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Posted to commits@tvm.apache.org by tq...@apache.org on 2022/06/08 06:48:39 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@f5f9600614c4aa933c863001459d92b13d9b72fc)
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 e9aa54106 deploying docs (apache/tvm@f5f9600614c4aa933c863001459d92b13d9b72fc)
e9aa54106 is described below
commit e9aa54106b50370cdb2164c51de75039438e9952
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Wed Jun 8 06:48:32 2022 +0000
deploying docs (apache/tvm@f5f9600614c4aa933c863001459d92b13d9b72fc)
---
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.../opt_gemm.ipynb | 4 +-
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.../8c7d8fd6a4b93bcff1f5573943dd02f4/scan.py | 2 +-
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.../deploy_object_detection_pytorch.rst.txt | 4 +-
.../deploy_models/deploy_prequantized.rst.txt | 6 +-
.../deploy_prequantized_tflite.rst.txt | 4 +-
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.../how_to/deploy_models/deploy_sparse.rst.txt | 8 +-
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.../tutorial/tensor_ir_blitz_course.rst.txt | 6 +-
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221 files changed, 4312 insertions(+), 4269 deletions(-)
diff --git a/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb b/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
index c727a782b..29020c7a0 100644
--- a/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
+++ b/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nDeploy a Hugging Face Pruned Model on CPU\n=========================================\n**Author**: `Josh Fromm <https://github.com/jwfromm>`_\n\nThis tutorial demonstrates how to take any pruned model, in this case `PruneBert\nfrom Hugging Face\n<https://huggingface.co/huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad>`_,\nand use TVM to leverage the model's sparsity support to produce real speedups. Although\nthe primary purpose of this tutorial is to realize spee [...]
+ "\nDeploy a Hugging Face Pruned Model on CPU\n=========================================\n**Author**: `Josh Fromm <https://github.com/jwfromm>`_\n\nThis tutorial demonstrates how to take any pruned model, in this case `PruneBert\nfrom Hugging Face\n<https://huggingface.co/huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad>`_,\nand use TVM to leverage the model's sparsity support to produce real speedups. Although\nthe primary purpose of this tutorial is to realize spee [...]
]
},
{
diff --git a/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb b/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
index b5cb9181d..2c7aa61da 100644
--- a/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
+++ b/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
@@ -220,7 +220,7 @@
},
"outputs": [],
"source": [
- "s = te.create_schedule(C.op)\n\n# Allocate write cache\nCC = s.cache_write(C, \"global\")\n\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n\n# Write cache is computed at no\ns[CC].compute_at(s[C], no)\n\n# New inner axes\nmc, nc = s[CC].op.axis\n\n(kaxis,) = s[CC].op.reduce_axis\nko, ki = s[CC].split(kaxis, factor=kfactor)\ns[CC].reorder(ko, mc, ki, nc)\ns[CC].vectorize(nc)\n\n# TODO: Add separate optimization step to discuss loop unrolloing\n# unrolling is a l [...]
+ "s = te.create_schedule(C.op)\n\n# Allocate write cache\nCC = s.cache_write(C, \"global\")\n\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n\n# Write cache is computed at no\ns[CC].compute_at(s[C], no)\n\n# New inner axes\nmc, nc = s[CC].op.axis\n\n(kaxis,) = s[CC].op.reduce_axis\nko, ki = s[CC].split(kaxis, factor=kfactor)\ns[CC].reorder(ko, mc, ki, nc)\ns[CC].vectorize(nc)\n\n# TODO: Add separate optimization step to discuss loop unrolling\n# unrolling is a lo [...]
]
},
{
@@ -281,7 +281,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Summary\n-------\nAfter applying the above simple optimizations with only 18 lines of code,\nour generated code can achieve 60% of the `numpy` performance with MKL.\nNote that the outputs on the web page reflect the running times on a non-exclusive\nDocker container, thereby they are *unreliable*. It is highly encouraged to run the\ntutorial by yourself to observe the performance gain acheived by TVM.\n\n"
+ "Summary\n-------\nAfter applying the above simple optimizations with only 18 lines of code,\nour generated code can achieve 60% of the `numpy` performance with MKL.\nNote that the outputs on the web page reflect the running times on a non-exclusive\nDocker container, thereby they are *unreliable*. It is highly encouraged to run the\ntutorial by yourself to observe the performance gain achieved by TVM.\n\n"
]
}
],
diff --git a/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb b/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
index 0449a06bf..8994b1354 100644
--- a/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
+++ b/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nIntrinsics and Math Functions\n=============================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nWhile TVM supports basic arithmetic operations. In many cases\nusually we will need more complicated builtin functions.\nFor example :code:`exp` to take the exponential of the function.\n\nThese functions are target system dependent and may have different\nnames of different target platforms. In this tutorial, we will learn\nhow we can invoke these target spec [...]
+ "\nIntrinsics and Math Functions\n=============================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nWhile TVM supports basic arithmetic operations. In many cases\nusually we will need more complicated builtin functions.\nFor example :code:`exp` to take the exponential of the function.\n\nThese functions are target system dependent and may have different\nnames of different target platforms. In this tutorial, we will learn\nhow we can invoke these target spec [...]
]
},
{
diff --git a/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb b/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb
index f61b8670e..4a567d69c 100644
--- a/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb
+++ b/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb
@@ -51,7 +51,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Create the search task\n----------------------\nWith the function defined, we can now create the task for the auto_scheduler\nto search against. We specify the particular parameters for this matrix\nmultiplication, in this case a multiplication of to square matricies of size\n1024x1024. We then create a search task with N=L=M=1024 and dtype=\"float32\"\n\n.. admonition:: Improve performance with custom targets\n\n In order for TVM to take full advantage of specific hardware pla [...]
+ "Create the search task\n----------------------\nWith the function defined, we can now create the task for the auto_scheduler\nto search against. We specify the particular parameters for this matrix\nmultiplication, in this case a multiplication of two square matrices of size\n1024x1024. We then create a search task with N=L=M=1024 and dtype=\"float32\"\n\n.. admonition:: Improve performance with custom targets\n\n In order for TVM to take full advantage of specific hardware pla [...]
]
},
{
diff --git a/docs/_downloads/286e7f77f494a25312ac88e3f234822e/extern_op.py b/docs/_downloads/286e7f77f494a25312ac88e3f234822e/extern_op.py
index fb9b2eaf8..a0aa5d724 100644
--- a/docs/_downloads/286e7f77f494a25312ac88e3f234822e/extern_op.py
+++ b/docs/_downloads/286e7f77f494a25312ac88e3f234822e/extern_op.py
@@ -25,7 +25,7 @@ the pipeline. For example, we might want to use cuDNN for
some of the convolution kernels and define the rest of the stages.
TVM supports these black box function calls natively.
-Specfically, TVM support all the tensor functions that are DLPack compatible.
+Specifically, TVM support all the tensor functions that are DLPack compatible.
Which means we can call any function with POD types(pointer, int, float)
or pointer to DLTensor as argument.
"""
@@ -52,7 +52,7 @@ if not tvm.get_global_func("tvm.contrib.cblas.matmul", allow_missing=True):
# list of symbolic placeholder for the outputs and returns the executing statement.
#
# In this case we simply call a registered TVM function, which invokes a CBLAS call.
-# TVM does not control internal of the extern array function and treats it as blackbox.
+# TVM does not control internal of the extern array function and treats it as black-box.
# We can further mix schedulable TVM calls that add a bias term to the result.
#
n = 1024
diff --git a/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb b/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb
index 0b75b7f57..3a83871b9 100644
--- a/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb
+++ b/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nOptimizing Operators with Schedule Templates and AutoTVM\n========================================================\n**Authors**:\n`Lianmin Zheng <https://github.com/merrymercy>`_,\n`Chris Hoge <https://github.com/hogepodge>`_\n\nIn this tutorial, we show how the TVM Tensor Expression (TE) language\ncan be used to write schedule templates that can be searched by AutoTVM to\nfind the optimal schedule. This process is called Auto-Tuning, which helps\nautomate the process of opt [...]
+ "\n\nOptimizing Operators with Schedule Templates and AutoTVM\n========================================================\n**Authors**:\n`Lianmin Zheng <https://github.com/merrymercy>`_,\n`Chris Hoge <https://github.com/hogepodge>`_\n\nIn this tutorial, we show how the TVM Tensor Expression (TE) language\ncan be used to write schedule templates that can be searched by AutoTVM to\nfind the optimal schedule. This process is called Auto-Tuning, which helps\nautomate the process of opt [...]
]
},
{
@@ -90,7 +90,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "A Matrix Multiplication Template with the Advanced Parameter API\n----------------------------------------------------------------\nIn the previous template, we manually listed all of the possible values for a\nknob. This is the lowest level API to define the space, and gives an explicit\nenumeration of the parameter space to search. However, we also provide\nanother set of APIs that can make the definition of the search space easier\nand smarter. Where possible, we receomment y [...]
+ "A Matrix Multiplication Template with the Advanced Parameter API\n----------------------------------------------------------------\nIn the previous template, we manually listed all of the possible values for a\nknob. This is the lowest level API to define the space, and gives an explicit\nenumeration of the parameter space to search. However, we also provide\nanother set of APIs that can make the definition of the search space easier\nand smarter. Where possible, we recommend yo [...]
]
},
{
@@ -115,7 +115,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Step 2: Use AutoTVM to Optimize the Matrix Multiplication\n---------------------------------------------------------\nIn Step 1, we wrote a matrix multiplication template that allowed us to\nparamaterize the block size used in the `split` schedule. We can now conduct\na search over this parameter space. The next step is to pick a tuner to guide\nthe exploration of this space.\n\nAuto-tuners in TVM\n~~~~~~~~~~~~~~~~~~\nThe job for a tuner can be described by following pseudo code [...]
+ "Step 2: Use AutoTVM to Optimize the Matrix Multiplication\n---------------------------------------------------------\nIn Step 1, we wrote a matrix multiplication template that allowed us to\nparameterize the block size used in the `split` schedule. We can now conduct\na search over this parameter space. The next step is to pick a tuner to guide\nthe exploration of this space.\n\nAuto-tuners in TVM\n~~~~~~~~~~~~~~~~~~\nThe job for a tuner can be described by following pseudo code [...]
]
},
{
@@ -176,7 +176,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "With tuning completed, we can choose the configuration from the log file that\nhas the best measured performance and compile the schedule with the\ncorresponding parameters. We also do a quick verfication that the schedule is\nproducing correct answers. We can call the function :code:`matmul` directly\nunder the :any:`autotvm.apply_history_best` context. When we call this\nfunction, it will query the dispatch context with its argument and get the\nbest config with the same argu [...]
+ "With tuning completed, we can choose the configuration from the log file that\nhas the best measured performance and compile the schedule with the\ncorresponding parameters. We also do a quick verification that the schedule is\nproducing correct answers. We can call the function :code:`matmul` directly\nunder the :any:`autotvm.apply_history_best` context. When we call this\nfunction, it will query the dispatch context with its argument and get the\nbest config with the same arg [...]
]
},
{
@@ -194,7 +194,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Final Notes and Summary\n-----------------------\nIn this tutorial, we have shown how to build operator templates that allow\nTVM to search a parameter space and choose optimized schedule configurations.\nTo gain a deeper understanding of how this works, we recommend expanding on\nthis example by adding new search parameters to the schedule based on\nschedule operations demonstated in the `Getting Started With Tensor\nExpressions <tensor_expr_get_started>_` tutorial. In the upco [...]
+ "Final Notes and Summary\n-----------------------\nIn this tutorial, we have shown how to build operator templates that allow\nTVM to search a parameter space and choose optimized schedule configurations.\nTo gain a deeper understanding of how this works, we recommend expanding on\nthis example by adding new search parameters to the schedule based on\nschedule operations demonstrated in the :ref: `Getting Started With Tensor\nExpressions <tensor_expr_get_started>_` tutorial. In t [...]
]
}
],
diff --git a/docs/_downloads/3a9b1d387f618487c8ccf6b8b78ae179/intro_topi.py b/docs/_downloads/3a9b1d387f618487c8ccf6b8b78ae179/intro_topi.py
index dad8c53bf..17fa3ff37 100644
--- a/docs/_downloads/3a9b1d387f618487c8ccf6b8b78ae179/intro_topi.py
+++ b/docs/_downloads/3a9b1d387f618487c8ccf6b8b78ae179/intro_topi.py
@@ -23,9 +23,8 @@ Introduction to TOPI
This is an introductory tutorial to TVM Operator Inventory (TOPI).
TOPI provides numpy-style generic operations and schedules with higher abstractions than TVM.
-In this tutorial, we will see how TOPI can save us from writing boilerplates code in TVM.
+In this tutorial, we will see how TOPI can save us from writing boilerplate code in TVM.
"""
-from __future__ import absolute_import, print_function
import tvm
import tvm.testing
diff --git a/docs/_downloads/3c5c85c3954f3110f16ca084e286f03a/opt_conv_cuda.py b/docs/_downloads/3c5c85c3954f3110f16ca084e286f03a/opt_conv_cuda.py
index 0ac2c625b..3d2caa0d3 100644
--- a/docs/_downloads/3c5c85c3954f3110f16ca084e286f03a/opt_conv_cuda.py
+++ b/docs/_downloads/3c5c85c3954f3110f16ca084e286f03a/opt_conv_cuda.py
@@ -97,7 +97,7 @@ B = te.compute(
# :width: 271px
#
# In this example, we load both Apad and W into buffer AA and WW, which are
-# stored in the shared memory. These bufferes will be later shared by all
+# stored in the shared memory. These buffers will be later shared by all
# threads within the same thread block to compute the convolution. Each thread
# then loads its own part from shared buffer into their local registers, AL and
# WL. BL is a local cache of output B, which is also stored in the thread local
diff --git a/docs/_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.py b/docs/_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.py
index 7d8c0d781..25ea4e8a5 100644
--- a/docs/_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.py
+++ b/docs/_downloads/40a01cffb015a67aaec0fad7e27cf80d/tensor_expr_get_started.py
@@ -187,8 +187,8 @@ log = [("numpy", np_running_time / np_repeat)]
evaluate_addition(fadd, tgt, "naive", log=log)
################################################################################
-# Updating the Schedule to Use Paralleism
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Updating the Schedule to Use Parallelism
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Now that we've illustrated the fundamentals of TE, let's go deeper into what
# schedules do, and how they can be used to optimize tensor expressions for
@@ -754,7 +754,7 @@ print(tvm.lower(s, [A, B, C], simple_mode=True))
# regular but discontinuous. We expect that after some transformation we can
# get a continuous access pattern. By reordering a ``[16][16]`` array to a
# ``[16/4][16][4]`` array the access pattern of B will be sequential when
-# grabing the corresponding value from the packed array.
+# grabbing the corresponding value from the packed array.
