disposable</a>.<a href="../interfaces/Disposable.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L210">runtime.ts:210</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L210">runtime.ts:210</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="endScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>end<wbr/>Scope</span><a href="#endScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -284,7 +284,7 @@ only the first call will take effect.</p>
<li class="tsd-description">
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L239">runtime.ts:239</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L239">runtime.ts:239</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="moveToParentScope" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>move<wbr/>To<wbr/>Parent<wbr/>Scope</span><a href="#moveToParentScope" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -302,7 +302,7 @@ only the first call will take effect.</p>
<h5><span class="tsd-kind-parameter">obj</span>: <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type tsd-kind-type-parameter">T</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L270">runtime.ts:270</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L270">runtime.ts:270</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.TVMString.html b/docs/reference/api/typedoc/classes/_internal_.TVMString.html
index f1226cbd1d..f779979b80 100644
--- a/docs/reference/api/typedoc/classes/_internal_.TVMString.html
+++ b/docs/reference/api/typedoc/classes/_internal_.TVMString.html
@@ -26,7 +26,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">TVMString</span></li></ul></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L927">runtime.ts:927</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L927">runtime.ts:927</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -63,7 +63,7 @@
<h4 class="tsd-returns-title">Returns <a href="_internal_.TVMString.html" class="tsd-signature-type tsd-kind-class">TVMString</a></h4><aside class="tsd-sources">
<p>Overrides <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#constructor">constructor</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L928">runtime.ts:928</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L928">runtime.ts:928</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="dispose" class="tsd-anchor"></a>
@@ -78,7 +78,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#dispose">dispose</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L832">runtime.ts:832</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L832">runtime.ts:832</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="getHandle" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>Handle</span><a href="#getHandle" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures tsd-is-inherited">
@@ -97,7 +97,7 @@ only the first call will take effect.</p>
<aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#getHandle">getHandle</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L847">runtime.ts:847</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L847">runtime.ts:847</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="toString" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>to<wbr/>String</span><a href="#toString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -107,7 +107,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><p>the size of the array.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L939">runtime.ts:939</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L939">runtime.ts:939</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="typeIndex" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Index</span><a href="#typeIndex" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures tsd-is-inherited">
@@ -118,7 +118,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#typeIndex">typeIndex</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L855">runtime.ts:855</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L855">runtime.ts:855</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member tsd-is-inherited"><a id="typeKey" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>type<wbr/>Key</span><a href="#typeKey" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures tsd-is-inherited">
@@ -129,7 +129,7 @@ only the first call will take effect.</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<p>Inherited from <a href="TVMObject.html">TVMObject</a>.<a href="TVMObject.html#typeKey">typeKey</a></p>
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L875">runtime.ts:875</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L875">runtime.ts:875</a></li></ul></aside></li></ul></section></section></div>
<div class="col-sidebar">
<div class="page-menu">
<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/classes/_internal_.WebGPUContext.html b/docs/reference/api/typedoc/classes/_internal_.WebGPUContext.html
index af3b87ecbf..834c49f02d 100644
--- a/docs/reference/api/typedoc/classes/_internal_.WebGPUContext.html
+++ b/docs/reference/api/typedoc/classes/_internal_.WebGPUContext.html
@@ -25,7 +25,7 @@ Manages all the webgpu resources here.</p>
<ul class="tsd-hierarchy">
<li><span class="target">WebGPUContext</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L386">webgpu.ts:386</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L386">webgpu.ts:386</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -69,19 +69,19 @@ Manages all the webgpu resources here.</p>
<h5><span class="tsd-kind-parameter">device</span>: <span class="tsd-signature-type ">GPUDevice</span></h5></li></ul></div>
<h4 class="tsd-returns-title">Returns <a href="_internal_.WebGPUContext.html" class="tsd-signature-type tsd-kind-class">WebGPUContext</a></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L411">webgpu.ts:411</a></li></ul></aside></li></ul></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L411">webgpu.ts:411</a></li></ul></aside></li></ul></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Properties</h2>
<section class="tsd-panel tsd-member"><a id="device" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>device</span><a href="#device" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">device</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type ">GPUDevice</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L387">webgpu.ts:387</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L387">webgpu.ts:387</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="memory" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>memory</span><a href="#memory" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">memory</span><span class="tsd-signature-symbol">:</span> <a href="_internal_.Memory.html" class="tsd-signature-type tsd-kind-class">Memory</a></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L388">webgpu.ts:388</a></li></ul></aside></section></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L388">webgpu.ts:388</a></li></ul></aside></section></section>
<section class="tsd-panel-group tsd-member-group">
<h2>Methods</h2>
<section class="tsd-panel tsd-member"><a id="bindCanvas" class="tsd-anchor"></a>
@@ -100,7 +100,7 @@ Manages all the webgpu resources here.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L495">webgpu.ts:495</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L495">webgpu.ts:495</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="clearCanvas" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>clear<wbr/>Canvas</span><a href="#clearCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -110,7 +110,7 @@ Manages all the webgpu resources here.</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L487">webgpu.ts:487</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L487">webgpu.ts:487</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="copyRawBytesToBuffer" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>copy<wbr/>Raw<wbr/>Bytes<wbr/>To<wbr/>Buffer</span><a href="#copyRawBytesToBuffer" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -139,7 +139,7 @@ Manages all the webgpu resources here.</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L469">webgpu.ts:469</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L469">webgpu.ts:469</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="createShader" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>create<wbr/>Shader</span><a href="#createShader" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -160,7 +160,7 @@ via createComputePipeline</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Function</span></h4><p>The shader</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L507">webgpu.ts:507</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L507">webgpu.ts:507</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="createShaderAsync" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>create<wbr/>Shader<wbr/>Async</span><a href="#createShaderAsync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -181,7 +181,7 @@ via createComputePipelineAsync</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type ">Function</span><span class="tsd-signature-symbol">></span></h4><p>The shader</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L519">webgpu.ts:519</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L519">webgpu.ts:519</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="dispose" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -191,7 +191,7 @@ via createComputePipelineAsync</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L419">webgpu.ts:419</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L419">webgpu.ts:419</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="drawImageFromBuffer" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>draw<wbr/>Image<wbr/>From<wbr/>Buffer</span><a href="#drawImageFromBuffer" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -216,7 +216,7 @@ via createComputePipelineAsync</p>
</div></li></ul></div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L454">webgpu.ts:454</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L454">webgpu.ts:454</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="getDeviceAPI" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>get<wbr/>DeviceAPI</span><a href="#getDeviceAPI" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -232,7 +232,7 @@ via createComputePipelineAsync</p>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Function</span></h4><p>The corresponding device api.</p>
<aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L775">webgpu.ts:775</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L775">webgpu.ts:775</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="runtimeStatsText" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>runtime<wbr/>Stats<wbr/>Text</span><a href="#runtimeStatsText" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -242,7 +242,7 @@ via createComputePipelineAsync</p>
</div>
<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L441">webgpu.ts:441</a></li></ul></aside></li></ul></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L441">webgpu.ts:441</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="sync" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>sync</span><a href="#sync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<ul class="tsd-signatures">
@@ -252,7 +252,7 @@ via createComputePipelineAsync</p>
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type ">Promise</span><span class="tsd-signature-symbol"><</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">></span></h4><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L434">webgpu.ts:434</a></li></ul></aside></li></ul></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L434">webgpu.ts:434</a></li></ul></aside></li></ul></section></section></div>
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diff --git a/docs/reference/api/typedoc/enums/_internal_.AsyncifyStateKind.html b/docs/reference/api/typedoc/enums/_internal_.AsyncifyStateKind.html
index 912008900e..27ade320e6 100644
--- a/docs/reference/api/typedoc/enums/_internal_.AsyncifyStateKind.html
+++ b/docs/reference/api/typedoc/enums/_internal_.AsyncifyStateKind.html
@@ -20,7 +20,7 @@
<div class="tsd-comment tsd-typography"><p>enums to check the current state of asynctify</p>
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L30">asyncify.ts:30</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L30">asyncify.ts:30</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -38,17 +38,17 @@
<h3 class="tsd-anchor-link"><span>None</span><a href="#None" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 [...]
<div class="tsd-signature"><span class="tsd-kind-enum-member">None</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">0</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L31">asyncify.ts:31</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L31">asyncify.ts:31</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="Rewinding" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>Rewinding</span><a href="#Rewinding" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-enum-member">Rewinding</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">2</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L33">asyncify.ts:33</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L33">asyncify.ts:33</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="Unwinding" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>Unwinding</span><a href="#Unwinding" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-enum-member">Unwinding</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">1</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/asyncify.ts#L32">asyncify.ts:32</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/asyncify.ts#L32">asyncify.ts:32</a></li></ul></aside></section></section></div>
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<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/enums/_internal_.RPCServerState.html b/docs/reference/api/typedoc/enums/_internal_.RPCServerState.html
index fd95988cbf..624f997e9b 100644
--- a/docs/reference/api/typedoc/enums/_internal_.RPCServerState.html
+++ b/docs/reference/api/typedoc/enums/_internal_.RPCServerState.html
@@ -17,7 +17,7 @@
<li><a href="_internal_.RPCServerState.html">RPCServerState</a></li></ul>
<h1>Enumeration RPCServerState</h1></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li></ul></aside>
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<section class="tsd-panel tsd-index-panel">
<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
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<div class="tsd-signature"><span class="tsd-kind-enum-member">Init<wbr/>Header</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">0</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="InitHeaderKey" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>Init<wbr/>Header<wbr/>Key</span><a href="#InitHeaderKey" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-enum-member">Init<wbr/>Header<wbr/>Key</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">1</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="InitServer" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>Init<wbr/>Server</span><a href="#InitServer" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-enum-member">Init<wbr/>Server</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">2</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="ReceivePacketBody" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>Receive<wbr/>Packet<wbr/>Body</span><a href="#ReceivePacketBody" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-enum-member">Receive<wbr/>Packet<wbr/>Body</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">5</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L33">rpc_server.ts:33</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L33">rpc_server.ts:33</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="ReceivePacketHeader" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>Receive<wbr/>Packet<wbr/>Header</span><a href="#ReceivePacketHeader" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-enum-member">Receive<wbr/>Packet<wbr/>Header</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">4</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="WaitForCallback" class="tsd-anchor"></a>
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<div class="tsd-signature"><span class="tsd-kind-enum-member">Wait<wbr/>For<wbr/>Callback</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">3</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li></ul></aside></section></section></div>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L65">support.ts:65</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/functions/createPolyfillWASI.html b/docs/reference/api/typedoc/functions/createPolyfillWASI.html
index 9ac43f7b23..e65ced056f 100644
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/compact.ts#L55">compact.ts:55</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/functions/deleteNDArrayCache.html b/docs/reference/api/typedoc/functions/deleteNDArrayCache.html
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L397">artifact_cache.ts:397</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/functions/detectGPUDevice.html b/docs/reference/api/typedoc/functions/detectGPUDevice.html
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L36">webgpu.ts:36</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/functions/hasNDArrayInCache.html b/docs/reference/api/typedoc/functions/hasNDArrayInCache.html
index b84bac2f6e..2fa76222d0 100644
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<ul>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L365">artifact_cache.ts:365</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/functions/instantiate.html b/docs/reference/api/typedoc/functions/instantiate.html
index 7f1ee22b38..38aa3671eb 100644
--- a/docs/reference/api/typedoc/functions/instantiate.html
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L2592">runtime.ts:2592</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/functions/wasmPath.html b/docs/reference/api/typedoc/functions/wasmPath.html
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--- a/docs/reference/api/typedoc/functions/wasmPath.html
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/support.ts#L75">support.ts:75</a></li></ul></aside></li></ul></section></div>
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diff --git a/docs/reference/api/typedoc/interfaces/ArtifactCacheTemplate.html b/docs/reference/api/typedoc/interfaces/ArtifactCacheTemplate.html
index 4b96cd1343..1fbd7167bf 100644
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-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L39">artifact_cache.ts:39</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L39">artifact_cache.ts:39</a></li></ul></aside>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L69">artifact_cache.ts:69</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="deleteInCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>delete<wbr/>In<wbr/>Cache</span><a href="#deleteInCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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<ul>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L83">artifact_cache.ts:83</a></li></ul></aside></li></ul></section>
<section class="tsd-panel tsd-member"><a id="fetchWithCache" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>fetch<wbr/>With<wbr/>Cache</span><a href="#fetchWithCache" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L55">artifact_cache.ts:55</a></li></ul></aside></li></ul></section>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L76">artifact_cache.ts:76</a></li></ul></aside></li></ul></section></section></div>
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diff --git a/docs/reference/api/typedoc/interfaces/Disposable.html b/docs/reference/api/typedoc/interfaces/Disposable.html
index 712d611edc..0620e186a1 100644
--- a/docs/reference/api/typedoc/interfaces/Disposable.html
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<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/types.ts#L46">types.ts:46</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/types.ts#L46">types.ts:46</a></li></ul></aside>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/types.ts#L52">types.ts:52</a></li></ul></aside></section></section></div>
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diff --git a/docs/reference/api/typedoc/interfaces/GPUDeviceDetectOutput.html b/docs/reference/api/typedoc/interfaces/GPUDeviceDetectOutput.html
index ff46caa946..57c0ba6290 100644
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-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L27">webgpu.ts:27</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L27">webgpu.ts:27</a></li></ul></aside>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L28">webgpu.ts:28</a></li></ul></aside></section>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L29">webgpu.ts:29</a></li></ul></aside></section>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L30">webgpu.ts:30</a></li></ul></aside></section></section></div>
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diff --git a/docs/reference/api/typedoc/interfaces/InitProgressReport.html b/docs/reference/api/typedoc/interfaces/InitProgressReport.html
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1002">runtime.ts:1002</a></li></ul></aside>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1003">runtime.ts:1003</a></li></ul></aside></section>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1005">runtime.ts:1005</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="timeElapsed" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>time<wbr/>Elapsed</span><a href="#timeElapsed" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">time<wbr/>Elapsed</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1004">runtime.ts:1004</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1004">runtime.ts:1004</a></li></ul></aside></section></section></div>
<div class="col-sidebar">
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diff --git a/docs/reference/api/typedoc/interfaces/LibraryProvider.html b/docs/reference/api/typedoc/interfaces/LibraryProvider.html
index f270599898..886ba47be2 100644
--- a/docs/reference/api/typedoc/interfaces/LibraryProvider.html
+++ b/docs/reference/api/typedoc/interfaces/LibraryProvider.html
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<ul class="tsd-hierarchy">
<li><a href="../classes/_internal_.Environment.html" class="tsd-signature-type tsd-kind-class">Environment</a></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/types.ts#L32">types.ts:32</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/types.ts#L32">types.ts:32</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
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@@ -51,7 +51,7 @@ to allow the library provider to initialize related resources during startup tim
<div class="tsd-comment tsd-typography"><p>The imports that can be passed to WebAssembly instance creation.</p>
</div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/types.ts#L34">types.ts:34</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/types.ts#L34">types.ts:34</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="start" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>start</span><a href="#start" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">start</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span><span class="tsd-kind-parameter">inst</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> => </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">)</span></div>
@@ -73,7 +73,7 @@ to allow the library provider to initialize related resources during startup tim
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></li></ul></div><aside class="tsd-sources">
<ul>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/types.ts#L39">types.ts:39</a></li></ul></aside></section></section></div>
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diff --git a/docs/reference/api/typedoc/interfaces/_internal_.FunctionInfo.html b/docs/reference/api/typedoc/interfaces/_internal_.FunctionInfo.html
index 418e869d34..497184d5ce 100644
--- a/docs/reference/api/typedoc/interfaces/_internal_.FunctionInfo.html
+++ b/docs/reference/api/typedoc/interfaces/_internal_.FunctionInfo.html
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<ul class="tsd-hierarchy">
<li><span class="target">FunctionInfo</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L376">webgpu.ts:376</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L376">webgpu.ts:376</a></li></ul></aside>
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@@ -42,17 +42,17 @@
<h3 class="tsd-anchor-link"><span>arg_<wbr/>types</span><a href="#arg_types" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0 [...]
<div class="tsd-signature"><span class="tsd-kind-property">arg_<wbr/>types</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L378">webgpu.ts:378</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L378">webgpu.ts:378</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="launch_param_tags" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>launch_<wbr/>param_<wbr/>tags</span><a href="#launch_param_tags" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">launch_<wbr/>param_<wbr/>tags</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L379">webgpu.ts:379</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L379">webgpu.ts:379</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="name" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>name</span><a href="#name" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">name</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/webgpu.ts#L377">webgpu.ts:377</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/webgpu.ts#L377">webgpu.ts:377</a></li></ul></aside></section></section></div>
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<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/interfaces/_internal_.NDArrayCacheEntry.html b/docs/reference/api/typedoc/interfaces/_internal_.NDArrayCacheEntry.html
index aab9e1acbe..47f20315f5 100644
--- a/docs/reference/api/typedoc/interfaces/_internal_.NDArrayCacheEntry.html
+++ b/docs/reference/api/typedoc/interfaces/_internal_.NDArrayCacheEntry.html
@@ -21,7 +21,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">NDArrayCacheEntry</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L20">artifact_cache.ts:20</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L20">artifact_cache.ts:20</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
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<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -42,32 +42,32 @@
<h3 class="tsd-anchor-link"><span>byte<wbr/>Offset</span><a href="#byteOffset" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 [...]