#
# To accomplish this, we are going to have to start with a new default
# schedule, taking into account the new packing of B. It's worth taking a
@@ -889,7 +889,7 @@ for result in log:
# have from this introduction to TE, we can now begin to explore how TVM can
# automate the schedule optimization process.
#
-# This tutorial provided a walkthrough of TVM Tensor Expresstion (TE) workflow
+# This tutorial provided a walk-through of TVM Tensor Expression (TE) workflow
# using a vector add and a matrix multiplication examples. The general workflow
# is
#
diff --git a/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb b/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb
index 50732399f..838761660 100644
--- a/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb
+++ b/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb
@@ -155,7 +155,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Updating the Schedule to Use Paralleism\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nNow that we've illustrated the fundamentals of TE, let's go deeper into what\nschedules do, and how they can be used to optimize tensor expressions for\ndifferent architectures. A schedule is a series of steps that are applied to\nan expression to transform it in a number of different ways. When a schedule\nis applied to an expression in TE, the inputs and outputs remain the same,\nbut when compi [...]
+ "Updating the Schedule to Use Parallelism\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nNow that we've illustrated the fundamentals of TE, let's go deeper into what\nschedules do, and how they can be used to optimize tensor expressions for\ndifferent architectures. A schedule is a series of steps that are applied to\nan expression to transform it in a number of different ways. When a schedule\nis applied to an expression in TE, the inputs and outputs remain the same,\nbut when com [...]
]
},
{
@@ -503,7 +503,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Optimization 4: Array Packing\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nAnother important trick is array packing. This trick is to reorder the\nstorage dimension of the array to convert the continuous access pattern on\ncertain dimension to a sequential pattern after flattening.\n\n\n\n :align: center\n\nJust as it is shown in the figure above, after blocking the computations, we\ncan observe the array access [...]
+ "Optimization 4: Array Packing\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nAnother important trick is array packing. This trick is to reorder the\nstorage dimension of the array to convert the continuous access pattern on\ncertain dimension to a sequential pattern after flattening.\n\n\n\n :align: center\n\nJust as it is shown in the figure above, after blocking the computations, we\ncan observe the array access [...]
]
},
{
@@ -582,7 +582,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Final Notes and Summary\n-----------------------\nAs mentioned earlier, how to apply optimizations using TE and scheduling\nprimitives can require some knowledge of the underlying architecture and\nalgorithms. However, TE was designed to act as a foundation for more complex\nalgorithms that can search the potential optimization. With the knowledge you\nhave from this introduction to TE, we can now begin to explore how TVM can\nautomate the schedule optimization process.\n\nThis [...]
+ "Final Notes and Summary\n-----------------------\nAs mentioned earlier, how to apply optimizations using TE and scheduling\nprimitives can require some knowledge of the underlying architecture and\nalgorithms. However, TE was designed to act as a foundation for more complex\nalgorithms that can search the potential optimization. With the knowledge you\nhave from this introduction to TE, we can now begin to explore how TVM can\nautomate the schedule optimization process.\n\nThis [...]
]
}
],
diff --git a/docs/_downloads/5499fb7d70e17c6aabf49246a978db52/use_pass_infra.py b/docs/_downloads/5499fb7d70e17c6aabf49246a978db52/use_pass_infra.py
index 67cdfdedc..e38383e69 100644
--- a/docs/_downloads/5499fb7d70e17c6aabf49246a978db52/use_pass_infra.py
+++ b/docs/_downloads/5499fb7d70e17c6aabf49246a978db52/use_pass_infra.py
@@ -35,7 +35,7 @@ on a Relay/tir program where the dependencies between passes can be resolved by
pass infra. For more details about each type of these passes, please refer to
the :ref:`pass-infra`
-This tutorial mainly demostrates how developers can use the pass infra to perform
+This tutorial mainly demonstrates how developers can use the pass infra to perform
a certain optimization and create an optimization pipeline for a Relay program.
The same approach can be used for tir as well.
"""
@@ -104,7 +104,7 @@ mod = relay.transform.EliminateCommonSubexpr()(mod)
print(mod)
###############################################################################
-# Some optimizations, such as fusion, are parameteric as well. For example,
+# Some optimizations, such as fusion, are parametric as well. For example,
# opt level 0 will not allow operators to be fused together. Users can pass the
# `fuse_opt_level` to enable this.
mod = relay.transform.FuseOps(fuse_opt_level=0)(mod)
@@ -127,7 +127,7 @@ print(mod)
# these issues explicitly by specifying the required passes of each pass and
# packing them as a whole to execute. For example, the same passes can now be
# applied using the sequential style as the following. :py:class:`tvm.transform.Sequential` is
-# similiar to `torch.nn.sequential <https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential>`_
+# similar to `torch.nn.sequential <https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential>`_
# and `mxnet.gluon.block <https://mxnet.apache.org/api/python/docs/_modules/mxnet/gluon/block.html>`_.
# For example, `torch.nn.sequential` is used to contain a sequence of PyTorch
# `Modules` that will be added to build a network. It focuses on the network
@@ -267,7 +267,7 @@ print("done")
# -------
# This tutorial has covered how we can write and invoke passes in TVM more
# conveniently using the pass infra. Different ways of invoking a pass are also
-# disucssed. Using :py:class:`tvm.transform.Sequential` can largely help
+# discussed. Using :py:class:`tvm.transform.Sequential` can largely help
# users to ease the work of handling multiple optimization passes and their
# dependencies. In addition, an example is provided to illustrate
# how we can debug a pass using the ``PrintIR`` and tracing.
diff --git a/docs/_downloads/5c7000b5aef924e29ec975ec3002ea03/tensor_ir_blitz_course.py b/docs/_downloads/5c7000b5aef924e29ec975ec3002ea03/tensor_ir_blitz_course.py
index e9a0801f3..11edc7ae9 100644
--- a/docs/_downloads/5c7000b5aef924e29ec975ec3002ea03/tensor_ir_blitz_course.py
+++ b/docs/_downloads/5c7000b5aef924e29ec975ec3002ea03/tensor_ir_blitz_course.py
@@ -25,7 +25,7 @@ TensorIR is a domain specific language for deep learning programs serving two br
- An implementation for transforming and optimizing programs on various hardware backends.
-- An abstraction for automatic tensorized program optimization.
+- An abstraction for automatic _tensorized_ program optimization.
"""
@@ -145,7 +145,7 @@ print(b)
# sequence of schedule primitives will help to improve the performance. And at last, we can lower
# and build it into a runnable module.
#
-# Here we just demostrate a very simple tranformation. First we create schedule on the input `ir_module`.
+# Here we just demonstrate a very simple transformation. First we create schedule on the input `ir_module`.
sch = tvm.tir.Schedule(ir_module)
print(type(sch))
@@ -155,7 +155,7 @@ print(type(sch))
# Get block by its name
block_b = sch.get_block("B")
-# Get loops surronding the block
+# Get loops surrounding the block
(i,) = sch.get_loops(block_b)
# Tile the loop nesting.
i_0, i_1, i_2 = sch.split(i, factors=[2, 2, 2])
diff --git a/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb b/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb
index 181e3cff2..1325b7cfb 100644
--- a/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb
+++ b/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nIntroduction to TOPI\n====================\n**Author**: `Ehsan M. Kermani <https://github.com/ehsanmok>`_\n\nThis is an introductory tutorial to TVM Operator Inventory (TOPI).\nTOPI provides numpy-style generic operations and schedules with higher abstractions than TVM.\nIn this tutorial, we will see how TOPI can save us from writing boilerplates code in TVM.\n"
+ "\n\nIntroduction to TOPI\n====================\n**Author**: `Ehsan M. Kermani <https://github.com/ehsanmok>`_\n\nThis is an introductory tutorial to TVM Operator Inventory (TOPI).\nTOPI provides numpy-style generic operations and schedules with higher abstractions than TVM.\nIn this tutorial, we will see how TOPI can save us from writing boilerplate code in TVM.\n"
]
},
{
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport tvm\nimport tvm.testing\nfrom tvm import te\nfrom tvm import topi\nimport numpy as np"
+ "import tvm\nimport tvm.testing\nfrom tvm import te\nfrom tvm import topi\nimport numpy as np"
]
},
{
diff --git a/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py b/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py
index ef921563e..e3072773b 100644
--- a/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py
+++ b/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py
@@ -74,7 +74,7 @@ from tvm import autotvm
#
# If you are familiar with writing cuda schedule, you can find the following
# template is very general. Actually this template can be easily modified
-# to tune other operators such as depthwise convolution and gemm.
+# to tune other operators such as depthwise convolution and GEMM.
# In order to fully understand this template, you should be familiar with
# the schedule primitives and auto tuning API. You can refer to the above
# tutorials and :ref:`autotvm tutorial <tutorial-autotvm-matmul-x86>`
diff --git a/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb b/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb
index 25d2afad1..454ceccc4 100644
--- a/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb
+++ b/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb
@@ -51,7 +51,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Schedule the Scan Cell\n----------------------\nWe can schedule the body of the scan by scheduling the update and\ninit part seperately. Note that it is invalid to schedule the\nfirst iteration dimension of the update part.\nTo split on the time iteration, user can schedule on scan_op.scan_axis instead.\n\n\n"
+ "Schedule the Scan Cell\n----------------------\nWe can schedule the body of the scan by scheduling the update and\ninit part separately. Note that it is invalid to schedule the\nfirst iteration dimension of the update part.\nTo split on the time iteration, user can schedule on scan_op.scan_axis instead.\n\n\n"
]
},
{
diff --git a/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb b/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb
index 7737043f4..b1f0fed95 100644
--- a/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb
+++ b/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb
@@ -40,7 +40,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Step 1: Define the search space\n--------------------------------\nThere are plenty of useful schedule primitives in tvm. You can also find\nsome tutorials that describe them in more details, such as\n(1). `opt-conv-gpu`\n(2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example>`_\n\nHowever, their implementations are manually tuned for some special input\nshapes. In this sectio [...]
+ "Step 1: Define the search space\n--------------------------------\nThere are plenty of useful schedule primitives in tvm. You can also find\nsome tutorials that describe them in more details, such as\n(1). `opt-conv-gpu`\n(2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example>`_\n\nHowever, their implementations are manually tuned for some special input\nshapes. In this sectio [...]
]
},
{
diff --git a/docs/_downloads/7372db5919b5619bc34fde3434862bca/opt_conv_tensorcore.py b/docs/_downloads/7372db5919b5619bc34fde3434862bca/opt_conv_tensorcore.py
index 702e4a777..ccfc7b974 100644
--- a/docs/_downloads/7372db5919b5619bc34fde3434862bca/opt_conv_tensorcore.py
+++ b/docs/_downloads/7372db5919b5619bc34fde3434862bca/opt_conv_tensorcore.py
@@ -306,7 +306,7 @@ def intrin_wmma_store_matrix():
# *Warp-level Operation*
#
# Note that all TensorCore instructions are warp-level instructions, which means all 32 threads
-# in a warp should do this instruction simultaneously. Making theadIdx.x extent=32 is one of the
+# in a warp should do this instruction simultaneously. Making threadIdx.x extent=32 is one of the
# easiest way to solve this. Then We can bind threadIdx.x to any loops except those contain
# TensorCore intrinsics directly or indirectly. Also note that it is not the unique solution.
# The only thing we should do is to make sure all threads in a warp can call TensorCore at the same time.
diff --git a/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb b/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb
index 8ff422f51..622bd15a0 100644
--- a/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb
+++ b/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb
@@ -83,7 +83,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Scheduling the Computation\n--------------------------\nTo use TensorCores in TVM, we must schedule the computation into specific structure\nto match the tensor intrinsic. The same as traditional GPU programs, we can also use\nshared memory to boost the speed. If you have any questions about blocking and shared\nmemory, please refer `opt-conv-gpu`.\n\nIn this example, each block contains 2x4 warps, and each warp calls 4x2 TensorCore\ninstructions. Thus, the output shape of each [...]
+ "Scheduling the Computation\n--------------------------\nTo use TensorCores in TVM, we must schedule the computation into specific structure\nto match the tensor intrinsic. The same as traditional GPU programs, we can also use\nshared memory to boost the speed. If you have any questions about blocking and shared\nmemory, please refer `opt-conv-gpu`.\n\nIn this example, each block contains 2x4 warps, and each warp calls 4x2 TensorCore\ninstructions. Thus, the output shape of each [...]
]
},
{
diff --git a/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb b/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb
index f9a25ae1e..073b77447 100644
--- a/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb
+++ b/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nHow to Use TVM Pass Infra\n=========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay/tir, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes and make it\napplicable to different layers of the IR in the TVM stack.\n\nThe optimizations of a Relay/tir program could be applied at various [...]
+ "\n\nHow to Use TVM Pass Infra\n=========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay/tir, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes and make it\napplicable to different layers of the IR in the TVM stack.\n\nThe optimizations of a Relay/tir program could be applied at various [...]
]
},
{
@@ -87,7 +87,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Some optimizations, such as fusion, are parameteric as well. For example,\nopt level 0 will not allow operators to be fused together. Users can pass the\n`fuse_opt_level` to enable this.\n\n"
+ "Some optimizations, such as fusion, are parametric as well. For example,\nopt level 0 will not allow operators to be fused together. Users can pass the\n`fuse_opt_level` to enable this.\n\n"
]
},
{
@@ -105,7 +105,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Use Sequential to Apply a Sequence of Passes\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nApplying passes as above is actually tedious and it may require users to have\nbetter understanding about the dependencies between them. For example, fusion\ncurrently doesn't work well on let bindings. Therefore, we would not be able\nto fuse operators that were fusable if :py:func:`relay.transform.ToANormalForm` is applied before\nfusion, as this pass generates let bindings for each exp [...]
+ "Use Sequential to Apply a Sequence of Passes\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nApplying passes as above is actually tedious and it may require users to have\nbetter understanding about the dependencies between them. For example, fusion\ncurrently doesn't work well on let bindings. Therefore, we would not be able\nto fuse operators that were fusable if :py:func:`relay.transform.ToANormalForm` is applied before\nfusion, as this pass generates let bindings for each exp [...]
]
},
{
@@ -213,7 +213,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Summary\n-------\nThis tutorial has covered how we can write and invoke passes in TVM more\nconveniently using the pass infra. Different ways of invoking a pass are also\ndisucssed. Using :py:class:`tvm.transform.Sequential` can largely help\nusers to ease the work of handling multiple optimization passes and their\ndependencies. In addition, an example is provided to illustrate\nhow we can debug a pass using the ``PrintIR`` and tracing.\n\n"
+ "Summary\n-------\nThis tutorial has covered how we can write and invoke passes in TVM more\nconveniently using the pass infra. Different ways of invoking a pass are also\ndiscussed. Using :py:class:`tvm.transform.Sequential` can largely help\nusers to ease the work of handling multiple optimization passes and their\ndependencies. In addition, an example is provided to illustrate\nhow we can debug a pass using the ``PrintIR`` and tracing.\n\n"
]
}
],
diff --git a/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb b/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb
index 0c9d40ecd..7d8c3b2fa 100644
--- a/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb
+++ b/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb
@@ -166,7 +166,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Compile and run with TVM\n------------------------\n\nExport the weigths from PyTorch model to Python Dict\n\n"
+ "Compile and run with TVM\n------------------------\n\nExport the weights from PyTorch model to Python Dict\n\n"
]
},
{
diff --git a/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb b/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb
index 8646d2b81..5bc8dead1 100644
--- a/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb
+++ b/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nExternal Tensor Functions\n=========================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nWhile TVM supports transparent code generation, sometimes\nit is also helpful to incorporate manual written code into\nthe pipeline. For example, we might want to use cuDNN for\nsome of the convolution kernels and define the rest of the stages.\n\nTVM supports these black box function calls natively.\nSpecfically, TVM support all the tensor functions that are DLPack c [...]