<div class="tsd-signature"><span class="tsd-kind-property">byte<wbr/>Offset</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L25">artifact_cache.ts:25</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L25">artifact_cache.ts:25</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="dtype" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>dtype</span><a href="#dtype" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">dtype</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L23">artifact_cache.ts:23</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L23">artifact_cache.ts:23</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="format" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>format</span><a href="#format" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">format</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">"f32-to-bf16"</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">"raw"</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L24">artifact_cache.ts:24</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L24">artifact_cache.ts:24</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="name" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>name</span><a href="#name" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">name</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L21">artifact_cache.ts:21</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L21">artifact_cache.ts:21</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="nbytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>nbytes</span><a href="#nbytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">nbytes</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L26">artifact_cache.ts:26</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L26">artifact_cache.ts:26</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="shape" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>shape</span><a href="#shape" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">shape</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L22">artifact_cache.ts:22</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L22">artifact_cache.ts:22</a></li></ul></aside></section></section></div>
<div class="col-sidebar">
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<div class="tsd-navigation settings">
diff --git a/docs/reference/api/typedoc/interfaces/_internal_.NDArrayShardEntry.html b/docs/reference/api/typedoc/interfaces/_internal_.NDArrayShardEntry.html
index 9514634be5..454f55d336 100644
--- a/docs/reference/api/typedoc/interfaces/_internal_.NDArrayShardEntry.html
+++ b/docs/reference/api/typedoc/interfaces/_internal_.NDArrayShardEntry.html
@@ -21,7 +21,7 @@
<ul class="tsd-hierarchy">
<li><span class="target">NDArrayShardEntry</span></li></ul></section><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L29">artifact_cache.ts:29</a></li></ul></aside>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L29">artifact_cache.ts:29</a></li></ul></aside>
<section class="tsd-panel-group tsd-index-group">
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<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
@@ -40,22 +40,22 @@
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<div class="tsd-signature"><span class="tsd-kind-property">data<wbr/>Path</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L30">artifact_cache.ts:30</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L30">artifact_cache.ts:30</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="format" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>format</span><a href="#format" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">format</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">"raw-shard"</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L31">artifact_cache.ts:31</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L31">artifact_cache.ts:31</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="nbytes" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>nbytes</span><a href="#nbytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">nbytes</span><span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L32">artifact_cache.ts:32</a></li></ul></aside></section>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L32">artifact_cache.ts:32</a></li></ul></aside></section>
<section class="tsd-panel tsd-member"><a id="records" class="tsd-anchor"></a>
<h3 class="tsd-anchor-link"><span>records</span><a href="#records" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
<div class="tsd-signature"><span class="tsd-kind-property">records</span><span class="tsd-signature-symbol">:</span> <a href="_internal_.NDArrayCacheEntry.html" class="tsd-signature-type tsd-kind-interface">NDArrayCacheEntry</a><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/artifact_cache.ts#L33">artifact_cache.ts:33</a></li></ul></aside></section></section></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/artifact_cache.ts#L33">artifact_cache.ts:33</a></li></ul></aside></section></section></div>
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diff --git a/docs/reference/api/typedoc/types/InitProgressCallback.html b/docs/reference/api/typedoc/types/InitProgressCallback.html
index 1ed9ac6d00..b093c652ac 100644
--- a/docs/reference/api/typedoc/types/InitProgressCallback.html
+++ b/docs/reference/api/typedoc/types/InitProgressCallback.html
@@ -30,7 +30,7 @@
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<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></li></ul></div><aside class="tsd-sources">
<ul>
-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L1008">runtime.ts:1008</a></li></ul></aside></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L1008">runtime.ts:1008</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/PackedFunc.html b/docs/reference/api/typedoc/types/PackedFunc.html
index f333b83e35..d83dca2e4b 100644
--- a/docs/reference/api/typedoc/types/PackedFunc.html
+++ b/docs/reference/api/typedoc/types/PackedFunc.html
@@ -19,7 +19,7 @@
<div class="tsd-comment tsd-typography"><p>Type for PackedFunc in the TVMRuntime.</p>
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-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L42">runtime.ts:42</a></li></ul></aside></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L42">runtime.ts:42</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.AsyncPackedFunc.html b/docs/reference/api/typedoc/types/_internal_.AsyncPackedFunc.html
index ece6d99581..bf2acb77ac 100644
--- a/docs/reference/api/typedoc/types/_internal_.AsyncPackedFunc.html
+++ b/docs/reference/api/typedoc/types/_internal_.AsyncPackedFunc.html
@@ -21,7 +21,7 @@
possibly may contain stack unwinding through Asynctify</p>
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-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L49">runtime.ts:49</a></li></ul></aside></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L49">runtime.ts:49</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.FObjectConstructor.html b/docs/reference/api/typedoc/types/_internal_.FObjectConstructor.html
index 3d1f8a954e..520ff692f2 100644
--- a/docs/reference/api/typedoc/types/_internal_.FObjectConstructor.html
+++ b/docs/reference/api/typedoc/types/_internal_.FObjectConstructor.html
@@ -37,7 +37,7 @@
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-<li>Defined in <a href="https://github.com/apache/tvm/blob/370ec6a2c/web/src/runtime.ts#L895">runtime.ts:895</a></li></ul></aside></div>
+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L895">runtime.ts:895</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.FTVMWasmAllocSpace.html b/docs/reference/api/typedoc/types/_internal_.FTVMWasmAllocSpace.html
index 4400f8ba39..e1acd57882 100644
--- a/docs/reference/api/typedoc/types/_internal_.FTVMWasmAllocSpace.html
+++ b/docs/reference/api/typedoc/types/_internal_.FTVMWasmAllocSpace.html
@@ -33,7 +33,7 @@
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/ctypes.ts#L194">ctypes.ts:194</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.FTVMWasmFreeSpace.html b/docs/reference/api/typedoc/types/_internal_.FTVMWasmFreeSpace.html
index 30ee42be27..6c5106aeb0 100644
--- a/docs/reference/api/typedoc/types/_internal_.FTVMWasmFreeSpace.html
+++ b/docs/reference/api/typedoc/types/_internal_.FTVMWasmFreeSpace.html
@@ -33,7 +33,7 @@
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/ctypes.ts#L197">ctypes.ts:197</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.FTVMWasmPackedCFunc.html b/docs/reference/api/typedoc/types/_internal_.FTVMWasmPackedCFunc.html
index 9a083ecfea..9bef60a3d0 100644
--- a/docs/reference/api/typedoc/types/_internal_.FTVMWasmPackedCFunc.html
+++ b/docs/reference/api/typedoc/types/_internal_.FTVMWasmPackedCFunc.html
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/ctypes.ts#L206">ctypes.ts:206</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.Pointer.html b/docs/reference/api/typedoc/types/_internal_.Pointer.html
index 4d0e0a35fd..0b5065d04e 100644
--- a/docs/reference/api/typedoc/types/_internal_.Pointer.html
+++ b/docs/reference/api/typedoc/types/_internal_.Pointer.html
@@ -20,7 +20,7 @@
<div class="tsd-comment tsd-typography"><p>A pointer to points to the raw address space.</p>
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/ctypes.ts#L25">ctypes.ts:25</a></li></ul></aside></div>
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diff --git a/docs/reference/api/typedoc/types/_internal_.TVMObjectBase.html b/docs/reference/api/typedoc/types/_internal_.TVMObjectBase.html
index 6e3b959fcd..fadf343d93 100644
--- a/docs/reference/api/typedoc/types/_internal_.TVMObjectBase.html
+++ b/docs/reference/api/typedoc/types/_internal_.TVMObjectBase.html
@@ -20,7 +20,7 @@
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+<li>Defined in <a href="https://github.com/apache/tvm/blob/4cfd1817f/web/src/runtime.ts#L898">runtime.ts:898</a></li></ul></aside></div>
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diff --git a/docs/searchindex.js b/docs/searchindex.js
index 8b438d88aa..281df2c070 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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+Search.setIndex({docnames:["arch/debugger","arch/device_target_interactions","arch/index","arch/introduction_to_module_serialization","arch/pass_infra","arch/runtime","arch/runtimes/vulkan","contribute/ci","contribute/code_guide","contribute/code_review","contribute/committer_guide","contribute/community","contribute/document","contribute/error_handling","contribute/git_howto","contribute/index","contribute/pull_request","contribute/release_process","deep_dive/relax/abstraction","deep_di [...]
\ No newline at end of file
diff --git a/docs/topic/vta/tutorials/frontend/deploy_detection.html b/docs/topic/vta/tutorials/frontend/deploy_detection.html
index 54298e44f9..74eb552664 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_detection.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_detection.html
@@ -640,7 +640,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
warnings.warn(
-yolov3-tiny inference graph built in 29.99s!
+yolov3-tiny inference graph built in 27.65s!
</pre></div>
</div>
</div>
@@ -725,7 +725,7 @@ Download test image</p>
alu_counter : 849056
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 6.960 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 3.529 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-topic-vta-tutorials-frontend-deploy-detection-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../../../_downloads/65b9451c8de050d7cd9da2fe5a49acc6/deploy_detection.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_detection.py</span></code></a></p>
diff --git a/docs/topic/vta/tutorials/frontend/sg_execution_times.html b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
index c6293b3d29..31a0ddd32f 100644
--- a/docs/topic/vta/tutorials/frontend/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-frontend-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>01:06.960</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
+<p><strong>01:03.529</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -390,7 +390,7 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="deploy_detection.html#sphx-glr-topic-vta-tutorials-frontend-deploy-detection-py"><span class="std std-ref">Deploy Pretrained Vision Detection Model from Darknet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_detection.py</span></code>)</p></td>
-<td><p>01:06.960</p></td>
+<td><p>01:03.529</p></td>
<td><p>0.0 MB</p></td>
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index 5c61842dca..c83e4a97bb 100644
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@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-optimize-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.354</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
+<p><strong>00:03.200</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
<table class="docutils align-default">
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<col style="width: 84%" />
@@ -390,11 +390,11 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="convolution_opt.html#sphx-glr-topic-vta-tutorials-optimize-convolution-opt-py"><span class="std std-ref">2D Convolution Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">convolution_opt.py</span></code>)</p></td>
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+<td><p>00:02.718</p></td>
<td><p>0.0 MB</p></td>
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+<td><p>00:00.481</p></td>
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index 12ad6f3d73..88c2f8bcf6 100644
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+++ b/docs/topic/vta/tutorials/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-topic-vta-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.855</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
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<table class="docutils align-default">
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@@ -390,11 +390,11 @@
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<tr class="row-odd"><td><p><a class="reference internal" href="matrix_multiply.html#sphx-glr-topic-vta-tutorials-matrix-multiply-py"><span class="std std-ref">Simple Matrix Multiply</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply.py</span></code>)</p></td>
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diff --git a/docs/tutorial/auto_scheduler_matmul_x86.html b/docs/tutorial/auto_scheduler_matmul_x86.html
index fed69384ed..046a4dac19 100644
--- a/docs/tutorial/auto_scheduler_matmul_x86.html
+++ b/docs/tutorial/auto_scheduler_matmul_x86.html
@@ -611,7 +611,7 @@ class Module:
<span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 92.744 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 90.997 ms
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</div>
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@@ -683,7 +683,7 @@ automatically optimize a matrix multiplication, without the need to specify a
search template. It ends a series of examples that starts from the Tensor
Expression (TE) language that demonstrates how TVM can optimize computational
operations.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 43.786 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 45.074 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-auto-scheduler-matmul-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">auto_scheduler_matmul_x86.py</span></code></a></p>
diff --git a/docs/tutorial/autotvm_matmul_x86.html b/docs/tutorial/autotvm_matmul_x86.html
index 1defc8ee34..026a8131aa 100644
--- a/docs/tutorial/autotvm_matmul_x86.html
+++ b/docs/tutorial/autotvm_matmul_x86.html
@@ -719,16 +719,16 @@ reduce variance, we take 5 measurements and average them.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>waiting for device...
device available
Get devices for measurement successfully!
-No: 1 GFLOPS: 11.26/11.26 result: MeasureResult(costs=(0.0238396768,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7623159885406494, timestamp=1731564136.839757) [('tile_y', [-1, 128]), ('tile_x', [-1, 256])],None,87
-No: 2 GFLOPS: 11.34/11.34 result: MeasureResult(costs=(0.023662184,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.74289870262146, timestamp=1731564137.4822106) [('tile_y', [-1, 64]), ('tile_x', [-1, 256])],None,86
-No: 3 GFLOPS: 7.35/11.34 result: MeasureResult(costs=(0.0365266352,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9317197799682617, timestamp=1731564138.3303134) [('tile_y', [-1, 4]), ('tile_x', [-1, 8])],None,32
-No: 4 GFLOPS: 2.02/11.34 result: MeasureResult(costs=(0.13290490279999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=3.456521511077881, timestamp=1731564140.7214527) [('tile_y', [-1, 64]), ('tile_x', [-1, 4])],None,26
-No: 5 GFLOPS: 11.90/11.90 result: MeasureResult(costs=(0.0225549626,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6778995990753174, timestamp=1731564141.5119245) [('tile_y', [-1, 8]), ('tile_x', [-1, 256])],None,83
-No: 6 GFLOPS: 2.02/11.90 result: MeasureResult(costs=(0.1330223358,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.4114341735839844, timestamp=1731564143.9009666) [('tile_y', [-1, 256]), ('tile_x', [-1, 4])],None,28
-No: 7 GFLOPS: 4.93/11.90 result: MeasureResult(costs=(0.054451531799999994,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.1396219730377197, timestamp=1731564145.0366194) [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
-No: 8 GFLOPS: 11.33/11.90 result: MeasureResult(costs=(0.0236997724,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6722221374511719, timestamp=1731564145.6777785) [('tile_y', [-1, 128]), ('tile_x', [-1, 32])],None,57
-No: 9 GFLOPS: 3.09/11.90 result: MeasureResult(costs=(0.08696859679999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6241281032562256, timestamp=1731564147.4439936) [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
-No: 10 GFLOPS: 11.94/11.94 result: MeasureResult(costs=(0.0224819448,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6275911331176758, timestamp=1731564148.0706666) [('tile_y', [-1, 16]), ('tile_x', [-1, 512])],None,94
+No: 1 GFLOPS: 15.24/15.24 result: MeasureResult(costs=(0.0176096938,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.960632085800171, timestamp=1731598401.6571627) [('tile_y', [-1, 64]), ('tile_x', [-1, 64])],None,66
+No: 2 GFLOPS: 7.81/15.24 result: MeasureResult(costs=(0.034386528,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.895888328552246, timestamp=1731598402.4670277) [('tile_y', [-1, 1]), ('tile_x', [-1, 8])],None,30
+No: 3 GFLOPS: 10.76/15.24 result: MeasureResult(costs=(0.024942435000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9009666442871094, timestamp=1731598403.1309404) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
+No: 4 GFLOPS: 10.96/15.24 result: MeasureResult(costs=(0.024503013799999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.7547705173492432, timestamp=1731598403.7815816) [('tile_y', [-1, 8]), ('tile_x', [-1, 64])],None,63
+No: 5 GFLOPS: 4.04/15.24 result: MeasureResult(costs=(0.066424105,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.3502404689788818, timestamp=1731598405.26695) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+No: 6 GFLOPS: 12.39/15.24 result: MeasureResult(costs=(0.0216712034,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6343398094177246, timestamp=1731598405.8801553) [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+No: 7 GFLOPS: 14.04/15.24 result: MeasureResult(costs=(0.019116380199999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6089017391204834, timestamp=1731598406.4480417) [('tile_y', [-1, 8]), ('tile_x', [-1, 256])],None,83
+No: 8 GFLOPS: 2.96/15.24 result: MeasureResult(costs=(0.0907385344,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.728644609451294, timestamp=1731598408.158339) [('tile_y', [-1, 16]), ('tile_x', [-1, 2])],None,14
+No: 9 GFLOPS: 9.06/15.24 result: MeasureResult(costs=(0.0296335848,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.744819164276123, timestamp=1731598409.0193412) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+No: 10 GFLOPS: 13.11/15.24 result: MeasureResult(costs=(0.020468879000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6116936206817627, timestamp=1731598409.6105006) [('tile_y', [-1, 4]), ('tile_x', [-1, 256])],None,82
</pre></div>
</div>
<p>With tuning completed, we can choose the configuration from the log file that
diff --git a/docs/tutorial/autotvm_relay_x86.html b/docs/tutorial/autotvm_relay_x86.html
index a5b6fb5acc..905f667875 100644
--- a/docs/tutorial/autotvm_relay_x86.html
+++ b/docs/tutorial/autotvm_relay_x86.html
@@ -597,7 +597,7 @@ standard deviation.</p>
<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">unoptimized</span></a><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 497.4373029200001, 'median': 498.5680613499994, 'std': 2.3229567310236985}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'mean': 467.6736101299912, 'median': 467.7150531000052, 'std': 0.7902913979128545}
</pre></div>
</div>
</div>
@@ -786,179 +786,178 @@ depending on the specifics of the model and the target platform.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 1/25] Current/Best: 9.32/ 20.40 GFLOPS | Progress: (4/20) | 15.05 s
-[Task 1/25] Current/Best: 17.09/ 20.40 GFLOPS | Progress: (8/20) | 17.66 s
-[Task 1/25] Current/Best: 9.36/ 21.69 GFLOPS | Progress: (12/20) | 25.62 s
-[Task 1/25] Current/Best: 13.07/ 21.69 GFLOPS | Progress: (16/20) | 29.01 s
-[Task 1/25] Current/Best: 3.33/ 21.69 GFLOPS | Progress: (20/20) | 32.93 s Done.
+[Task 1/25] Current/Best: 7.22/ 16.70 GFLOPS | Progress: (4/20) | 9.94 s
+[Task 1/25] Current/Best: 6.66/ 18.57 GFLOPS | Progress: (8/20) | 13.07 s
+[Task 1/25] Current/Best: 21.80/ 21.80 GFLOPS | Progress: (12/20) | 15.24 s
+[Task 1/25] Current/Best: 7.29/ 21.90 GFLOPS | Progress: (16/20) | 17.82 s
+[Task 1/25] Current/Best: 15.79/ 21.90 GFLOPS | Progress: (20/20) | 20.54 s Done.
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 2/25] Current/Best: 15.34/ 15.34 GFLOPS | Progress: (4/20) | 4.49 s
-[Task 2/25] Current/Best: 12.05/ 15.34 GFLOPS | Progress: (8/20) | 7.30 s
-[Task 2/25] Current/Best: 6.92/ 15.34 GFLOPS | Progress: (12/20) | 8.76 s
-[Task 2/25] Current/Best: 7.24/ 18.03 GFLOPS | Progress: (16/20) | 10.14 s
-[Task 2/25] Current/Best: 5.43/ 18.03 GFLOPS | Progress: (20/20) | 11.96 s Done.
+[Task 2/25] Current/Best: 9.01/ 19.35 GFLOPS | Progress: (4/20) | 3.87 s
+[Task 2/25] Current/Best: 2.59/ 20.71 GFLOPS | Progress: (8/20) | 5.85 s
+[Task 2/25] Current/Best: 20.73/ 20.87 GFLOPS | Progress: (12/20) | 7.17 s
+[Task 2/25] Current/Best: 3.92/ 20.87 GFLOPS | Progress: (16/20) | 8.85 s
+[Task 2/25] Current/Best: 7.67/ 20.87 GFLOPS | Progress: (20/20) | 10.68 s Done.