+ "\nExternal Tensor Functions\n=========================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nWhile TVM supports transparent code generation, sometimes\nit is also helpful to incorporate manual written code into\nthe pipeline. For example, we might want to use cuDNN for\nsome of the convolution kernels and define the rest of the stages.\n\nTVM supports these black box function calls natively.\nSpecifically, TVM support all the tensor functions that are DLPack [...]
]
},
{
@@ -33,7 +33,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Use Extern Tensor Function\n--------------------------\nIn the example below, we use :any:`te.extern` to add an extern\narray function call. In the extern call, we declare the shape\nof output tensors. In the second argument we provide the list of inputs.\n\nUser will need to provide a function describing how to compute the result.\nThe compute function takes list of symbolic placeholder for the inputs,\nlist of symbolic placeholder for the outputs and returns the executing stat [...]
+ "Use Extern Tensor Function\n--------------------------\nIn the example below, we use :any:`te.extern` to add an extern\narray function call. In the extern call, we declare the shape\nof output tensors. In the second argument we provide the list of inputs.\n\nUser will need to provide a function describing how to compute the result.\nThe compute function takes list of symbolic placeholder for the inputs,\nlist of symbolic placeholder for the outputs and returns the executing stat [...]
]
},
{
diff --git a/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb b/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb
index 471a6529a..df0b5d24e 100644
--- a/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb
+++ b/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb
@@ -40,7 +40,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Memory Hierarchy\n----------------\n\nWe first specify the memory hierarchy for buffers. The figure below shows the\nGPU memory hierarchy. One important difference from CPU memory hierarchy is\nthat GPU provides a cache buffer called shared memory, which is managed by\nprogrammers. Thus how to maximize the data reuse in the shared memory is\ncritical to achieve high performance in GPU kernels.\n\n![](https://github.com/dmlc/web-data/raw/main/tvm/tutorial/gpu_memory_hierarchy.png [...]
+ "Memory Hierarchy\n----------------\n\nWe first specify the memory hierarchy for buffers. The figure below shows the\nGPU memory hierarchy. One important difference from CPU memory hierarchy is\nthat GPU provides a cache buffer called shared memory, which is managed by\nprogrammers. Thus how to maximize the data reuse in the shared memory is\ncritical to achieve high performance in GPU kernels.\n\n
# Schedule the Scan Cell
# ----------------------
# We can schedule the body of the scan by scheduling the update and
-# init part seperately. Note that it is invalid to schedule the
+# init part separately. Note that it is invalid to schedule the
# first iteration dimension of the update part.
# To split on the time iteration, user can schedule on scan_op.scan_axis instead.
#
diff --git a/docs/_downloads/8e7bbc9dbdda76ac573b24606b41c006/autotvm_matmul_x86.py b/docs/_downloads/8e7bbc9dbdda76ac573b24606b41c006/autotvm_matmul_x86.py
index 545811721..b84a6193c 100644
--- a/docs/_downloads/8e7bbc9dbdda76ac573b24606b41c006/autotvm_matmul_x86.py
+++ b/docs/_downloads/8e7bbc9dbdda76ac573b24606b41c006/autotvm_matmul_x86.py
@@ -28,7 +28,7 @@ can be used to write schedule templates that can be searched by AutoTVM to
find the optimal schedule. This process is called Auto-Tuning, which helps
automate the process of optimizing tensor computation.
-This tutorial builds on the previous `tutorial on how to write a matrix
+This tutorial builds on the previous :doc:`tutorial on how to write a matrix
multiplication using TE <tensor_expr_get_started>`.
There are two steps in auto-tuning.
@@ -201,7 +201,7 @@ def matmul_v1(N, L, M, dtype):
# knob. This is the lowest level API to define the space, and gives an explicit
# enumeration of the parameter space to search. However, we also provide
# another set of APIs that can make the definition of the search space easier
-# and smarter. Where possible, we receomment you use this higher-level API
+# and smarter. Where possible, we recommend you use this higher-level API
#
# In the following example, we use :any:`ConfigSpace.define_split` to define a
# split knob. It will enumerate all the possible ways to split an axis and
@@ -267,7 +267,7 @@ def matmul(N, L, M, dtype):
# Step 2: Use AutoTVM to Optimize the Matrix Multiplication
# ---------------------------------------------------------
# In Step 1, we wrote a matrix multiplication template that allowed us to
-# paramaterize the block size used in the `split` schedule. We can now conduct
+# parameterize the block size used in the `split` schedule. We can now conduct
# a search over this parameter space. The next step is to pick a tuner to guide
# the exploration of this space.
#
@@ -295,7 +295,7 @@ def matmul(N, L, M, dtype):
#
# You can choose the tuner according to the size of your space, your time
# budget and other factors. For example, if your space is very small (less
-# than 1000), a gridsearch tuner or a random tuner is good enough. If your
+# than 1000), a grid-search tuner or a random tuner is good enough. If your
# space is at the level of 10^9 (this is the space size of a conv2d operator on
# CUDA GPU), XGBoostTuner can explore more efficiently and find better configs.
@@ -342,7 +342,7 @@ tuner.tune(
################################################################################
# With tuning completed, we can choose the configuration from the log file that
# has the best measured performance and compile the schedule with the
-# corresponding parameters. We also do a quick verfication that the schedule is
+# corresponding parameters. We also do a quick verification that the schedule is
# producing correct answers. We can call the function :code:`matmul` directly
# under the :any:`autotvm.apply_history_best` context. When we call this
# function, it will query the dispatch context with its argument and get the
@@ -371,7 +371,7 @@ tvm.testing.assert_allclose(c_np, c_tvm.numpy(), rtol=1e-4)
# TVM to search a parameter space and choose optimized schedule configurations.
# To gain a deeper understanding of how this works, we recommend expanding on
# this example by adding new search parameters to the schedule based on
-# schedule operations demonstated in the `Getting Started With Tensor
+# schedule operations demonstrated in the :ref: `Getting Started With Tensor
# Expressions <tensor_expr_get_started>_` tutorial. In the upcoming sections, we
-# will demonstate the AutoScheduler, a method for TVM to optimize common
+# will demonstrate the AutoScheduler, a method for TVM to optimize common
# operators without the need for the user to provide a user-defined template.
diff --git a/docs/_downloads/96137df89d8034b548f407123ec50ce9/opt_gemm.py b/docs/_downloads/96137df89d8034b548f407123ec50ce9/opt_gemm.py
index 5d698c612..920d7a87f 100644
--- a/docs/_downloads/96137df89d8034b548f407123ec50ce9/opt_gemm.py
+++ b/docs/_downloads/96137df89d8034b548f407123ec50ce9/opt_gemm.py
@@ -312,7 +312,7 @@ ko, ki = s[CC].split(kaxis, factor=kfactor)
s[CC].reorder(ko, mc, ki, nc)
s[CC].vectorize(nc)
-# TODO: Add separate optimization step to discuss loop unrolloing
+# TODO: Add separate optimization step to discuss loop unrolling
# unrolling is a loop optimization strategy which can reduce branch
# prediction failures and increases the chance of concurrent execution
# unroll kfactor loops
@@ -390,4 +390,4 @@ print(tvm.lower(s, [A, B, C], simple_mode=True))
# our generated code can achieve 60% of the `numpy` performance with MKL.
# Note that the outputs on the web page reflect the running times on a non-exclusive
# Docker container, thereby they are *unreliable*. It is highly encouraged to run the
-# tutorial by yourself to observe the performance gain acheived by TVM.
+# tutorial by yourself to observe the performance gain achieved by TVM.
diff --git a/docs/_downloads/9c3764c88ab3eb57dc223b4eda1e8a2f/deploy_sparse.py b/docs/_downloads/9c3764c88ab3eb57dc223b4eda1e8a2f/deploy_sparse.py
index 768a697f4..56a5f1aaf 100644
--- a/docs/_downloads/9c3764c88ab3eb57dc223b4eda1e8a2f/deploy_sparse.py
+++ b/docs/_downloads/9c3764c88ab3eb57dc223b4eda1e8a2f/deploy_sparse.py
@@ -36,11 +36,11 @@ different types of sparsity: **structured** and **unstructured**.
Pruning is a technique primarily used to reduce the parameter size of a model
by replacing weight values with 0s. Although many methods exist for choosing which
-weights should be set to 0, the most straight forward is by picking the
+weights should be set to 0, the most straight forward is by picking the
weights with the smallest value. Typically, weights are pruned to a desired
sparsity percentage. For example, a 95% sparse model would have only 5% of
its weights non-zero. Pruning to very high sparsities often requires
-finetuning or full retraining as it tends to be a lossy approximation.
+fine-tuning or full retraining as it tends to be a lossy approximation.
Although parameter size benefits are quite easy to obtain from a pruned model
through simple compression, leveraging sparsity to yield runtime speedups
is more complicated.
@@ -50,8 +50,8 @@ pruned weights together. In other words, they are pruned using both their
value and location. The benefit of bunching up pruned weights is that it allows
an algorithm such as matrix multiplication to skip entire blocks. It turns out
that some degree of *block sparsity* is very important to realizing significant
-speedups on most hardware available today.
-This is because when loading memory in most CPUs or GPUs,
+speedups on most hardware available today.
+This is because when loading memory in most CPUs or GPUs,
it doesn't save any work to skip reading a single value at a time, instead an entire
chunk or tile is read in and executed using something like vectorized instructions.
diff --git a/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb b/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb
index 6ee1999c9..7458071e0 100644
--- a/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb
+++ b/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb
@@ -292,7 +292,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "When we attempt to run the model, we get a familiar error telling us that more functions need to be registerd for myfloat.\n\nBecause this is a neural network, many more operations are required.\nHere, we register all the needed functions:\n\n"
+ "When we attempt to run the model, we get a familiar error telling us that more functions need to be registered for myfloat.\n\nBecause this is a neural network, many more operations are required.\nHere, we register all the needed functions:\n\n"
]
},
{
diff --git a/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb b/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb
index c061311f9..1251dab9d 100644
--- a/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb
+++ b/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nUse Relay Visualizer to Visualize Relay\n============================================================\n**Author**: `Chi-Wei Wang <https://github.com/chiwwang>`_\n\nRelay IR module can contain lots of operations. Although an individual\noperation is usually easy to understand, putting them together can cause\na complicated, hard-to-read graph. Things can get even worse with optimiztion-passes\ncoming into play.\n\nThis utility visualizes an IR module as nodes and edges. It defi [...]
+ "\nUse Relay Visualizer to Visualize Relay\n============================================================\n**Author**: `Chi-Wei Wang <https://github.com/chiwwang>`_\n\nRelay IR module can contain lots of operations. Although an individual\noperation is usually easy to understand, putting them together can cause\na complicated, hard-to-read graph. Things can get even worse with optimization-passes\ncoming into play.\n\nThis utility visualizes an IR module as nodes and edges. It def [...]
]
},
{
@@ -69,7 +69,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customize Parser for Interested Relay Types\n-------------------------------------------\nSometimes we want to emphasize interested information, or parse things differently for a specific usage.\nIt is possible to provide customized parsers as long as it obeys the interface.\nHere demostrate how to customize parsers for ``relay.var``.\nWe need to implement abstract interface :py:class:`tvm.contrib.relay_viz.interface.VizParser`.\n\n"
+ "Customize Parser for Interested Relay Types\n-------------------------------------------\nSometimes we want to emphasize interested information, or parse things differently for a specific usage.\nIt is possible to provide customized parsers as long as it obeys the interface.\nHere demonstrate how to customize parsers for ``relay.var``.\nWe need to implement abstract interface :py:class:`tvm.contrib.relay_viz.interface.VizParser`.\n\n"
]
},
{
@@ -116,7 +116,7 @@
},
"outputs": [],
"source": [
- "class AwesomeGraph(TermGraph):\n def node(self, viz_node):\n # add the node first\n super().node(viz_node)\n # if it's AwesomeVar, duplicate it.\n if viz_node.type_name == \"AwesomeVar\":\n duplicated_id = f\"duplciated_{viz_node.identity}\"\n duplicated_type = \"double AwesomeVar\"\n super().node(VizNode(duplicated_id, duplicated_type, \"\"))\n # connect the duplicated var to the original one\n [...]
+ "class AwesomeGraph(TermGraph):\n def node(self, viz_node):\n # add the node first\n super().node(viz_node)\n # if it's AwesomeVar, duplicate it.\n if viz_node.type_name == \"AwesomeVar\":\n duplicated_id = f\"duplicated_{viz_node.identity}\"\n duplicated_type = \"double AwesomeVar\"\n super().node(VizNode(duplicated_id, duplicated_type, \"\"))\n # connect the duplicated var to the original one\n [...]
]
},
{
diff --git a/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb b/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb
index 3e40008cb..fee27fd3c 100644
--- a/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb
+++ b/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nBlitz Course to TensorIR\n========================\n**Author**: `Siyuan Feng <https://github.com/Hzfengsy>`_\n\nTensorIR is a domain specific language for deep learning programs serving two broad purposes:\n\n- An implementation for transforming and optimizing programs on various hardware backends.\n\n- An abstraction for automatic tensorized program optimization.\n"
+ "\n\nBlitz Course to TensorIR\n========================\n**Author**: `Siyuan Feng <https://github.com/Hzfengsy>`_\n\nTensorIR is a domain specific language for deep learning programs serving two broad purposes:\n\n- An implementation for transforming and optimizing programs on various hardware backends.\n\n- An abstraction for automatic _tensorized_ program optimization.\n"
]
},
{
@@ -112,7 +112,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Transform an IRModule\n---------------------\nThe IRModule is the central data structure for program optimization, which can be transformed\nby :code:`Schedule`.\nA schedule contains multiple primitive methods to interactively transform the program.\nEach primitive transforms the program in certain ways to bring additional performance optimizations.\n\n\n\n :align: center\n [...]
+ "Transform an IRModule\n---------------------\nThe IRModule is the central data structure for program optimization, which can be transformed\nby :code:`Schedule`.\nA schedule contains multiple primitive methods to interactively transform the program.\nEach primitive transforms the program in certain ways to bring additional performance optimizations.\n\n\n\n :align: center\n [...]