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 3/25] Current/Best: 9.47/ 21.43 GFLOPS | Progress: (4/20) | 4.64 s
-[Task 3/25] Current/Best: 13.07/ 21.43 GFLOPS | Progress: (8/20) | 6.97 s
-[Task 3/25] Current/Best: 20.18/ 21.43 GFLOPS | Progress: (12/20) | 9.21 s
-[Task 3/25] Current/Best: 15.86/ 21.62 GFLOPS | Progress: (16/20) | 12.81 s
-[Task 3/25] Current/Best: 8.14/ 21.62 GFLOPS | Progress: (20/20) | 15.50 s Done.
+[Task 3/25] Current/Best: 15.09/ 19.11 GFLOPS | Progress: (4/20) | 4.66 s
+[Task 3/25] Current/Best: 10.08/ 20.65 GFLOPS | Progress: (8/20) | 7.01 s
+[Task 3/25] Current/Best: 15.13/ 20.65 GFLOPS | Progress: (12/20) | 10.27 s
+[Task 3/25] Current/Best: 15.50/ 23.48 GFLOPS | Progress: (16/20) | 12.42 s
+[Task 3/25] Current/Best: 24.92/ 24.92 GFLOPS | Progress: (20/20) | 14.42 s Done.
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 4/25] Current/Best: 13.08/ 13.73 GFLOPS | Progress: (4/20) | 5.73 s
-[Task 4/25] Current/Best: 15.98/ 15.98 GFLOPS | Progress: (8/20) | 7.81 s
-[Task 4/25] Current/Best: 9.33/ 15.98 GFLOPS | Progress: (12/20) | 13.54 s
-[Task 4/25] Current/Best: 3.33/ 18.59 GFLOPS | Progress: (16/20) | 15.63 s
-[Task 4/25] Current/Best: 16.81/ 18.59 GFLOPS | Progress: (20/20) | 17.52 s Done.
+[Task 4/25] Current/Best: 19.22/ 19.22 GFLOPS | Progress: (4/20) | 4.34 s
+[Task 4/25] Current/Best: 7.02/ 20.98 GFLOPS | Progress: (8/20) | 6.24 s
+[Task 4/25] Current/Best: 14.92/ 22.88 GFLOPS | Progress: (12/20) | 8.96 s
+[Task 4/25] Current/Best: 21.30/ 22.88 GFLOPS | Progress: (16/20) | 10.90 s
+[Task 4/25] Current/Best: 22.54/ 22.88 GFLOPS | Progress: (20/20) | 12.51 s Done.
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 5/25] Current/Best: 7.92/ 15.13 GFLOPS | Progress: (4/20) | 4.55 s
-[Task 5/25] Current/Best: 4.18/ 17.79 GFLOPS | Progress: (8/20) | 6.64 s
-[Task 5/25] Current/Best: 17.32/ 17.79 GFLOPS | Progress: (12/20) | 8.74 s
-[Task 5/25] Current/Best: 11.92/ 20.12 GFLOPS | Progress: (16/20) | 11.84 s
-[Task 5/25] Current/Best: 16.68/ 20.12 GFLOPS | Progress: (20/20) | 13.74 s Done.
+[Task 5/25] Current/Best: 18.17/ 18.17 GFLOPS | Progress: (4/20) | 4.28 s
+[Task 5/25] Current/Best: 14.88/ 18.17 GFLOPS | Progress: (8/20) | 6.20 s
+[Task 5/25] Current/Best: 11.13/ 18.17 GFLOPS | Progress: (12/20) | 8.95 s
+[Task 5/25] Current/Best: 11.15/ 18.17 GFLOPS | Progress: (16/20) | 11.16 s
+[Task 5/25] Current/Best: 5.66/ 19.19 GFLOPS | Progress: (20/20) | 13.16 s Done.
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 6/25] Current/Best: 14.26/ 14.26 GFLOPS | Progress: (4/20) | 5.18 s
-[Task 6/25] Current/Best: 12.57/ 18.34 GFLOPS | Progress: (8/20) | 7.87 s
-[Task 6/25] Current/Best: 10.08/ 18.51 GFLOPS | Progress: (12/20) | 11.06 s
-[Task 6/25] Current/Best: 17.69/ 21.26 GFLOPS | Progress: (16/20) | 13.04 s
-[Task 6/25] Current/Best: 12.45/ 21.26 GFLOPS | Progress: (20/20) | 15.42 s Done.
+[Task 6/25] Current/Best: 13.53/ 15.76 GFLOPS | Progress: (4/20) | 5.85 s
+[Task 6/25] Current/Best: 12.31/ 15.76 GFLOPS | Progress: (8/20) | 9.69 s
+[Task 6/25] Current/Best: 6.59/ 16.98 GFLOPS | Progress: (12/20) | 12.68 s
+[Task 6/25] Current/Best: 14.47/ 16.98 GFLOPS | Progress: (16/20) | 17.14 s
+[Task 6/25] Current/Best: 19.86/ 19.86 GFLOPS | Progress: (20/20) | 20.39 s Done.
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 7/25] Current/Best: 19.36/ 19.36 GFLOPS | Progress: (4/20) | 5.01 s
-[Task 7/25] Current/Best: 1.59/ 19.36 GFLOPS | Progress: (8/20) | 9.17 s
-[Task 7/25] Current/Best: 21.16/ 21.16 GFLOPS | Progress: (12/20) | 11.80 s
-[Task 7/25] Current/Best: 13.42/ 21.16 GFLOPS | Progress: (16/20) | 14.94 s
-[Task 7/25] Current/Best: 8.42/ 21.16 GFLOPS | Progress: (20/20) | 17.48 s Done.
+[Task 7/25] Current/Best: 20.91/ 20.91 GFLOPS | Progress: (4/20) | 5.17 s
+[Task 7/25] Current/Best: 3.19/ 20.91 GFLOPS | Progress: (8/20) | 8.75 s
+[Task 7/25] Current/Best: 18.01/ 20.91 GFLOPS | Progress: (12/20) | 10.76 s
+[Task 7/25] Current/Best: 10.93/ 20.91 GFLOPS | Progress: (16/20) | 13.26 s
+[Task 7/25] Current/Best: 10.78/ 20.91 GFLOPS | Progress: (20/20) | 15.44 s Done.
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 8/25] Current/Best: 11.79/ 12.55 GFLOPS | Progress: (4/20) | 9.52 s
-[Task 8/25] Current/Best: 8.93/ 12.87 GFLOPS | Progress: (8/20) | 13.47 s
-[Task 8/25] Current/Best: 5.41/ 13.77 GFLOPS | Progress: (12/20) | 16.74 s
-[Task 8/25] Current/Best: 8.74/ 13.77 GFLOPS | Progress: (16/20) | 25.53 s
-[Task 8/25] Current/Best: 5.03/ 19.14 GFLOPS | Progress: (20/20) | 28.24 s Done.
+[Task 8/25] Current/Best: 11.17/ 15.97 GFLOPS | Progress: (4/20) | 4.76 s
+[Task 8/25] Current/Best: 2.56/ 15.97 GFLOPS | Progress: (8/20) | 7.95 s
+[Task 8/25] Current/Best: 12.79/ 20.68 GFLOPS | Progress: (12/20) | 11.75 s
+[Task 8/25] Current/Best: 13.80/ 20.68 GFLOPS | Progress: (16/20) | 17.48 s
+[Task 8/25] Current/Best: 5.22/ 20.68 GFLOPS | Progress: (20/20) | 22.34 s Done.
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 9/25] Current/Best: 18.38/ 18.38 GFLOPS | Progress: (4/20) | 4.69 s
-[Task 9/25] Current/Best: 13.95/ 18.38 GFLOPS | Progress: (8/20) | 9.85 s
-[Task 9/25] Current/Best: 13.88/ 18.38 GFLOPS | Progress: (12/20) | 17.89 s
-[Task 9/25] Current/Best: 18.52/ 18.52 GFLOPS | Progress: (16/20) | 21.91 s
-[Task 9/25] Current/Best: 17.10/ 19.86 GFLOPS | Progress: (20/20) | 26.00 s Done.
+[Task 9/25] Current/Best: 9.98/ 16.95 GFLOPS | Progress: (4/20) | 4.59 s
+[Task 9/25] Current/Best: 9.91/ 21.16 GFLOPS | Progress: (8/20) | 15.95 s
+[Task 9/25] Current/Best: 16.32/ 21.16 GFLOPS | Progress: (12/20) | 19.30 s
+[Task 9/25] Current/Best: 6.69/ 21.16 GFLOPS | Progress: (16/20) | 26.35 s
+[Task 9/25] Current/Best: 13.53/ 22.36 GFLOPS | Progress: (20/20) | 28.55 s Done.
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 10/25] Current/Best: 13.95/ 16.35 GFLOPS | Progress: (4/20) | 4.50 s
-[Task 10/25] Current/Best: 5.37/ 16.35 GFLOPS | Progress: (8/20) | 6.61 s
-[Task 10/25] Current/Best: 11.63/ 16.35 GFLOPS | Progress: (12/20) | 9.01 s
-[Task 10/25] Current/Best: 16.23/ 16.35 GFLOPS | Progress: (16/20) | 11.00 s
-[Task 10/25] Current/Best: 13.31/ 16.35 GFLOPS | Progress: (20/20) | 13.21 s Done.