]
},
{
@@ -141,7 +141,7 @@
},
"outputs": [],
"source": [
- "# Get block by its name\nblock_b = sch.get_block(\"B\")\n# Get loops surronding the block\n(i,) = sch.get_loops(block_b)\n# Tile the loop nesting.\ni_0, i_1, i_2 = sch.split(i, factors=[2, 2, 2])\nprint(sch.mod.script())"
+ "# Get block by its name\nblock_b = sch.get_block(\"B\")\n# Get loops surrounding the block\n(i,) = sch.get_loops(block_b)\n# Tile the loop nesting.\ni_0, i_1, i_2 = sch.split(i, factors=[2, 2, 2])\nprint(sch.mod.script())"
]
},
{
diff --git a/docs/_downloads/caa649473e845a115a0397a2855fd356/low_level_custom_pass.py b/docs/_downloads/caa649473e845a115a0397a2855fd356/low_level_custom_pass.py
index 8f6310754..ee96d8220 100644
--- a/docs/_downloads/caa649473e845a115a0397a2855fd356/low_level_custom_pass.py
+++ b/docs/_downloads/caa649473e845a115a0397a2855fd356/low_level_custom_pass.py
@@ -129,7 +129,7 @@ def vectorize(f, mod, ctx):
tvm.tir.stmt_functor.post_order_visit(f.body, find_width8)
if not loops:
- return sf
+ return f
# The last list arugment indicates what kinds of nodes will be transformed.
# Thus, in this case only `For` nodes will call `vectorize8`
diff --git a/docs/_downloads/cb089f2129f9829a01cc54eb81528811/using_relay_viz.py b/docs/_downloads/cb089f2129f9829a01cc54eb81528811/using_relay_viz.py
index 10e6dab12..b0132f40b 100644
--- a/docs/_downloads/cb089f2129f9829a01cc54eb81528811/using_relay_viz.py
+++ b/docs/_downloads/cb089f2129f9829a01cc54eb81528811/using_relay_viz.py
@@ -22,7 +22,7 @@ Use Relay Visualizer to Visualize Relay
Relay IR module can contain lots of operations. Although an individual
operation is usually easy to understand, putting them together can cause
-a complicated, hard-to-read graph. Things can get even worse with optimiztion-passes
+a complicated, hard-to-read graph. Things can get even worse with optimization-passes
coming into play.
This utility visualizes an IR module as nodes and edges. It defines a set of interfaces including
@@ -89,7 +89,7 @@ viz.render()
# -------------------------------------------
# Sometimes we want to emphasize interested information, or parse things differently for a specific usage.
# It is possible to provide customized parsers as long as it obeys the interface.
-# Here demostrate how to customize parsers for ``relay.var``.
+# Here demonstrate how to customize parsers for ``relay.var``.
# We need to implement abstract interface :py:class:`tvm.contrib.relay_viz.interface.VizParser`.
class YourAwesomeParser(VizParser):
def __init__(self):
@@ -131,7 +131,7 @@ class AwesomeGraph(TermGraph):
super().node(viz_node)
# if it's AwesomeVar, duplicate it.
if viz_node.type_name == "AwesomeVar":
- duplicated_id = f"duplciated_{viz_node.identity}"
+ duplicated_id = f"duplicated_{viz_node.identity}"
duplicated_type = "double AwesomeVar"
super().node(VizNode(duplicated_id, duplicated_type, ""))
# connect the duplicated var to the original one
diff --git a/docs/_downloads/d0a1817b910da958b41d88afe4d4952d/use_pass_instrument.py b/docs/_downloads/d0a1817b910da958b41d88afe4d4952d/use_pass_instrument.py
index 3369304a6..036aa63e3 100644
--- a/docs/_downloads/d0a1817b910da958b41d88afe4d4952d/use_pass_instrument.py
+++ b/docs/_downloads/d0a1817b910da958b41d88afe4d4952d/use_pass_instrument.py
@@ -30,7 +30,7 @@ instances to :py:class:`tvm.transform.PassContext`. We provide a pass instrument
for collecting timing information (:py:class:`tvm.ir.instrument.PassTimingInstrument`),
but an extension mechanism is available via the :py:func:`tvm.instrument.pass_instrument` decorator.
-This tutorial demostrates how developers can use ``PassContext`` to instrument
+This tutorial demonstrates how developers can use ``PassContext`` to instrument
passes. Please also refer to the :ref:`pass-infra`.
"""
import tvm
@@ -314,7 +314,7 @@ except ValueError as ex:
print("Catching", str(ex).split("\n")[-1])
###############################################################################
-# Exceptions occured in ``should_run``, ``run_before_pass``, ``run_after_pass``
+# Exceptions occurred in ``should_run``, ``run_before_pass``, ``run_after_pass``
# are not handled explicitly -- we rely on the context manager (the ``with`` syntax)
# to exit ``PassContext`` safely.
#
diff --git a/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb b/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb
index 80e1d23cb..08ebd6c93 100644
--- a/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb
+++ b/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "def vectorize8(op):\n \"\"\"Split can vectorize the loops found in `find_width8`.\"\"\"\n if op in loops:\n extent = op.extent.value\n name = op.loop_var.name\n lo, li = te.var(name + \".outer\"), te.var(name + \".inner\")\n body = tvm.tir.stmt_functor.substitute(op.body, {op.loop_var: lo * 8 + li})\n body = tvm.tir.For(li, 0, 8, tvm.tir.ForKind.VECTORIZED, body)\n body = tvm.tir.For(lo, 0, extent // 8, tvm.tir.ForKind.SERIAL, body [...]
+ "def vectorize8(op):\n \"\"\"Split can vectorize the loops found in `find_width8`.\"\"\"\n if op in loops:\n extent = op.extent.value\n name = op.loop_var.name\n lo, li = te.var(name + \".outer\"), te.var(name + \".inner\")\n body = tvm.tir.stmt_functor.substitute(op.body, {op.loop_var: lo * 8 + li})\n body = tvm.tir.For(li, 0, 8, tvm.tir.ForKind.VECTORIZED, body)\n body = tvm.tir.For(lo, 0, extent // 8, tvm.tir.ForKind.SERIAL, body [...]
]
},
{
diff --git a/docs/_downloads/d9089082842c138d4c81335f88c60c82/intrin_math.py b/docs/_downloads/d9089082842c138d4c81335f88c60c82/intrin_math.py
index 92383b90a..535563bfb 100644
--- a/docs/_downloads/d9089082842c138d4c81335f88c60c82/intrin_math.py
+++ b/docs/_downloads/d9089082842c138d4c81335f88c60c82/intrin_math.py
@@ -26,7 +26,7 @@ For example :code:`exp` to take the exponential of the function.
These functions are target system dependent and may have different
names of different target platforms. In this tutorial, we will learn
how we can invoke these target specific functions, and how we can unify
-the interface via tvm's intrinsic API.
+the interface via TVM's intrinsic API.
"""
from __future__ import absolute_import, print_function
import numpy as np
diff --git a/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py b/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py
index d76baec1e..fcffbd77f 100644
--- a/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py
+++ b/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py
@@ -314,7 +314,7 @@ output = layers[-1]
# Compile and run with TVM
# ------------------------
#
-# Export the weigths from PyTorch model to Python Dict
+# Export the weights from PyTorch model to Python Dict
model_params = {}
for param_tensor in torch_model.state_dict():
model_params[param_tensor] = torch_model.state_dict()[param_tensor].numpy()
diff --git a/docs/_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py b/docs/_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py
index 9f3a6070c..b9f89f672 100644
--- a/docs/_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py
+++ b/docs/_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py
@@ -78,13 +78,13 @@ def matmul_add(N, L, M, dtype):
# ----------------------
# With the function defined, we can now create the task for the auto_scheduler
# to search against. We specify the particular parameters for this matrix
-# multiplication, in this case a multiplication of to square matricies of size
+# multiplication, in this case a multiplication of two square matrices of size
# 1024x1024. We then create a search task with N=L=M=1024 and dtype="float32"
#
# .. admonition:: Improve performance with custom targets
#
# In order for TVM to take full advantage of specific hardware platforms,
-# you will want to manuall specify your CPU capabilities. For example:
+# you will want to manually specify your CPU capabilities. For example:
#
# - replace ``llvm`` below with ``llvm -mcpu=core-avx2`` to enable AVX2
# - replace ``llvm`` below with ``llvm -mcpu=skylake-avx512`` to enable AVX-512
diff --git a/docs/_downloads/ee99205e9f2e4f54c0fb7925008a5354/bring_your_own_datatypes.py b/docs/_downloads/ee99205e9f2e4f54c0fb7925008a5354/bring_your_own_datatypes.py
index 018245609..1a48781e2 100644
--- a/docs/_downloads/ee99205e9f2e4f54c0fb7925008a5354/bring_your_own_datatypes.py
+++ b/docs/_downloads/ee99205e9f2e4f54c0fb7925008a5354/bring_your_own_datatypes.py
@@ -313,7 +313,7 @@ except tvm.TVMError as e:
print(str(e).split("\n")[-1])
######################################################################
-# When we attempt to run the model, we get a familiar error telling us that more functions need to be registerd for myfloat.
+# When we attempt to run the model, we get a familiar error telling us that more functions need to be registered for myfloat.
#
# Because this is a neural network, many more operations are required.
# Here, we register all the needed functions:
diff --git a/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb b/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb
index 4c4d92611..b1b6f6a03 100644
--- a/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb
+++ b/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nHow to Use TVM Pass Instrument\n==============================\n**Author**: `Chi-Wei Wang <https://github.com/chiwwang>`_\n\nAs more and more passes are implemented, it becomes useful to instrument\npass execution, analyze per-pass effects, and observe various events.\n\nWe can instrument passes by providing a list of :py:class:`tvm.ir.instrument.PassInstrument`\ninstances to :py:class:`tvm.transform.PassContext`. We provide a pass instrument\nfor collecting timing informati [...]
+ "\n\nHow to Use TVM Pass Instrument\n==============================\n**Author**: `Chi-Wei Wang <https://github.com/chiwwang>`_\n\nAs more and more passes are implemented, it becomes useful to instrument\npass execution, analyze per-pass effects, and observe various events.\n\nWe can instrument passes by providing a list of :py:class:`tvm.ir.instrument.PassInstrument`\ninstances to :py:class:`tvm.transform.PassContext`. We provide a pass instrument\nfor collecting timing informati [...]
]
},
{
@@ -231,7 +231,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Exceptions occured in ``should_run``, ``run_before_pass``, ``run_after_pass``\nare not handled explicitly -- we rely on the context manager (the ``with`` syntax)\nto exit ``PassContext`` safely.\n\nWe use ``run_before_pass`` as an example:\n\n"
+ "Exceptions occurred in ``should_run``, ``run_before_pass``, ``run_after_pass``\nare not handled explicitly -- we rely on the context manager (the ``with`` syntax)\nto exit ``PassContext`` safely.\n\nWe use ``run_before_pass`` as an example:\n\n"
]
},
{
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index f706b2d7a..ea5a086df 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -98,7 +98,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip5dbcd572-c086-45f1-81d8-9ca43debd0e0 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip69be23b3-820e-43a1-95fd-b3934a3c4108 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
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 5bbcffcc4..6442de675 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -100,7 +100,7 @@ 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
-
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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 907bb8a54..9cfc80339 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -210,7 +210,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 5.539 seconds)
+ **Total running time of the script:** ( 1 minutes 8.144 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 63693e99f..9ae50a3df 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -79,7 +79,7 @@ Load a pretrained PyTorch model
.. code-block:: none
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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+
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83%|########3 | 37.1M/44.7M [00:00<00:00, 144MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 135MB/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 0920e0b80..42ef184ab 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -381,7 +381,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 0.224 seconds)
+ **Total running time of the script:** ( 1 minutes 2.154 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 4ef230b4e..5a3fb5e72 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
@@ -5,15 +5,15 @@
Computation times
=================
-**05:36.540** total execution time for **how_to_compile_models** files:
+**05:29.087** total execution time for **how_to_compile_models** files:
-- **01:05.539**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **01:00.224**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:57.002**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:40.945**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:31.870**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
-- **00:22.716**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:21.762**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:19.043**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:14.532**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:02.906**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:08.144**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **01:02.154**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:58.529**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:34.150**: :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)
+- **00:24.336**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:22.939**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:21.806**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:20.351**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:14.185**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:02.493**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
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 91bc13f18..ad81ab6ae 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
@@ -402,7 +402,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.2326 15.9737 16.9055 15.8591 0.4070
+ 16.0267 15.9703 16.3084 15.9199 0.1226
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 caf61b210..374409321 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
@@ -108,7 +108,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
.. code-block:: none
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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/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -262,7 +262,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 58.760 seconds)
+ **Total running time of the script:** ( 2 minutes 59.421 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 1c20c2a88..142746084 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -187,7 +187,7 @@ training. Other models require a full post training calibration.
.. code-block:: none
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
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72%|#######1 | 9.72M/13.6M [00:00<00:00, 49.9MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 65.0MB/s]
+
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51%|##### | 6.87M/13.6M [00:00<00:00, 20.6MB/s]
86%|########6 | 11.7M/13.6M [00:00<00:00, 30.4MB/s]
100%|##########| 13.6M/13.6M [00:00<00:00, 30.5MB/s]
@@ -353,7 +353,7 @@ 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)
- 89.7738 89.6539 91.2964 89.0311 0.5350
+ 90.5046 90.2703 100.2387 90.1407 1.1541
@@ -393,7 +393,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 6.339 seconds)
+ **Total running time of the script:** ( 1 minutes 7.039 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 db62c932a..00031c5f0 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
@@ -360,7 +360,7 @@ 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)
- 117.8574 117.7878 123.0413 117.0000 0.6198
+ 120.5413 120.4076 126.1949 119.6308 0.7128
@@ -394,7 +394,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 51.540 seconds)
+ **Total running time of the script:** ( 1 minutes 58.943 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
index 95bcab31f..320a5d176 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -223,7 +223,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 12.855 seconds)
+ **Total running time of the script:** ( 1 minutes 12.976 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_sparse.rst.txt b/docs/_sources/how_to/deploy_models/deploy_sparse.rst.txt
index de02f9c7e..f0d86a8b9 100644
--- a/docs/_sources/how_to/deploy_models/deploy_sparse.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_sparse.rst.txt
@@ -28,11 +28,11 @@ different types of sparsity: **structured** and **unstructured**.
Pruning is a technique primarily used to reduce the parameter size of a model
by replacing weight values with 0s. Although many methods exist for choosing which
-weights should be set to 0, the most straight forward is by picking the
+weights should be set to 0, the most straight forward is by picking the
weights with the smallest value. Typically, weights are pruned to a desired
sparsity percentage. For example, a 95% sparse model would have only 5% of
its weights non-zero. Pruning to very high sparsities often requires
-finetuning or full retraining as it tends to be a lossy approximation.
+fine-tuning or full retraining as it tends to be a lossy approximation.
Although parameter size benefits are quite easy to obtain from a pruned model
through simple compression, leveraging sparsity to yield runtime speedups
is more complicated.
@@ -42,8 +42,8 @@ pruned weights together. In other words, they are pruned using both their
value and location. The benefit of bunching up pruned weights is that it allows
an algorithm such as matrix multiplication to skip entire blocks. It turns out
that some degree of *block sparsity* is very important to realizing significant
-speedups on most hardware available today.
-This is because when loading memory in most CPUs or GPUs,
+speedups on most hardware available today.
+This is because when loading memory in most CPUs or GPUs,
it doesn't save any work to skip reading a single value at a time, instead an entire
chunk or tile is read in and executed using something like vectorized instructions.
diff --git a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
index 101e3217f..091714297 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -137,7 +137,7 @@ Convert and compile model for CPU.
data: None
input_sym_arg_type = in_param.infer_type()[0]
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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@@ -211,7 +211,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 19.378 seconds)
+ **Total running time of the script:** ( 2 minutes 18.823 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.py:
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 2ea7265e1..78b8415f6 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
@@ -5,13 +5,13 @@
Computation times
=================
-**10:20.023** total execution time for **how_to_deploy_models** files:
+**10:29.094** total execution time for **how_to_deploy_models** files:
-- **02:58.760**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:19.378**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **01:51.540**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:12.855**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:06.339**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:28.900**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:22.056**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.197**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **02:59.421**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:18.823**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:58.943**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:12.976**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:07.039**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:29.058**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:22.634**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.201**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index bdfa411a1..c5d8ae9d1 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -425,7 +425,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipf72c5caa-2564-4689-b8f2-56e2ea6329cf from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip5f463e3d-4b2a-4d5e-966b-aca87eb90c16 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
@@ -527,11 +527,11 @@ Now, to actually convert the entire network, we have written `a pass in Relay <h
.. code-block:: none
- Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
+ Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
-When we attempt to run the model, we get a familiar error telling us that more functions need to be registerd for myfloat.
+When we attempt to run the model, we get a familiar error telling us that more functions need to be registered for myfloat.
Because this is a neural network, many more operations are required.
Here, we register all the needed functions:
diff --git a/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt b/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt
index 70bd276ca..0ff5d0cd1 100644
--- a/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/low_level_custom_pass.rst.txt
@@ -169,7 +169,7 @@ this value.
tvm.tir.stmt_functor.post_order_visit(f.body, find_width8)
if not loops:
- return sf
+ return f
# The last list arugment indicates what kinds of nodes will be transformed.
# Thus, in this case only `For` nodes will call `vectorize8`
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 fabe0a596..19d51daf2 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
@@ -5,9 +5,9 @@
Computation times
=================
-**00:40.821** total execution time for **how_to_extend_tvm** files:
+**00:40.261** total execution time for **how_to_extend_tvm** files:
-- **00:36.996**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.456**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.156**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:36.576**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.362**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.109**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
- **00:00.213**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_infra.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_infra.rst.txt
index ce5d7099f..afa2747d7 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_infra.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_infra.rst.txt
@@ -26,7 +26,7 @@ on a Relay/tir program where the dependencies between passes can be resolved by
pass infra. For more details about each type of these passes, please refer to
the :ref:`pass-infra`
-This tutorial mainly demostrates how developers can use the pass infra to perform
+This tutorial mainly demonstrates how developers can use the pass infra to perform
a certain optimization and create an optimization pipeline for a Relay program.
The same approach can be used for tir as well.
@@ -163,7 +163,7 @@ eliminate the common expressions that used by `z` and `z1`.
-Some optimizations, such as fusion, are parameteric as well. For example,
+Some optimizations, such as fusion, are parametric as well. For example,
opt level 0 will not allow operators to be fused together. Users can pass the
`fuse_opt_level` to enable this.
@@ -222,7 +222,7 @@ Relay, hence, provides :py:class:`tvm.transform.Sequential` to alleviate develop
these issues explicitly by specifying the required passes of each pass and
packing them as a whole to execute. For example, the same passes can now be
applied using the sequential style as the following. :py:class:`tvm.transform.Sequential` is
-similiar to `torch.nn.sequential <https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential>`_
+similar to `torch.nn.sequential <https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential>`_
and `mxnet.gluon.block <https://mxnet.apache.org/api/python/docs/_modules/mxnet/gluon/block.html>`_.
For example, `torch.nn.sequential` is used to contain a sequence of PyTorch
`Modules` that will be added to build a network. It focuses on the network
@@ -626,7 +626,7 @@ Summary
-------
This tutorial has covered how we can write and invoke passes in TVM more
conveniently using the pass infra. Different ways of invoking a pass are also
-disucssed. Using :py:class:`tvm.transform.Sequential` can largely help
+discussed. Using :py:class:`tvm.transform.Sequential` can largely help
users to ease the work of handling multiple optimization passes and their
dependencies. In addition, an example is provided to illustrate
how we can debug a pass using the ``PrintIR`` and tracing.
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 3ea116479..2bd81d7a9 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
@@ -21,7 +21,7 @@ instances to :py:class:`tvm.transform.PassContext`. We provide a pass instrument
for collecting timing information (:py:class:`tvm.ir.instrument.PassTimingInstrument`),
but an extension mechanism is available via the :py:func:`tvm.instrument.pass_instrument` decorator.
-This tutorial demostrates how developers can use ``PassContext`` to instrument
+This tutorial demonstrates how developers can use ``PassContext`` to instrument
passes. Please also refer to the :ref:`pass-infra`.
@@ -199,10 +199,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 6572us [6572us] (45.25%; 45.25%)
- FoldScaleAxis: 7953us [6us] (54.75%; 54.75%)
- FoldConstant: 7946us [1582us] (54.71%; 99.92%)
- InferType: 6364us [6364us] (43.82%; 80.09%)
+ InferType: 6541us [6541us] (44.78%; 44.78%)
+ FoldScaleAxis: 8065us [6us] (55.22%; 55.22%)
+ FoldConstant: 8059us [1554us] (55.18%; 99.93%)
+ InferType: 6505us [6505us] (44.54%; 80.72%)
@@ -239,10 +239,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 6396us [6396us] (44.75%; 44.75%)
- FoldScaleAxis: 7897us [6us] (55.25%; 55.25%)
- FoldConstant: 7891us [1621us] (55.21%; 99.92%)
- InferType: 6270us [6270us] (43.87%; 79.46%)
+ InferType: 6270us [6270us] (44.51%; 44.51%)
+ FoldScaleAxis: 7816us [5us] (55.49%; 55.49%)
+ FoldConstant: 7811us [1613us] (55.45%; 99.94%)
+ InferType: 6198us [6198us] (44.00%; 79.35%)
@@ -595,7 +595,7 @@ after the one throwing the exception do not execute ``exit_pass_ctx``.
-Exceptions occured in ``should_run``, ``run_before_pass``, ``run_after_pass``
+Exceptions occurred in ``should_run``, ``run_before_pass``, ``run_after_pass``
are not handled explicitly -- we rely on the context manager (the ``with`` syntax)
to exit ``PassContext`` safely.
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 3f8022e1d..d9ea20a0d 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
@@ -95,7 +95,7 @@ critical to achieve high performance in GPU kernels.
:width: 271px
In this example, we load both Apad and W into buffer AA and WW, which are
-stored in the shared memory. These bufferes will be later shared by all
+stored in the shared memory. These buffers will be later shared by all
threads within the same thread block to compute the convolution. Each thread
then loads its own part from shared buffer into their local registers, AL and
WL. BL is a local cache of output B, which is also stored in the thread local
@@ -295,7 +295,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 54.181335 ms
+ Convolution: 54.131947 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 f42f5cbf2..659653107 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
@@ -321,7 +321,7 @@ one time.
*Warp-level Operation*
Note that all TensorCore instructions are warp-level instructions, which means all 32 threads
- in a warp should do this instruction simultaneously. Making theadIdx.x extent=32 is one of the
+ in a warp should do this instruction simultaneously. Making threadIdx.x extent=32 is one of the
easiest way to solve this. Then We can bind threadIdx.x to any loops except those contain
TensorCore intrinsics directly or indirectly. Also note that it is not the unique solution.
The only thing we should do is to make sure all threads in a warp can call TensorCore at the same time.
@@ -628,7 +628,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 7.453295 ms
+ conv2d with tensor core: 6.993976 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 27bb74a7d..6b7c315ef 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.018660
- Baseline: 3.258906
+ Numpy running time: 0.019189
+ Baseline: 3.452864
@@ -210,7 +210,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.315168
+ Opt1: 0.300515
@@ -309,7 +309,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.341248
+ Opt2: 0.334529
@@ -401,7 +401,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.118415
+ Opt3: 0.116622
@@ -520,7 +520,7 @@ flattening.
.. code-block:: none
- Opt4: 0.110422
+ Opt4: 0.110502
@@ -608,7 +608,7 @@ write to C when all the block results are ready.
s[CC].reorder(ko, mc, ki, nc)
s[CC].vectorize(nc)
- # TODO: Add separate optimization step to discuss loop unrolloing
+ # TODO: Add separate optimization step to discuss loop unrolling
# unrolling is a loop optimization strategy which can reduce branch
# prediction failures and increases the chance of concurrent execution
# unroll kfactor loops
@@ -638,7 +638,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.110941
+ Opt5: 0.111951
@@ -759,7 +759,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.145064
+ Opt6: 0.145564
@@ -834,7 +834,7 @@ After applying the above simple optimizations with only 18 lines of code,
our generated code can achieve 60% of the `numpy` performance with MKL.
Note that the outputs on the web page reflect the running times on a non-exclusive
Docker container, thereby they are *unreliable*. It is highly encouraged to run the
-tutorial by yourself to observe the performance gain acheived by TVM.
+tutorial by yourself to observe the performance gain achieved by TVM.
.. _sphx_glr_download_how_to_optimize_operators_opt_gemm.py:
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 22a4886cd..c5e0c601f 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
@@ -5,8 +5,8 @@
Computation times
=================
-**00:34.855** total execution time for **how_to_optimize_operators** files:
+**00:35.355** total execution time for **how_to_optimize_operators** files:
-- **00:32.178**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.456**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.221**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:32.584**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.479**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.291**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
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 e28f51fd9..211d8c22c 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
@@ -5,11 +5,11 @@
Computation times
=================
-**05:13.382** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:34.359**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:20.451**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:43.345**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:17.691**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:08.877**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.657**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**05:11.293** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:32.762**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:20.411**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:43.020**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:17.522**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:08.978**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:08.601**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
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 a80baaa78..9f76cef8b 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
@@ -751,7 +751,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.360 ms
+ Execution time of this operator: 0.358 ms
@@ -1351,7 +1351,7 @@ In the example below we resume the status and do more 5 trials.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 34.359 seconds)
+ **Total running time of the script:** ( 2 minutes 32.762 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py:
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 35078b6ed..d5ef784a0 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
@@ -616,7 +616,7 @@ 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)
- 9.8982 9.9180 9.9450 9.8317 0.0483
+ 9.8067 9.8261 9.8339 9.7602 0.0331
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 1e10e6de7..2e6303fc0 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
@@ -635,7 +635,7 @@ 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)
- 747.8577 749.1900 749.2567 745.1264 1.9315
+ 755.5129 755.9471 756.3586 754.2330 0.9205
@@ -660,7 +660,7 @@ Other Tips
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 20.451 seconds)
+ **Total running time of the script:** ( 1 minutes 20.411 seconds)
.. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
index e54f567cb..85fb3ad1c 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
@@ -362,75 +362,77 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_6: placeholder_15: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_16: Buffer(placeholder_13, int32, [33], []), placeholder_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 256) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [256]), storage_scope = global {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 8) {
- let cse_var_1: int32 = ((i.inner.init*32) + (nb_j.inner*16))
- {
- compute_5: Buffer(compute_4, float32, [256], [])[cse_var_1] = 0f32
- compute_5[(cse_var_1 + 1)] = 0f32
- compute_5[(cse_var_1 + 2)] = 0f32
- compute_5[(cse_var_1 + 3)] = 0f32
- compute_5[(cse_var_1 + 4)] = 0f32
- compute_5[(cse_var_1 + 5)] = 0f32
- compute_5[(cse_var_1 + 6)] = 0f32
- compute_5[(cse_var_1 + 7)] = 0f32
- compute_5[(cse_var_1 + 8)] = 0f32
- compute_5[(cse_var_1 + 9)] = 0f32
- compute_5[(cse_var_1 + 10)] = 0f32
- compute_5[(cse_var_1 + 11)] = 0f32
- compute_5[(cse_var_1 + 12)] = 0f32
- compute_5[(cse_var_1 + 13)] = 0f32
- compute_5[(cse_var_1 + 14)] = 0f32
- compute_5[(cse_var_1 + 15)] = 0f32
- }
- }
- for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 8) {
- let cse_var_21: int32 = (elem_idx*16)
- let cse_var_20: int32 = ((i.inner*32) + (nb_j.inner*16))
- let cse_var_19: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
- let cse_var_18: int32 = (cse_var_20 + 1)
- let cse_var_17: int32 = (cse_var_20 + 11)
- let cse_var_16: int32 = (cse_var_20 + 12)
- let cse_var_15: int32 = (cse_var_20 + 13)
- let cse_var_14: int32 = (cse_var_20 + 14)
- let cse_var_13: int32 = (cse_var_20 + 15)
- let cse_var_12: int32 = (cse_var_20 + 2)
- let cse_var_11: int32 = (cse_var_20 + 3)
- let cse_var_10: int32 = (cse_var_20 + 4)
- let cse_var_9: int32 = (cse_var_20 + 5)
- let cse_var_8: int32 = (cse_var_20 + 6)
- let cse_var_7: int32 = (cse_var_20 + 7)
- let cse_var_6: int32 = (cse_var_20 + 8)
- let cse_var_5: int32 = (cse_var_20 + 9)
- let cse_var_4: int32 = ((floordiv(i0.outer.i1.outer.fused, 16)*2048) + (i.inner*256))
- let cse_var_3: int32 = (cse_var_20 + 10)
+ preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 16) {
+ for (nb_j.inner: int32, 0, 2) {
+ for (i.inner.init: int32, 0, 4) {
+ let cse_var_1: int32 = (((i.outer.inner*128) + (i.inner.init*32)) + (nb_j.inner*16))
{
- compute_5[cse_var_20] = (compute_5[cse_var_20] + (placeholder_1[((placeholder_3[cse_var_19]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
+ compute_5: Buffer(compute_4, float32, [2048], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
+ }
+ }
+ for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 4) {
+ let cse_var_21: int32 = (elem_idx*16)
+ let cse_var_20: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+ let cse_var_19: int32 = (((i.outer.inner*128) + (i.inner*32)) + (nb_j.inner*16))
+ let cse_var_18: int32 = (cse_var_19 + 1)
+ let cse_var_17: int32 = (cse_var_19 + 11)
+ let cse_var_16: int32 = (cse_var_19 + 12)
+ let cse_var_15: int32 = (cse_var_19 + 13)
+ let cse_var_14: int32 = (cse_var_19 + 14)
+ let cse_var_13: int32 = (cse_var_19 + 15)
+ let cse_var_12: int32 = (cse_var_19 + 2)
+ let cse_var_11: int32 = (cse_var_19 + 3)
+ let cse_var_10: int32 = (cse_var_19 + 4)
+ let cse_var_9: int32 = (cse_var_19 + 5)
+ let cse_var_8: int32 = (cse_var_19 + 6)
+ let cse_var_7: int32 = (cse_var_19 + 7)
+ let cse_var_6: int32 = (cse_var_19 + 8)
+ let cse_var_5: int32 = (cse_var_19 + 9)
+ let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.outer.inner*1024)) + (i.inner*256))
+ let cse_var_3: int32 = (cse_var_19 + 10)
+ {
+ compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ }
}
}
}
}
- for (i0.inner: int32, 0, 8) {
- let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
+ for (i0.inner: int32, 0, 64) {
+ let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
compute[ramp(cse_var_22, 1, 32)] = max((compute_5[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
}
}
@@ -485,7 +487,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 1.884 ms
+ Execution time of this operator: 2.160 ms
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 7c156a32a..9438f3c1a 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
@@ -5,10 +5,10 @@
Computation times
=================
-**00:44.438** total execution time for **how_to_tune_with_autotvm** files:
+**00:45.767** total execution time for **how_to_tune_with_autotvm** files:
-- **00:43.546**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.234**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.223**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.218**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.216**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:44.888**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.237**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.216**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.215**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.212**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
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 5b5f2baaa..ce648b8a6 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
@@ -73,7 +73,7 @@ to search through this space and pick some good configurations.