+[Task 10/25] Current/Best: 11.24/ 20.95 GFLOPS | Progress: (4/20) | 5.30 s
+[Task 10/25] Current/Best: 12.91/ 20.95 GFLOPS | Progress: (8/20) | 7.81 s
+[Task 10/25] Current/Best: 21.72/ 21.72 GFLOPS | Progress: (12/20) | 9.48 s
+[Task 10/25] Current/Best: 6.46/ 21.72 GFLOPS | Progress: (16/20) | 11.19 s
+[Task 10/25] Current/Best: 16.33/ 21.72 GFLOPS | Progress: (20/20) | 13.23 s Done.
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 11/25] Current/Best: 16.22/ 16.22 GFLOPS | Progress: (4/20) | 5.60 s
-[Task 11/25] Current/Best: 11.21/ 21.60 GFLOPS | Progress: (8/20) | 8.29 s
-[Task 11/25] Current/Best: 21.12/ 21.60 GFLOPS | Progress: (12/20) | 11.92 s
-[Task 11/25] Current/Best: 21.48/ 21.60 GFLOPS | Progress: (16/20) | 14.51 s
-[Task 11/25] Current/Best: 22.72/ 22.72 GFLOPS | Progress: (20/20) | 17.10 s Done.
+[Task 11/25] Current/Best: 14.14/ 21.75 GFLOPS | Progress: (4/20) | 5.55 s
+[Task 11/25] Current/Best: 12.79/ 21.75 GFLOPS | Progress: (8/20) | 9.28 s
+[Task 11/25] Current/Best: 11.67/ 21.75 GFLOPS | Progress: (12/20) | 11.69 s
+[Task 11/25] Current/Best: 14.04/ 21.75 GFLOPS | Progress: (16/20) | 14.05 s
+[Task 11/25] Current/Best: 13.61/ 24.58 GFLOPS | Progress: (20/20) | 17.18 s Done.
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 12/25] Current/Best: 11.27/ 17.14 GFLOPS | Progress: (4/20) | 5.64 s
-[Task 12/25] Current/Best: 14.03/ 17.14 GFLOPS | Progress: (8/20) | 9.50 s
-[Task 12/25] Current/Best: 12.33/ 17.14 GFLOPS | Progress: (12/20) | 11.52 s
-[Task 12/25] Current/Best: 15.93/ 17.14 GFLOPS | Progress: (16/20) | 16.39 s
-[Task 12/25] Current/Best: 12.83/ 17.14 GFLOPS | Progress: (20/20) | 19.44 s Done.
+[Task 12/25] Current/Best: 10.19/ 16.49 GFLOPS | Progress: (4/20) | 8.62 s
+[Task 12/25] Current/Best: 3.18/ 18.37 GFLOPS | Progress: (8/20) | 11.30 s
+[Task 12/25] Current/Best: 11.46/ 18.37 GFLOPS | Progress: (12/20) | 15.46 s
+[Task 12/25] Current/Best: 15.16/ 18.37 GFLOPS | Progress: (16/20) | 18.65 s
+[Task 12/25] Current/Best: 11.36/ 19.27 GFLOPS | Progress: (20/20) | 21.95 s Done.
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 13/25] Current/Best: 6.26/ 21.13 GFLOPS | Progress: (4/20) | 6.77 s
-[Task 13/25] Current/Best: 18.53/ 21.28 GFLOPS | Progress: (8/20) | 9.37 s
-[Task 13/25] Current/Best: 18.71/ 21.28 GFLOPS | Progress: (12/20) | 14.13 s
-[Task 13/25] Current/Best: 12.72/ 21.28 GFLOPS | Progress: (16/20) | 17.69 s
-[Task 13/25] Current/Best: 20.97/ 21.28 GFLOPS | Progress: (20/20) | 20.31 s Done.
+[Task 13/25] Current/Best: 20.85/ 21.46 GFLOPS | Progress: (4/20) | 4.42 s
+[Task 13/25] Current/Best: 19.04/ 21.46 GFLOPS | Progress: (8/20) | 7.47 s
+[Task 13/25] Current/Best: 13.24/ 21.46 GFLOPS | Progress: (12/20) | 10.70 s
+[Task 13/25] Current/Best: 13.23/ 21.46 GFLOPS | Progress: (16/20) | 13.46 s
+[Task 13/25] Current/Best: 10.28/ 21.46 GFLOPS | Progress: (20/20) | 16.62 s Done.
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 14/25] Current/Best: 7.24/ 18.00 GFLOPS | Progress: (4/20) | 13.24 s
-[Task 14/25] Current/Best: 4.30/ 18.00 GFLOPS | Progress: (8/20) | 21.91 s
-[Task 14/25] Current/Best: 3.91/ 21.57 GFLOPS | Progress: (12/20) | 30.92 s
-[Task 14/25] Current/Best: 16.23/ 21.57 GFLOPS | Progress: (16/20) | 36.46 s
-[Task 14/25] Current/Best: 13.68/ 21.57 GFLOPS | Progress: (20/20) | 47.84 s
+[Task 14/25] Current/Best: 14.88/ 20.58 GFLOPS | Progress: (4/20) | 10.51 s
+[Task 14/25] Current/Best: 16.48/ 20.58 GFLOPS | Progress: (8/20) | 13.97 s
+[Task 14/25] Current/Best: 7.59/ 20.58 GFLOPS | Progress: (12/20) | 25.67 s
+[Task 14/25] Current/Best: 18.45/ 20.58 GFLOPS | Progress: (16/20) | 29.78 s
+[Task 14/25] Current/Best: 12.64/ 21.98 GFLOPS | Progress: (20/20) | 33.03 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 15/25] Current/Best: 6.33/ 18.76 GFLOPS | Progress: (4/20) | 5.96 s
-[Task 15/25] Current/Best: 8.47/ 20.78 GFLOPS | Progress: (8/20) | 12.24 s
-[Task 15/25] Current/Best: 6.42/ 20.78 GFLOPS | Progress: (12/20) | 19.31 s
-[Task 15/25] Current/Best: 13.45/ 22.73 GFLOPS | Progress: (16/20) | 25.01 s
-[Task 15/25] Current/Best: 1.72/ 22.73 GFLOPS | Progress: (20/20) | 27.29 s Done.
+[Task 15/25] Current/Best: 11.33/ 22.76 GFLOPS | Progress: (4/20) | 4.68 s
+[Task 15/25] Current/Best: 21.37/ 22.76 GFLOPS | Progress: (8/20) | 6.32 s
+[Task 15/25] Current/Best: 4.93/ 22.76 GFLOPS | Progress: (12/20) | 17.15 s
+[Task 15/25] Current/Best: 19.88/ 22.76 GFLOPS | Progress: (16/20) | 18.69 s
+[Task 15/25] Current/Best: 20.28/ 22.76 GFLOPS | Progress: (20/20) | 20.22 s Done.
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 16/25] Current/Best: 20.34/ 20.34 GFLOPS | Progress: (4/20) | 4.22 s
-[Task 16/25] Current/Best: 19.03/ 20.34 GFLOPS | Progress: (8/20) | 6.04 s
-[Task 16/25] Current/Best: 9.24/ 20.34 GFLOPS | Progress: (12/20) | 8.30 s
-[Task 16/25] Current/Best: 18.79/ 20.34 GFLOPS | Progress: (16/20) | 11.17 s
-[Task 16/25] Current/Best: 19.73/ 20.34 GFLOPS | Progress: (20/20) | 13.96 s Done.
+[Task 16/25] Current/Best: 9.02/ 11.06 GFLOPS | Progress: (4/20) | 6.28 s
+[Task 16/25] Current/Best: 11.26/ 19.18 GFLOPS | Progress: (8/20) | 7.87 s
+[Task 16/25] Current/Best: 20.37/ 20.37 GFLOPS | Progress: (12/20) | 9.99 s
+[Task 16/25] Current/Best: 5.44/ 20.37 GFLOPS | Progress: (16/20) | 12.51 s
+[Task 16/25] Current/Best: 6.97/ 20.37 GFLOPS | Progress: (20/20) | 14.72 s Done.
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 17/25] Current/Best: 19.86/ 21.48 GFLOPS | Progress: (4/20) | 4.94 s
-[Task 17/25] Current/Best: 16.40/ 21.48 GFLOPS | Progress: (8/20) | 7.58 s
-[Task 17/25] Current/Best: 9.68/ 21.48 GFLOPS | Progress: (12/20) | 10.72 s
-[Task 17/25] Current/Best: 9.08/ 22.49 GFLOPS | Progress: (16/20) | 13.07 s
-[Task 17/25] Current/Best: 10.35/ 22.49 GFLOPS | Progress: (20/20) | 15.86 s Done.
+[Task 17/25] Current/Best: 18.49/ 21.55 GFLOPS | Progress: (4/20) | 5.74 s
+[Task 17/25] Current/Best: 21.09/ 24.13 GFLOPS | Progress: (8/20) | 8.82 s
+[Task 17/25] Current/Best: 10.57/ 24.13 GFLOPS | Progress: (12/20) | 13.05 s
+[Task 17/25] Current/Best: 12.47/ 24.13 GFLOPS | Progress: (16/20) | 15.45 s
+[Task 17/25] Current/Best: 17.51/ 24.13 GFLOPS | Progress: (20/20) | 18.81 s Done.
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 18/25] Current/Best: 14.16/ 20.18 GFLOPS | Progress: (4/20) | 5.23 s
-[Task 18/25] Current/Best: 7.07/ 20.93 GFLOPS | Progress: (8/20) | 10.68 s
-[Task 18/25] Current/Best: 17.90/ 20.93 GFLOPS | Progress: (12/20) | 16.72 s
-[Task 18/25] Current/Best: 17.19/ 20.93 GFLOPS | Progress: (16/20) | 20.93 s
-[Task 18/25] Current/Best: 14.42/ 22.33 GFLOPS | Progress: (20/20) | 23.11 s Done.
+[Task 18/25] Current/Best: 6.81/ 15.95 GFLOPS | Progress: (4/20) | 4.76 s
+[Task 18/25] Current/Best: 12.53/ 18.09 GFLOPS | Progress: (8/20) | 8.17 s
+[Task 18/25] Current/Best: 17.50/ 18.09 GFLOPS | Progress: (12/20) | 12.43 s
+[Task 18/25] Current/Best: 11.84/ 20.27 GFLOPS | Progress: (16/20) | 17.92 s
+[Task 18/25] Current/Best: 14.21/ 20.27 GFLOPS | Progress: (20/20) | 20.16 s Done.
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 19/25] Current/Best: 10.60/ 10.60 GFLOPS | Progress: (4/20) | 6.79 s
-[Task 19/25] Current/Best: 6.15/ 10.60 GFLOPS | Progress: (8/20) | 11.40 s
-[Task 19/25] Current/Best: 21.46/ 21.46 GFLOPS | Progress: (12/20) | 14.64 s
-[Task 19/25] Current/Best: 9.79/ 21.46 GFLOPS | Progress: (16/20) | 18.28 s
-[Task 19/25] Current/Best: 13.06/ 21.46 GFLOPS | Progress: (20/20) | 21.56 s Done.
+[Task 19/25] Current/Best: 12.14/ 12.14 GFLOPS | Progress: (4/20) | 6.65 s
+[Task 19/25] Current/Best: 17.66/ 17.66 GFLOPS | Progress: (8/20) | 11.05 s
+[Task 19/25] Current/Best: 9.37/ 20.79 GFLOPS | Progress: (12/20) | 14.54 s
+[Task 19/25] Current/Best: 16.67/ 20.79 GFLOPS | Progress: (16/20) | 18.07 s
+[Task 19/25] Current/Best: 12.38/ 20.79 GFLOPS | Progress: (20/20) | 21.07 s Done.
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 20/25] Current/Best: 11.06/ 16.42 GFLOPS | Progress: (4/20) | 5.39 s
-[Task 20/25] Current/Best: 5.03/ 16.42 GFLOPS | Progress: (8/20) | 17.18 s
-[Task 20/25] Current/Best: 15.52/ 16.42 GFLOPS | Progress: (12/20) | 25.14 s
-[Task 20/25] Current/Best: 4.69/ 17.74 GFLOPS | Progress: (16/20) | 29.54 s
-[Task 20/25] Current/Best: 1.58/ 17.74 GFLOPS | Progress: (20/20) | 33.59 s
-[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 21/25] Current/Best: 13.42/ 16.26 GFLOPS | Progress: (4/20) | 6.86 s Done.
+[Task 20/25] Current/Best: 18.88/ 23.94 GFLOPS | Progress: (4/20) | 4.48 s
+[Task 20/25] Current/Best: 10.16/ 23.94 GFLOPS | Progress: (8/20) | 16.15 s
+[Task 20/25] Current/Best: 8.21/ 23.94 GFLOPS | Progress: (12/20) | 28.00 s
+[Task 20/25] Current/Best: 12.54/ 23.94 GFLOPS | Progress: (16/20) | 34.37 s
+[Task 20/25] Current/Best: 19.65/ 23.94 GFLOPS | Progress: (20/20) | 37.24 s
+[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s Done.
Done.
-[Task 21/25] Current/Best: 19.96/ 19.96 GFLOPS | Progress: (8/20) | 16.16 s
-[Task 21/25] Current/Best: 13.46/ 19.96 GFLOPS | Progress: (12/20) | 19.11 s
-[Task 21/25] Current/Best: 6.86/ 19.96 GFLOPS | Progress: (16/20) | 26.46 s
-[Task 21/25] Current/Best: 5.20/ 19.96 GFLOPS | Progress: (20/20) | 33.45 s Done.
-
+[Task 21/25] Current/Best: 8.72/ 8.72 GFLOPS | Progress: (4/20) | 13.83 s
+[Task 21/25] Current/Best: 16.93/ 16.93 GFLOPS | Progress: (8/20) | 25.00 s
+[Task 21/25] Current/Best: 18.86/ 18.86 GFLOPS | Progress: (12/20) | 29.16 s
+[Task 21/25] Current/Best: 10.14/ 18.86 GFLOPS | Progress: (16/20) | 34.30 s
+[Task 21/25] Current/Best: 23.54/ 23.54 GFLOPS | Progress: (20/20) | 45.38 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 22/25] Current/Best: 16.18/ 16.18 GFLOPS | Progress: (4/20) | 4.74 s
-[Task 22/25] Current/Best: 19.13/ 19.13 GFLOPS | Progress: (8/20) | 6.36 s
-[Task 22/25] Current/Best: 16.93/ 19.13 GFLOPS | Progress: (12/20) | 8.79 s
-[Task 22/25] Current/Best: 17.40/ 19.13 GFLOPS | Progress: (16/20) | 10.66 s
-[Task 22/25] Current/Best: 10.53/ 20.71 GFLOPS | Progress: (20/20) | 14.15 s Done.
+[Task 22/25] Current/Best: 14.70/ 20.56 GFLOPS | Progress: (4/20) | 5.86 s
+[Task 22/25] Current/Best: 22.56/ 22.56 GFLOPS | Progress: (8/20) | 8.95 s
+[Task 22/25] Current/Best: 8.17/ 22.56 GFLOPS | Progress: (12/20) | 11.50 s
+[Task 22/25] Current/Best: 9.71/ 22.56 GFLOPS | Progress: (16/20) | 13.41 s
+[Task 22/25] Current/Best: 19.93/ 22.56 GFLOPS | Progress: (20/20) | 15.41 s Done.
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 23/25] Current/Best: 12.86/ 19.77 GFLOPS | Progress: (4/20) | 5.39 s
-[Task 23/25] Current/Best: 11.78/ 19.77 GFLOPS | Progress: (8/20) | 9.86 s
-[Task 23/25] Current/Best: 11.77/ 19.77 GFLOPS | Progress: (12/20) | 12.91 s
-[Task 23/25] Current/Best: 22.46/ 22.46 GFLOPS | Progress: (16/20) | 16.38 s
-[Task 23/25] Current/Best: 23.21/ 23.21 GFLOPS | Progress: (20/20) | 20.74 s Done.
+[Task 23/25] Current/Best: 23.48/ 23.48 GFLOPS | Progress: (4/20) | 5.96 s
+[Task 23/25] Current/Best: 9.98/ 23.48 GFLOPS | Progress: (8/20) | 8.93 s
+[Task 23/25] Current/Best: 1.60/ 23.48 GFLOPS | Progress: (12/20) | 12.96 s
+[Task 23/25] Current/Best: 21.60/ 23.48 GFLOPS | Progress: (16/20) | 16.06 s
+[Task 23/25] Current/Best: 20.45/ 23.48 GFLOPS | Progress: (20/20) | 18.91 s Done.
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 24/25] Current/Best: 5.38/ 10.19 GFLOPS | Progress: (4/20) | 13.53 s
-[Task 24/25] Current/Best: 3.40/ 10.19 GFLOPS | Progress: (8/20) | 25.99 s
-[Task 24/25] Current/Best: 3.26/ 10.19 GFLOPS | Progress: (12/20) | 38.21 s
-[Task 24/25] Current/Best: 3.19/ 10.19 GFLOPS | Progress: (16/20) | 50.74 s
-[Task 24/25] Current/Best: 5.54/ 10.19 GFLOPS | Progress: (20/20) | 54.82 s
+[Task 24/25] Current/Best: 10.49/ 10.49 GFLOPS | Progress: (4/20) | 13.07 s
+[Task 24/25] Current/Best: 2.87/ 10.49 GFLOPS | Progress: (8/20) | 24.08 s
+[Task 24/25] Current/Best: 3.08/ 10.49 GFLOPS | Progress: (12/20) | 27.85 s
+[Task 24/25] Current/Best: 7.88/ 10.49 GFLOPS | Progress: (16/20) | 33.98 s
+[Task 24/25] Current/Best: 2.20/ 10.49 GFLOPS | Progress: (20/20) | 44.73 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
-[Task 25/25] Current/Best: 5.63/ 8.49 GFLOPS | Progress: (4/20) | 13.26 s Done.
+[Task 25/25] Current/Best: 5.72/ 9.23 GFLOPS | Progress: (4/20) | 15.14 s
+[Task 25/25] Current/Best: 1.60/ 9.23 GFLOPS | Progress: (8/20) | 19.90 s Done.
-[Task 25/25] Current/Best: 5.56/ 8.84 GFLOPS | Progress: (8/20) | 16.87 s
-[Task 25/25] Current/Best: 1.48/ 8.84 GFLOPS | Progress: (12/20) | 19.05 s
-[Task 25/25] Current/Best: 5.37/ 9.18 GFLOPS | Progress: (16/20) | 30.03 s
-[Task 25/25] Current/Best: 1.55/ 9.18 GFLOPS | Progress: (20/20) | 33.30 s
+[Task 25/25] Current/Best: 3.12/ 9.60 GFLOPS | Progress: (12/20) | 22.56 s
+[Task 25/25] Current/Best: 7.39/ 9.60 GFLOPS | Progress: (16/20) | 26.78 s
+[Task 25/25] Current/Best: 9.08/ 10.54 GFLOPS | Progress: (20/20) | 37.75 s
</pre></div>
</div>
<p>The output from this tuning process will look something like this:</p>
@@ -1006,6 +1005,7 @@ model using optimized operators to speed up our computations.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Done.