If you are familiar with writing cuda schedule, you can find the following
template is very general. Actually this template can be easily modified
-to tune other operators such as depthwise convolution and gemm.
+to tune other operators such as depthwise convolution and GEMM.
In order to fully understand this template, you should be familiar with
the schedule primitives and auto tuning API. You can refer to the above
tutorials and :ref:`autotvm tutorial <tutorial-autotvm-matmul-x86>`
@@ -859,8 +859,8 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
- No: 6 GFLOPS: 95.92/95.92 result: MeasureResult(costs=(0.0024134619583333337,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6140697002410889, timestamp=1654644371.017709) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
- No: 7 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 6 GFLOPS: 42.64/42.64 result: MeasureResult(costs=(0.005429114789473685,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5012328624725342, timestamp=1654669018.3772974) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+ No: 7 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -983,7 +983,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
- No: 8 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 8 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1106,7 +1106,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
- No: 9 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 9 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1229,7 +1229,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
- No: 10 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 10 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1247,7 +1247,7 @@ for this template
TimeoutError
[('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
- No: 11 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 11 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1370,7 +1370,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
- No: 12 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 12 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1493,7 +1493,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
- No: 13 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 13 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1616,7 +1616,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
- No: 14 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 14 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1739,7 +1739,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
- No: 15 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 15 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1862,7 +1862,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
- No: 16 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 16 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1985,7 +1985,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
- No: 17 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 17 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2108,7 +2108,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
- No: 18 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 18 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2231,7 +2231,7 @@ for this template
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
- No: 19 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+ No: 19 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
yield remote, remote.load_module(os.path.split(build_result.filename)[1])
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2319,7 +2319,7 @@ for this template
15: _PyEval_EvalFrameDefault
14: 0x0000000000537c30
13: _PyObject_FastCallKeywords
- 12: 0x00007fb37c553fa2
+ 12: 0x00007f46e1a5dfa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2384,7 +2384,7 @@ for this template
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
- No: 20 GFLOPS: 144.97/144.97 result: MeasureResult(costs=(0.00159690949,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.464625597000122, timestamp=1654644397.5587177) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+ No: 20 GFLOPS: 144.29/144.29 result: MeasureResult(costs=(0.0016044719299999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4524359703063965, timestamp=1654669045.0340207) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
@@ -2437,7 +2437,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
- Time cost of this operator: 0.001964
+ Time cost of this operator: 0.001957
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 67ca71b2c..6b73d5771 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
@@ -294,10 +294,10 @@ Timing the untuned program
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 314.1 98.745 (1, 2, 10, 10, 3) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.07 0.965 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.922 0.29 (1, 1, 10, 10, 3) 1 1
- Total_time - 318.092 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 314.6 98.747 (1, 2, 10, 10, 3) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.091 0.97 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.901 0.283 (1, 1, 10, 10, 3) 1 1
+ Total_time - 318.592 - - - -
@@ -359,10 +359,10 @@ Timing the tuned program
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
- tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 154.1 98.277 (1, 6, 10, 10, 1) 2 1
- tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.738 1.108 (1, 6, 10, 10) 1 1
- tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.963 0.614 (1, 1, 10, 10, 3) 1 1
- Total_time - 156.801 - - - -
+ tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 226.2 98.8 (1, 1, 10, 10, 6) 2 1
+ tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.935 0.845 (1, 6, 10, 10) 1 1
+ tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.812 0.355 (1, 3, 10, 10, 1) 1 1
+ Total_time - 228.947 - - - -
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 7ddde433c..52e5ed91e 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
@@ -297,8 +297,8 @@ objects to other stuff? We can display some examples from our datasets using ``m
.. code-block:: none
- /tmp/tmpfxj8a1el/images/target contains 8144 images
- /tmp/tmpfxj8a1el/images/random contains 5000 images
+ /tmp/tmpv4vn17m7/images/target contains 8144 images
+ /tmp/tmpv4vn17m7/images/random contains 5000 images
@@ -459,11 +459,11 @@ the time on our validation set).
.. code-block:: none
Epoch 1/3
- 328/328 - 54s - loss: 0.2189 - accuracy: 0.9242 - val_loss: 0.1366 - val_accuracy: 0.9592
+ 328/328 - 54s - loss: 0.2052 - accuracy: 0.9296 - val_loss: 0.1563 - val_accuracy: 0.9543
Epoch 2/3
- 328/328 - 52s - loss: 0.0989 - accuracy: 0.9638 - val_loss: 0.1167 - val_accuracy: 0.9615
+ 328/328 - 52s - loss: 0.0980 - accuracy: 0.9630 - val_loss: 0.1520 - val_accuracy: 0.9585
Epoch 3/3
- 328/328 - 52s - loss: 0.0664 - accuracy: 0.9753 - val_loss: 0.1076 - val_accuracy: 0.9637
+ 328/328 - 52s - loss: 0.0603 - accuracy: 0.9768 - val_loss: 0.1156 - val_accuracy: 0.9656
@@ -825,7 +825,7 @@ 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 36.910 seconds)
+ **Total running time of the script:** ( 5 minutes 10.613 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 0e3f0d8da..a52d21073 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
@@ -5,11 +5,11 @@
Computation times
=================
-**05:22.774** total execution time for **how_to_work_with_microtvm** files:
+**05:57.419** total execution time for **how_to_work_with_microtvm** files:
-- **04:36.910**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)
-- **00:41.661**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.587**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **05:10.613**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)
+- **00:42.484**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.716**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
- **00:00.209**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
-- **00:00.206**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
-- **00:00.201**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:00.199**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
+- **00:00.199**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
diff --git a/docs/_sources/how_to/work_with_relay/build_gcn.rst.txt b/docs/_sources/how_to/work_with_relay/build_gcn.rst.txt
index 43e3aa570..e29b21236 100644
--- a/docs/_sources/how_to/work_with_relay/build_gcn.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/build_gcn.rst.txt
@@ -433,7 +433,7 @@ Put layers together
Compile and run with TVM
------------------------
-Export the weigths from PyTorch model to Python Dict
+Export the weights from PyTorch model to Python Dict
.. code-block:: default
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 5656dd918..073b488f2 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
@@ -5,8 +5,8 @@
Computation times
=================
-**00:11.906** total execution time for **how_to_work_with_relay** files:
+**00:12.095** total execution time for **how_to_work_with_relay** files:
-- **00:09.955**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.724**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.226**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:10.170**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.710**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.215**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
diff --git a/docs/_sources/how_to/work_with_relay/using_relay_viz.rst.txt b/docs/_sources/how_to/work_with_relay/using_relay_viz.rst.txt
index 6411288c8..8141838f4 100644
--- a/docs/_sources/how_to/work_with_relay/using_relay_viz.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/using_relay_viz.rst.txt
@@ -13,7 +13,7 @@ Use Relay Visualizer to Visualize Relay
Relay IR module can contain lots of operations. Although an individual
operation is usually easy to understand, putting them together can cause
-a complicated, hard-to-read graph. Things can get even worse with optimiztion-passes
+a complicated, hard-to-read graph. Things can get even worse with optimization-passes
coming into play.
This utility visualizes an IR module as nodes and edges. It defines a set of interfaces including
@@ -126,7 +126,7 @@ Customize Parser for Interested Relay Types
-------------------------------------------
Sometimes we want to emphasize interested information, or parse things differently for a specific usage.
It is possible to provide customized parsers as long as it obeys the interface.
-Here demostrate how to customize parsers for ``relay.var``.
+Here demonstrate how to customize parsers for ``relay.var``.
We need to implement abstract interface :py:class:`tvm.contrib.relay_viz.interface.VizParser`.
@@ -210,7 +210,7 @@ We add a hook duplicating above ``AwesomeVar``, and make ``TermPlotter`` use the
super().node(viz_node)
# if it's AwesomeVar, duplicate it.
if viz_node.type_name == "AwesomeVar":
- duplicated_id = f"duplciated_{viz_node.identity}"
+ duplicated_id = f"duplicated_{viz_node.identity}"
duplicated_type = "double AwesomeVar"
super().node(VizNode(duplicated_id, duplicated_type, ""))
# connect the duplicated var to the original one
diff --git a/docs/_sources/how_to/work_with_schedules/extern_op.rst.txt b/docs/_sources/how_to/work_with_schedules/extern_op.rst.txt
index d063c4c2d..a7ca2264a 100644
--- a/docs/_sources/how_to/work_with_schedules/extern_op.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/extern_op.rst.txt
@@ -17,7 +17,7 @@ the pipeline. For example, we might want to use cuDNN for
some of the convolution kernels and define the rest of the stages.
TVM supports these black box function calls natively.
-Specfically, TVM support all the tensor functions that are DLPack compatible.
+Specifically, TVM support all the tensor functions that are DLPack compatible.
Which means we can call any function with POD types(pointer, int, float)
or pointer to DLTensor as argument.
@@ -52,7 +52,7 @@ The compute function takes list of symbolic placeholder for the inputs,
list of symbolic placeholder for the outputs and returns the executing statement.
In this case we simply call a registered TVM function, which invokes a CBLAS call.
-TVM does not control internal of the extern array function and treats it as blackbox.
+TVM does not control internal of the extern array function and treats it as black-box.
We can further mix schedulable TVM calls that add a bias term to the result.
diff --git a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
index b22d798f1..eba8b4572 100644
--- a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
@@ -18,7 +18,7 @@ For example :code:`exp` to take the exponential of the function.
These functions are target system dependent and may have different
names of different target platforms. In this tutorial, we will learn
how we can invoke these target specific functions, and how we can unify
-the interface via tvm's intrinsic API.
+the interface via TVM's intrinsic API.
.. code-block:: default
diff --git a/docs/_sources/how_to/work_with_schedules/scan.rst.txt b/docs/_sources/how_to/work_with_schedules/scan.rst.txt
index 9a56294bc..8c65add04 100644
--- a/docs/_sources/how_to/work_with_schedules/scan.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/scan.rst.txt
@@ -69,7 +69,7 @@ update over the time domain.
Schedule the Scan Cell
----------------------
We can schedule the body of the scan by scheduling the update and
-init part seperately. Note that it is invalid to schedule the
+init part separately. Note that it is invalid to schedule the
first iteration dimension of the update part.
To split on the time iteration, user can schedule on scan_op.scan_axis instead.
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 89a78afa9..190a400e4 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
@@ -5,13 +5,13 @@
Computation times
=================
-**00:05.833** total execution time for **how_to_work_with_schedules** files:
+**00:05.959** total execution time for **how_to_work_with_schedules** files:
-- **00:02.134**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.198**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.745**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.729**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.312**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.251**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.239**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.225**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:02.170**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.236**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.746**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.736**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.315**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.284**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.242**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.230**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index f6bb58bf7..c50c598e1 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -318,7 +318,7 @@ The importing needs to happen before the tensorized GEMV being executed.
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [1024, 64], []), B_1: B_3: Buffer(B_2, float32, [512, 64], []), C_1: C_3: Buffer(C_2, float32, [1024, 512], [])} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpcsetdwf3/input0.cc'\nsource_filename = \"/tmp/tmpcsetdwf3/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca floa [...]
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpt78h14xo/input0.cc'\nsource_filename = \"/tmp/tmpt78h14xo/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca floa [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 8298f4c2c..f4bfa1f8b 100644
--- a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:21.462** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:21.354** total execution time for **topic_vta_tutorials_autotvm** files:
-- **00:21.250**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.212**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:21.135**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.219**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index 8db4b95ea..8cdcf08e6 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -267,7 +267,7 @@ The compilation steps are:
DeprecationWarning,
/workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the new recommended usage.
relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
- resnet18_v1 inference graph built in 22.82s!
+ resnet18_v1 inference graph built in 22.17s!
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 634175f8e..2523fc51e 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -303,7 +303,7 @@ The compilation steps are:
"target_host parameter is going to be deprecated. "
/workspace/python/tvm/relay/build_module.py:389: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
DeprecationWarning,
- yolov3-tiny inference graph built in 15.80s!
+ yolov3-tiny inference graph built in 15.55s!