+Done.
</pre></div>
</div>
<p>Verify that the optimized model runs and produces the same results:</p>
@@ -1060,8 +1060,8 @@ improvement in comparing the optimized model to the unoptimized model.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">"unoptimized: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">unoptimized</span></a><span class="p">))</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 427.23773420999805, 'median': 426.2538619499992, 'std': 1.9056924415046534}
-unoptimized: {'mean': 497.4373029200001, 'median': 498.5680613499994, 'std': 2.3229567310236985}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {'mean': 368.6606970599951, 'median': 368.54574569997567, 'std': 0.44749195546819753}
+unoptimized: {'mean': 467.6736101299912, 'median': 467.7150531000052, 'std': 0.7902913979128545}
</pre></div>
</div>
</div>
@@ -1075,7 +1075,7 @@ models.</p>
<p>Here we presented a simple example using ResNet-50 v2 locally. However, TVM
supports many more features including cross-compilation, remote execution and
profiling/benchmarking.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 14 minutes 17.714 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 13 minutes 25.316 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-autotvm-relay-x86-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/57a45d9bef1af358191e7d50043e652c/autotvm_relay_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">autotvm_relay_x86.py</span></code></a></p>
diff --git a/docs/tutorial/intro_topi.html b/docs/tutorial/intro_topi.html
index ab1f12196f..1a71de1a6a 100644
--- a/docs/tutorial/intro_topi.html
+++ b/docs/tutorial/intro_topi.html
@@ -547,7 +547,7 @@ class Module:
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/ir.html#tvm.ir.Array" title="tvm.ir.Array" class="sphx-glr-backref-module-tvm-ir sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sg</span><span class="o">.</span><span class="n">stages</span></a><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x1b9c9a50)), stage(b, placeholder(b, 0xb88acf0)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs [...]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x208bbf10)), stage(b, placeholder(b, 0x28cbe600)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attr [...]
</pre></div>
</div>
<p>We can test the correctness by comparing with <code class="code docutils literal notranslate"><span class="pre">numpy</span></code> result as follows</p>
diff --git a/docs/tutorial/sg_execution_times.html b/docs/tutorial/sg_execution_times.html
index bf909d7a51..0bb390f900 100644
--- a/docs/tutorial/sg_execution_times.html
+++ b/docs/tutorial/sg_execution_times.html
@@ -381,7 +381,7 @@
<div class="section" id="computation-times">
<span id="sphx-glr-tutorial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>18:09.424</strong> total execution time for <strong>tutorial</strong> files:</p>
+<p><strong>17:08.962</strong> total execution time for <strong>tutorial</strong> files:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 83%" />
@@ -390,42 +390,42 @@
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></td>
-<td><p>14:17.714</p></td>
+<td><p>13:25.316</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></td>
-<td><p>01:43.786</p></td>
+<td><p>01:45.074</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></td>
-<td><p>01:01.152</p></td>
+<td><p>00:58.242</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></td>
-<td><p>00:45.458</p></td>
+<td><p>00:42.810</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></td>
-<td><p>00:18.982</p></td>
+<td><p>00:15.910</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></td>
-<td><p>00:01.432</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></td>
+<td><p>00:00.864</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></td>
-<td><p>00:00.900</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></td>
+<td><p>00:00.745</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="uma.html#sphx-glr-tutorial-uma-py"><span class="std std-ref">Making your Hardware Accelerator TVM-ready with UMA</span></a> (<code class="docutils literal notranslate"><span class="pre">uma.py</span></code>)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="uma.html#sphx-glr-tutorial-uma-py"><span class="std std-ref">Making your Hardware Accelerator TVM-ready with UMA</span></a> (<code class="docutils literal notranslate"><span class="pre">uma.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0 MB</p></td>
</tr>
diff --git a/docs/tutorial/tensor_expr_get_started.html b/docs/tutorial/tensor_expr_get_started.html
index bb2aeff117..1b9550f673 100644
--- a/docs/tutorial/tensor_expr_get_started.html
+++ b/docs/tutorial/tensor_expr_get_started.html
@@ -588,8 +588,8 @@ helper function to run a profile of the TVM generated code.</p>
<span class="n">evaluate_addition</span><span class="p">(</span><span class="n">fadd</span><span class="p">,</span> <a href="../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tgt</span></a><span class="p">,</span> <span class="s2">"naive"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" ti [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.000008
-naive: 0.000008
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.000006
+naive: 0.000006
</pre></div>
</div>
</div>
@@ -644,7 +644,7 @@ compile and run this new schedule with the parallel operation applied:</p>
<span class="n">evaluate_addition</span><span class="p">(</span><span class="n">fadd_parallel</span><span class="p">,</span> <a href="../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tgt</span></a><span class="p">,</span> <span class="s2">"parallel"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.h [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallel: 0.000010
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallel: 0.000006
</pre></div>
</div>
</div>
@@ -683,7 +683,7 @@ factor to be the number of threads on your CPU.</p>
<span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/driver.html#tvm.lower" title="tvm.lower" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span></a><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector: 0.000040
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector: 0.000039
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -720,10 +720,10 @@ class Module:
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Operator Timing Performance
- numpy 7.735510000657087e-06 1.0
- naive 7.6943e-06 0.9946726200788847
-parallel 1.01776e-05 1.3156986416067553
- vector 3.9928799999999996e-05 5.161754040342301
+ numpy 6.213489996298449e-06 1.0
+ naive 5.6550999999999994e-06 0.9101326313181323
+parallel 5.893e-06 0.9484202925426172
+ vector 3.89052e-05 6.261408648469206
</pre></div>
</div>
<div class="admonition-code-specialization admonition">
@@ -1039,7 +1039,7 @@ matrix multiplication.</p>
<span class="n">answer</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">b</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019047
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013850
</pre></div>
</div>
<p>Now we write a basic matrix multiplication using TVM TE and verify that it
@@ -1080,7 +1080,7 @@ optimizations.</p>
<span class="n">evaluate_operation</span><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">s</span></a><span class="p">,</span> <span class="p">[</span><a href="../reference/api/python/te.html#tvm.te.Tensor" title="tvm.te.Tensor" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.448148
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.352162
</pre></div>
</div>
<p>Let’s take a look at the intermediate representation of the operator and
@@ -1144,7 +1144,7 @@ schedule.</p>
<span class="n">evaluate_operation</span><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">s</span></a><span class="p">,</span> <span class="p">[</span><a href="../reference/api/python/te.html#tvm.te.Tensor" title="tvm.te.Tensor" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.307210
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.292476
</pre></div>
</div>
<p>By reordering the computation to take advantage of caching, you should see a
@@ -1193,7 +1193,7 @@ already cache friendly from our previous optimizations.</p>
<span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/driver.html#tvm.lower" title="tvm.lower" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span></a><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.297649
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.270381
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1242,7 +1242,7 @@ more cache friendly.</p>
<span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/driver.html#tvm.lower" title="tvm.lower" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span></a><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.118966
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.109763
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1312,7 +1312,7 @@ optimized schedule.</p>
<span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/driver.html#tvm.lower" title="tvm.lower" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span></a><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.106300
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.103115
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1378,7 +1378,7 @@ to `C</cite> when all the block results are ready.</p>
<span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/driver.html#tvm.lower" title="tvm.lower" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span></a><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.111623
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.094788
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1435,7 +1435,7 @@ of thread-level parallelization.</p>
<span class="nb">print</span><span class="p">(</span><a href="../reference/api/python/driver.html#tvm.lower" title="tvm.lower" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">lower</span></a><span class="p">(</span><a href="../reference/api/python/te.html#tvm.te.Schedule" title="tvm.te.Schedule" class="sphx-glr-backref-module-tvm-te sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.133673
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.113398
# from tvm.script import ir as I
# from tvm.script import tir as T
@@ -1488,13 +1488,13 @@ working, we can compare the results.</p>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span> Operator Timing Performance
- none 3.4481480034 1.0
- blocking 0.30720981950000004 0.08909415117827886
- vectorization 0.297648947 0.0863213953422264
-loop permutation 0.11896635180000001 0.03450152130439147
- array packing 0.10630043630000001 0.030828269608840424
- block caching 0.1116226948 0.03237178180575078
- parallelization 0.13367337540000002 0.03876671629761634
+ none 3.3521616879 1.0
+ blocking 0.29247639039999995 0.0872500844621326
+ vectorization 0.2703805014 0.08065855008604401
+loop permutation 0.10976311450000001 0.03274397977168052
+ array packing 0.103115292 0.030760834828524562
+ block caching 0.0947880493 0.028276693705482046
+ parallelization 0.11339750280000001 0.03382817219387743
</pre></div>
</div>
<p>Note that the outputs on the web page reflect the running times on a
@@ -1526,7 +1526,6 @@ is</p>
you can build generic templates of the matrix multiplication and other
operations with tunable parameters that allows you to automatically optimize
the computation for specific platforms.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 1.152 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-tensor-expr-get-started-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tensor_expr_get_started.py</span></code></a></p>