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 ff0f02396..9eb8bffe3 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
@@ -5,7 +5,7 @@
Computation times
=================
-**01:31.892** total execution time for **topic_vta_tutorials_frontend** files:
+**01:30.556** total execution time for **topic_vta_tutorials_frontend** files:
-- **00:48.834**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:43.057**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:48.088**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:42.468**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
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 fc22e5f71..dcf09aa78 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
@@ -5,7 +5,7 @@
Computation times
=================
-**00:03.598** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.647** total execution time for **topic_vta_tutorials_optimize** files:
-- **00:03.019**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.579**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:03.035**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.612**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
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 7e8a28ee3..e7e4defbe 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:01.089** total execution time for **topic_vta_tutorials** files:
+**00:01.092** total execution time for **topic_vta_tutorials** files:
-- **00:00.550**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.540**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.553**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.539**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 6732bac01..7ca8307e9 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -86,13 +86,13 @@ Create the search task
----------------------
With the function defined, we can now create the task for the auto_scheduler
to search against. We specify the particular parameters for this matrix
-multiplication, in this case a multiplication of to square matricies of size
+multiplication, in this case a multiplication of two square matrices of size
1024x1024. We then create a search task with N=L=M=1024 and dtype="float32"
.. admonition:: Improve performance with custom targets
In order for TVM to take full advantage of specific hardware platforms,
- you will want to manuall specify your CPU capabilities. For example:
+ you will want to manually specify your CPU capabilities. For example:
- replace ``llvm`` below with ``llvm -mcpu=core-avx2`` to enable AVX2
- replace ``llvm`` below with ``llvm -mcpu=skylake-avx512`` to enable AVX-512
@@ -306,7 +306,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 93.499 ms
+ Execution time of this operator: 93.865 ms
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index 0e9fa028f..1e4535c07 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -20,7 +20,7 @@ can be used to write schedule templates that can be searched by AutoTVM to
find the optimal schedule. This process is called Auto-Tuning, which helps
automate the process of optimizing tensor computation.
-This tutorial builds on the previous `tutorial on how to write a matrix
+This tutorial builds on the previous :doc:`tutorial on how to write a matrix
multiplication using TE <tensor_expr_get_started>`.
There are two steps in auto-tuning.
@@ -216,7 +216,7 @@ In the previous template, we manually listed all of the possible values for a
knob. This is the lowest level API to define the space, and gives an explicit
enumeration of the parameter space to search. However, we also provide
another set of APIs that can make the definition of the search space easier
-and smarter. Where possible, we receomment you use this higher-level API
+and smarter. Where possible, we recommend you use this higher-level API
In the following example, we use :any:`ConfigSpace.define_split` to define a
split knob. It will enumerate all the possible ways to split an axis and
@@ -290,7 +290,7 @@ can always fall back to using the low level API.
Step 2: Use AutoTVM to Optimize the Matrix Multiplication
---------------------------------------------------------
In Step 1, we wrote a matrix multiplication template that allowed us to
-paramaterize the block size used in the `split` schedule. We can now conduct
+parameterize the block size used in the `split` schedule. We can now conduct
a search over this parameter space. The next step is to pick a tuner to guide
the exploration of this space.
@@ -318,7 +318,7 @@ strategies. Some of the tuner strategies provided by TVM include:
You can choose the tuner according to the size of your space, your time
budget and other factors. For example, if your space is very small (less
-than 1000), a gridsearch tuner or a random tuner is good enough. If your
+than 1000), a grid-search tuner or a random tuner is good enough. If your
space is at the level of 10^9 (this is the space size of a conv2d operator on
CUDA GPU), XGBoostTuner can explore more efficiently and find better configs.
@@ -402,7 +402,7 @@ reduce variance, we take 5 measurements and average them.
With tuning completed, we can choose the configuration from the log file that
has the best measured performance and compile the schedule with the
-corresponding parameters. We also do a quick verfication that the schedule is
+corresponding parameters. We also do a quick verification that the schedule is
producing correct answers. We can call the function :code:`matmul` directly
under the :any:`autotvm.apply_history_best` context. When we call this
function, it will query the dispatch context with its argument and get the
@@ -440,9 +440,9 @@ In this tutorial, we have shown how to build operator templates that allow
TVM to search a parameter space and choose optimized schedule configurations.
To gain a deeper understanding of how this works, we recommend expanding on
this example by adding new search parameters to the schedule based on
-schedule operations demonstated in the `Getting Started With Tensor
+schedule operations demonstrated in the :ref: `Getting Started With Tensor
Expressions <tensor_expr_get_started>_` tutorial. In the upcoming sections, we
-will demonstate the AutoScheduler, a method for TVM to optimize common
+will demonstrate the AutoScheduler, a method for TVM to optimize common
operators without the need for the user to provide a user-defined template.
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index a36aa4312..ab6177be3 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -280,7 +280,7 @@ standard deviation.
.. code-block:: none
- {'mean': 494.48566579999914, 'median': 494.308517449997, 'std': 1.0335504241642963}
+ {'mean': 494.4295934299953, 'median': 494.5921276000263, 'std': 0.5236284867723745}
@@ -494,31 +494,31 @@ 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: 17.51/ 17.51 GFLOPS | Progress: (4/20) | 5.57 s
[Task 1/25] Current/Best: 6.17/ 17.51 GFLOPS | Progress: (8/20) | 9.01 s
[Task 1/25] Current/Best: 11.55/ 22.86 GFLOPS | Progress: (12/20) | 11.42 s
[Task 1/25] Current/Best: 16.86/ 22.86 GFLOPS | Progress: (16/20) | 13.11 s
[Task 1/25] Current/Best: 11.61/ 23.88 GFLOPS | Progress: (20/20) | 14.84 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 12.32/ 12.76 GFLOPS | Progress: (4/20) | 3.58 s
[Task 2/25] Current/Best: 14.07/ 18.70 GFLOPS | Progress: (8/20) | 4.87 s
[Task 2/25] Current/Best: 20.75/ 20.75 GFLOPS | Progress: (12/20) | 6.22 s
[Task 2/25] Current/Best: 12.05/ 20.75 GFLOPS | Progress: (16/20) | 7.48 s
[Task 2/25] Current/Best: 19.43/ 20.75 GFLOPS | Progress: (20/20) | 9.01 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.58 GFLOPS | Progress: (4/20) | 5.80 s
[Task 3/25] Current/Best: 15.57/ 16.86 GFLOPS | Progress: (8/20) | 7.71 s
[Task 3/25] Current/Best: 14.89/ 16.86 GFLOPS | Progress: (12/20) | 9.43 s
[Task 3/25] Current/Best: 7.19/ 23.67 GFLOPS | Progress: (16/20) | 11.33 s
[Task 3/25] Current/Best: 12.60/ 23.67 GFLOPS | Progress: (20/20) | 15.83 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.56/ 20.42 GFLOPS | Progress: (4/20) | 2.34 s
[Task 4/25] Current/Best: 6.88/ 20.42 GFLOPS | Progress: (8/20) | 6.65 s
[Task 4/25] Current/Best: 22.16/ 22.16 GFLOPS | Progress: (12/20) | 11.05 s
[Task 4/25] Current/Best: 17.07/ 22.16 GFLOPS | Progress: (16/20) | 13.25 s
[Task 4/25] Current/Best: 13.36/ 22.16 GFLOPS | Progress: (20/20) | 15.14 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.83/ 10.40 GFLOPS | Progress: (4/20) | 2.52 s
[Task 5/25] Current/Best: 11.86/ 12.76 GFLOPS | Progress: (8/20) | 4.56 s
[Task 5/25] Current/Best: 11.88/ 18.14 GFLOPS | Progress: (12/20) | 7.48 s
[Task 5/25] Current/Best: 11.85/ 22.75 GFLOPS | Progress: (16/20) | 8.88 s
[Task 5/25] Current/Best: 12.04/ 22.75 GFLOPS | Progress: (20/20) | 10.72 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.32/ 20.75 GFLOPS | Progress: (4/20) | 3.89 s
[Task 6/25] Current/Best: 18.82/ 20.75 GFLOPS | Progress: (8/20) | 5.64 s
[Task 6/25] Current/Best: 13.26/ 20.75 GFLOPS | Progress: (12/20) | 7.56 s
[Task 6/25] Current/Best: 20.06/ 20.75 GFLOPS | Progress: (16/20) | 9.80 s
[Task 6/25] Current/Best: 3.60/ 20.75 GFLOPS | Progress: (20/20) | 12.36 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 10.35/ 12.24 GFLOPS | Progress: (4/20) | 3.61 s
[Task 7/25] Current/Best: 20.32/ 21.23 GFLOPS | Progress: (8/20) | 5.12 s
[Task 7/25] Current/Best: 16.06/ 21.23 GFLOPS | Progress: (12/20) | 7.03 s
[Task 7/25] Current/Best: 12.25/ 21.23 GFLOPS | Progress: (16/20) | 9.07 s
[Task 7/25] Current/Best: 6.38/ 21.70 GFLOPS | Progress: (20/20) | 11.54 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.49/ 14.46 GFLOPS | Progress: (4/20) | 2.88 s
[Task 8/25] Current/Best: 9.65/ 14.46 GFLOPS | Progress: (8/20) | 7.62 s
[Task 8/25] Current/Best: 12.89/ 14.46 GFLOPS | Progress: (12/20) | 13.68 s
[Task 8/25] Current/Best: 18.78/ 18.78 GFLOPS | Progress: (16/20) | 15.75 s
[Task 8/25] Current/Best: 20.31/ 20.31 GFLOPS | Progress: (20/20) | 22.24 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.27/ 14.54 GFLOPS | Progress: (4/20) | 11.94 s
[Task 9/25] Current/Best: 23.41/ 23.41 GFLOPS | Progress: (8/20) | 13.71 s
[Task 9/25] Current/Best: 8.24/ 23.41 GFLOPS | Progress: (12/20) | 16.07 s
[Task 9/25] Current/Best: 17.92/ 23.41 GFLOPS | Progress: (16/20) | 18.69 s
[Task 9/25] Current/Best: 9.04/ 23.41 GFLOPS | Progress: (20/20) | 26.39 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.29/ 18.29 GFLOPS | Progress: (4/20) | 2.48 s
[Task 10/25] Current/Best: 15.41/ 18.29 GFLOPS | Progress: (8/20) | 4.05 s
[Task 10/25] Current/Best: 12.56/ 19.12 GFLOPS | Progress: (12/20) | 5.57 s
[Task 10/25] Current/Best: 19.08/ 20.45 GFLOPS | Progress: (16/20) | 6.68 s
[Task 10/25] Current/Best: 8.94/ 20.45 GFLOPS | Progress: (20/20
) | 8.24 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 12.22/ 18.12 GFLOPS | Progress: (4/20) | 3.27 s
[Task 11/25] Current/Best: 15.49/ 18.12 GFLOPS | Progress: (8/20) | 6.04 s
[Task 11/25] Current/Best: 17.28/ 18.12 GFLOPS | Progress: (12/20) | 8.09 s
[Task 11/25] Current/Best: 12.99/ 21.15 GFLOPS | Progress: (16/20) | 10.85 s
[Task 11/25] Current/Best: 19.44/ 21.59 GFLOPS | Progress: (20/20) | 12.86 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.83/ 18.10 GFLOPS | Progress: (4/20) | 5.31 s
[Task 12/25] Current/Best: 5.26/ 18.10 GFLOPS | Progress: (8/20) | 8.97 s
[Task 12/25] Current/Best: 18.92/ 18.92 GFLOPS | Progress: (12/20) | 10.98 s
[Task 12/25] Current/Best: 15.45/ 18.92 GFLOPS | Progress: (16/20) | 13.71 s
[Task 12/25] Current/Best: 15.15/ 18.93 GFLOPS | Progress: (20/20) | 15.62 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.67/ 17.31 GFLOPS | Progress: (4/20) | 3.55 s
[Task 13/25] Current/Best: 16.05/ 20.90 GFLOPS | Progress: (8/20) | 5.96 s
[Task 13/25] Current/Best: 19.64/ 21.54 GFLOPS | Progress: (12/20) | 8.78 s
[Task 13/25] Current/Best: 12.23/ 21.54 GFLOPS | Progress: (16/20) | 12.20 s
[Task 13/25] Current/Best: 18.87/ 21.54 GFLOPS | Progress: (20/20) | 14.45 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.60/ 13.60 GFLOPS | Progress: (4/20) | 3.17 s
[Task 14/25] Current/Best: 6.09/ 13.60 GFLOPS | Progress: (8/20) | 5.32 s
[Task 14/25] Current/Best: 20.59/ 20.59 GFLOPS | Progress: (12/20) | 7.84 s
[Task 14/25] Current/Best: 16.57/ 20.59 GFLOPS | Progress: (16/20) | 9.56 s Done.
-
[Task 14/25] Current/Best: 16.99/ 20.59 GFLOPS | Progress: (20/20) | 11.33 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.08/ 17.63 GFLOPS | Progress: (4/20) | 2.60 s
[Task 15/25] Current/Best: 14.33/ 18.00 GFLOPS | Progress: (8/20) | 3.93 s
[Task 15/25] Current/Best: 10.37/ 22.22 GFLOPS | Progress: (12/20) | 5.98 s
[Task 15/25] Current/Best: 19.90/ 22.22 GFLOPS | Progress: (16/20) | 8.89 s
[Task 15/25] Current/Best: 9.68/ 22.22 GFLOPS | Progress: (20/20) | 9.86 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.74/ 20.74 GFLOPS | Progress: (4/20) | 2.85 s
[Task 16/25] Current/Best: 3.02/ 20.74 GFLOPS | Progress: (8/20) | 4.46 s
[Task 16/25] Current/Best: 19.67/ 20.74 GFLOPS | Progress: (12/20) | 5.67 s
[Task 16/25] Current/Best: 17.95/ 20.74 GFLOPS | Progress: (16/20) |
7.02 s
[Task 16/25] Current/Best: 10.02/ 22.63 GFLOPS | Progress: (20/20) | 9.03 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 11.65/ 18.99 GFLOPS | Progress: (4/20) | 4.64 s
[Task 17/25] Current/Best: 14.48/ 23.32 GFLOPS | Progress: (8/20) | 7.45 s
[Task 17/25] Current/Best: 16.90/ 23.32 GFLOPS | Progress: (12/20) | 9.50 s
[Task 17/25] Current/Best: 16.54/ 23.32 GFLOPS | Progress: (16/20) | 11.70 s
[Task 17/25] Current/Best: 10.05/ 23.32 GFLOPS | Progress: (20/20) | 13.82 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.41/ 18.16 GFLOPS | Progress: (4/20) | 3.60 s
[Task 18/25] Current/Best: 10.61/ 18.16 GFLOPS | Progress: (8/20) | 7.03 s
[Task 18/25] Current/Best: 19.39/ 19.39 GFLOPS | Progress: (12/20) | 8.95 s
[Task 18/25] Current/Best: 10.02/ 19.39 GFLOPS | Progress: (16/20) | 12.52 s
[Task 18/25] Current/Best: 20.64/ 20.64 GFLOPS | Progress: (20/20) | 14.04 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 7.27/ 20.28 GFLOPS | Progress: (4/20) | 5.94 s
[Task 19/25] Current/Best: 2.61/ 20.28 GFLOPS | Progress: (8/20) | 9.25 s
[Task 19/25] Current/Best: 19.80/ 21.10 GFLOPS | Progress: (12/20) | 11.99 s
[Task 19/25] Current/Best: 15.36/ 21.10 GFLOPS | Progress: (16/20) | 14.80 s
[Task 19/25] Current/Best: 2.70/ 23.23 GFLOPS | Progress: (20/20) | 17.59 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 9.68/ 15.25 GFLOPS | Progress: (4/20) | 3.22 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 1/25] Current/Best: 17.45/ 17.45 GFLOPS | Progress: (4/20) | 5.56 s
[Task 1/25] Current/Best: 6.15/ 17.45 GFLOPS | Progress: (8/20) | 9.02 s
[Task 1/25] Current/Best: 11.53/ 22.79 GFLOPS | Progress: (12/20) | 11.53 s
[Task 1/25] Current/Best: 16.85/ 22.80 GFLOPS | Progress: (16/20) | 13.21 s
[Task 1/25] Current/Best: 11.61/ 23.60 GFLOPS | Progress: (20/20) | 14.98 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 2/25] Current/Best: 11.63/ 12.89 GFLOPS | Progress: (4/20) | 3.85 s
[Task 2/25] Current/Best: 13.80/ 18.29 GFLOPS | Progress: (8/20) | 5.16 s
[Task 2/25] Current/Best: 20.51/ 20.51 GFLOPS | Progress: (12/20) | 6.48 s
[Task 2/25] Current/Best: 12.68/ 20.51 GFLOPS | Progress: (16/20) | 7.77 s
[Task 2/25] Current/Best: 19.50/ 20.51 GFLOPS | Progress: (20/20) | 9.41 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 3/25] Current/Best: 1.63/ 10.55 GFLOPS | Progress: (4/20) | 5.81 s
[Task 3/25] Current/Best: 15.59/ 16.83 GFLOPS | Progress: (8/20) | 7.73 s
[Task 3/25] Current/Best: 14.75/ 16.83 GFLOPS | Progress: (12/20) | 9.45 s
[Task 3/25] Current/Best: 7.17/ 23.74 GFLOPS | Progress: (16/20) | 11.34 s
[Task 3/25] Current/Best: 12.50/ 23.74 GFLOPS | Progress: (20/20) | 15.93 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 4/25] Current/Best: 9.49/ 20.34 GFLOPS | Progress: (4/20) | 2.33 s
[Task 4/25] Current/Best: 6.74/ 20.34 GFLOPS | Progress: (8/20) | 7.12 s
[Task 4/25] Current/Best: 22.23/ 22.23 GFLOPS | Progress: (12/20) | 12.14 s
[Task 4/25] Current/Best: 16.72/ 22.23 GFLOPS | Progress: (16/20) | 14.57 s
[Task 4/25] Current/Best: 13.17/ 22.23 GFLOPS | Progress: (20/20) | 16.65 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 5/25] Current/Best: 9.55/ 10.30 GFLOPS | Progress: (4/20) | 2.53 s
[Task 5/25] Current/Best: 11.68/ 12.67 GFLOPS | Progress: (8/20) | 4.59 s
[Task 5/25] Current/Best: 11.12/ 18.14 GFLOPS | Progress: (12/20) | 7.81 s
[Task 5/25] Current/Best: 11.64/ 22.63 GFLOPS | Progress: (16/20) | 9.24 s
[Task 5/25] Current/Best: 11.93/ 22.63 GFLOPS | Progress: (20/20) | 11.14 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 6/25] Current/Best: 12.19/ 20.79 GFLOPS | Progress: (4/20) | 4.10 s
[Task 6/25] Current/Best: 18.95/ 20.79 GFLOPS | Progress: (8/20) | 5.87 s
[Task 6/25] Current/Best: 13.34/ 20.79 GFLOPS | Progress: (12/20) | 7.82 s
[Task 6/25] Current/Best: 19.93/ 20.79 GFLOPS | Progress: (16/20) | 10.10 s
[Task 6/25] Current/Best: 3.76/ 20.79 GFLOPS | Progress: (20/20) | 12.61 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 7/25] Current/Best: 10.71/ 12.88 GFLOPS | Progress: (4/20) | 3.61 s
[Task 7/25] Current/Best: 20.18/ 21.05 GFLOPS | Progress: (8/20) | 5.12 s
[Task 7/25] Current/Best: 16.02/ 21.05 GFLOPS | Progress: (12/20) | 7.02 s
[Task 7/25] Current/Best: 12.24/ 21.05 GFLOPS | Progress: (16/20) | 9.07 s
[Task 7/25] Current/Best: 6.40/ 21.75 GFLOPS | Progress: (20/20) | 11.52 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 8/25] Current/Best: 10.04/ 13.99 GFLOPS | Progress: (4/20) | 2.88 s
[Task 8/25] Current/Best: 9.72/ 13.99 GFLOPS | Progress: (8/20) | 8.16 s
[Task 8/25] Current/Best: 13.17/ 13.99 GFLOPS | Progress: (12/20) | 14.66 s
[Task 8/25] Current/Best: 18.68/ 18.68 GFLOPS | Progress: (16/20) | 16.74 s
[Task 8/25] Current/Best: 19.59/ 19.59 GFLOPS | Progress: (20/20) | 23.88 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 9/25] Current/Best: 14.31/ 15.80 GFLOPS | Progress: (4/20) | 11.90 s
[Task 9/25] Current/Best: 23.41/ 23.41 GFLOPS | Progress: (8/20) | 13.67 s
[Task 9/25] Current/Best: 8.25/ 23.41 GFLOPS | Progress: (12/20) | 16.24 s
[Task 9/25] Current/Best: 18.02/ 23.41 GFLOPS | Progress: (16/20) | 19.15 s
[Task 9/25] Current/Best: 8.94/ 23.41 GFLOPS | Progress: (20/20) | 27.73 s
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 10/25] Current/Best: 18.21/ 18.21 GFLOPS | Progress: (4/20) | 2.51 s
[Task 10/25] Current/Best: 15.53/ 18.21 GFLOPS | Progress: (8/20) | 4.18 s
[Task 10/25] Current/Best: 12.32/ 18.88 GFLOPS | Progress: (12/20) | 5.73 s
[Task 10/25] Current/Best: 19.15/ 20.34 GFLOPS | Progress: (16/20) | 6.84 s
[Task 10/25] Current/Best: 8.94/ 20.34 GFLOPS | Progress: (20/20
) | 8.37 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 11/25] Current/Best: 11.54/ 18.09 GFLOPS | Progress: (4/20) | 3.29 s
[Task 11/25] Current/Best: 16.90/ 18.09 GFLOPS | Progress: (8/20) | 6.12 s
[Task 11/25] Current/Best: 18.03/ 18.09 GFLOPS | Progress: (12/20) | 8.20 s
[Task 11/25] Current/Best: 12.99/ 21.21 GFLOPS | Progress: (16/20) | 11.17 s
[Task 11/25] Current/Best: 19.46/ 21.62 GFLOPS | Progress: (20/20) | 13.26 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 12/25] Current/Best: 7.81/ 18.07 GFLOPS | Progress: (4/20) | 5.66 s
[Task 12/25] Current/Best: 5.15/ 18.07 GFLOPS | Progress: (8/20) | 9.64 s
[Task 12/25] Current/Best: 18.87/ 18.87 GFLOPS | Progress: (12/20) | 11.64 s
[Task 12/25] Current/Best: 15.29/ 18.87 GFLOPS | Progress: (16/20) | 14.56 s
[Task 12/25] Current/Best: 15.13/ 18.87 GFLOPS | Progress: (20/20) | 16.48 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 13/25] Current/Best: 8.69/ 17.31 GFLOPS | Progress: (4/20) | 3.68 s
[Task 13/25] Current/Best: 15.72/ 20.93 GFLOPS | Progress: (8/20) | 6.31 s
[Task 13/25] Current/Best: 19.39/ 21.55 GFLOPS | Progress: (12/20) | 9.34 s
[Task 13/25] Current/Best: 12.25/ 21.55 GFLOPS | Progress: (16/20) | 12.80 s
[Task 13/25] Current/Best: 18.75/ 21.55 GFLOPS | Progress: (20/20) | 15.14 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 14/25] Current/Best: 13.47/ 13.47 GFLOPS | Progress: (4/20) | 3.38 s
[Task 14/25] Current/Best: 6.09/ 13.47 GFLOPS | Progress: (8/20) | 5.59 s
[Task 14/25] Current/Best: 21.10/ 21.10 GFLOPS | Progress: (12/20) | 8.33 s
[Task 14/25] Current/Best: 16.11/ 21.10 GFLOPS | Progress: (16/20) | 10.01 s Done.
+
[Task 14/25] Current/Best: 17.37/ 21.10 GFLOPS | Progress: (20/20) | 11.73 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 15/25] Current/Best: 16.21/ 17.66 GFLOPS | Progress: (4/20) | 2.62 s
[Task 15/25] Current/Best: 14.31/ 18.12 GFLOPS | Progress: (8/20) | 3.97 s
[Task 15/25] Current/Best: 10.40/ 22.29 GFLOPS | Progress: (12/20) | 6.30 s
[Task 15/25] Current/Best: 20.23/ 22.29 GFLOPS | Progress: (16/20) | 9.70 s
[Task 15/25] Current/Best: 9.70/ 22.29 GFLOPS | Progress: (20/20) | 10.72 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 16/25] Current/Best: 20.30/ 20.30 GFLOPS | Progress: (4/20) | 2.89 s
[Task 16/25] Current/Best: 3.01/ 20.30 GFLOPS | Progress: (8/20) | 4.53 s
[Task 16/25] Current/Best: 19.41/ 20.30 GFLOPS | Progress: (12/20) | 5.75 s
[Task 16/25] Current/Best: 17.72/ 20.30 GFLOPS | Progress: (16/20) |
7.11 s
[Task 16/25] Current/Best: 10.12/ 22.42 GFLOPS | Progress: (20/20) | 9.28 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 17/25] Current/Best: 13.06/ 18.80 GFLOPS | Progress: (4/20) | 4.78 s
[Task 17/25] Current/Best: 14.38/ 23.08 GFLOPS | Progress: (8/20) | 7.64 s
[Task 17/25] Current/Best: 17.15/ 23.08 GFLOPS | Progress: (12/20) | 9.71 s
[Task 17/25] Current/Best: 16.74/ 23.08 GFLOPS | Progress: (16/20) | 11.96 s
[Task 17/25] Current/Best: 10.05/ 23.08 GFLOPS | Progress: (20/20) | 14.12 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 18/25] Current/Best: 11.68/ 17.84 GFLOPS | Progress: (4/20) | 3.75 s
[Task 18/25] Current/Best: 10.60/ 19.81 GFLOPS | Progress: (8/20) | 7.49 s
[Task 18/25] Current/Best: 18.82/ 19.81 GFLOPS | Progress: (12/20) | 9.42 s
[Task 18/25] Current/Best: 9.99/ 19.81 GFLOPS | Progress: (16/20) | 13.32 s
[Task 18/25] Current/Best: 20.73/ 20.73 GFLOPS | Progress: (20/20) | 14.84 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 19/25] Current/Best: 6.88/ 20.51 GFLOPS | Progress: (4/20) | 6.10 s
[Task 19/25] Current/Best: 2.60/ 20.51 GFLOPS | Progress: (8/20) | 9.42 s
[Task 19/25] Current/Best: 19.88/ 21.65 GFLOPS | Progress: (12/20) | 12.44 s
[Task 19/25] Current/Best: 15.08/ 21.76 GFLOPS | Progress: (16/20) | 15.56 s
[Task 19/25] Current/Best: 2.70/ 23.20 GFLOPS | Progress: (20/20) | 18.35 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 20/25] Current/Best: 8.83/ 15.08 GFLOPS | Progress: (4/20) | 3.31 s Done.
Done.
-
[Task 20/25] Current/Best: 9.83/ 15.25 GFLOPS | Progress: (8/20) | 6.63 s
[Task 20/25] Current/Best: 2.32/ 16.76 GFLOPS | Progress: (12/20) | 10.53 s
[Task 20/25] Current/Best: 12.37/ 16.76 GFLOPS | Progress: (16/20) | 14.04 s
[Task 20/25] Current/Best: 13.21/ 22.15 GFLOPS | Progress: (20/20) | 16.16 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.42/ 17.69 GFLOPS | Progress: (4/20) | 3.13 s
[Task 21/25] Current/Best: 14.54/ 17.69 GFLOPS | Progress: (8/20) | 4.68 s
[Task 21/25] Current/Best: 1.61/ 17.69 GFLOPS | Progress: (12/20) | 6.81 s
[Task 21/25] Current/Best: 18.27/ 18.27 GFLOPS | Progress: (16/20) | 10.20 s
[Task 21/25] Current/Best: 4.47/ 18.27 GFLOPS | Progress: (20/20) | 17.23 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 16.90 GFLOPS | Progress: (4/20
) | 2.61 s
[Task 22/25] Current/Best: 8.57/ 21.71 GFLOPS | Progress: (8/20) | 4.59 s
[Task 22/25] Current/Best: 19.82/ 21.71 GFLOPS | Progress: (12/20) | 6.89 s
[Task 22/25] Current/Best: 15.51/ 21.71 GFLOPS | Progress: (16/20) | 8.92 s
[Task 22/25] Current/Best: 14.33/ 21.71 GFLOPS | Progress: (20/20) | 10.57 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.57/ 20.36 GFLOPS | Progress: (4/20) | 3.19 s
[Task 23/25] Current/Best: 15.33/ 20.36 GFLOPS | Progress: (8/20) | 6.44 s
[Task 23/25] Current/Best: 21.03/ 21.22 GFLOPS | Progress: (12/20) | 8.24 s
[Task 23/25] Current/Best: 6.45/ 21.22 GFLOPS | Progress: (16/20) | 15.16 s
[Task 23/25] Current/Best: 7.90/ 21.22 GFLOPS | Progress: (20/20) | 19.37 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.60/ 8.60 GFLOPS | Progress: (4/20) | 11.73 s
[Task 24/25] Current/Best: 1.89/ 8.60 GFLOPS | Progress: (8/20) | 22.75 s
[Task 24/25] Current/Best: 4.48/ 8.60 GFLOPS | Progress: (12/20) | 34.26 s Done.
+
[Task 20/25] Current/Best: 10.22/ 15.08 GFLOPS | Progress: (8/20) | 6.71 s
[Task 20/25] Current/Best: 2.32/ 16.68 GFLOPS | Progress: (12/20) | 10.66 s
[Task 20/25] Current/Best: 12.48/ 16.68 GFLOPS | Progress: (16/20) | 14.54 s
[Task 20/25] Current/Best: 13.32/ 21.93 GFLOPS | Progress: (20/20) | 16.64 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 21/25] Current/Best: 6.40/ 17.72 GFLOPS | Progress: (4/20) | 3.23 s
[Task 21/25] Current/Best: 14.65/ 17.72 GFLOPS | Progress: (8/20) | 4.84 s
[Task 21/25] Current/Best: 1.61/ 17.72 GFLOPS | Progress: (12/20) | 6.98 s
[Task 21/25] Current/Best: 17.86/ 17.86 GFLOPS | Progress: (16/20) | 10.49 s
[Task 21/25] Current/Best: 4.45/ 17.86 GFLOPS | Progress: (20/20) | 17.92 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 22/25] Current/Best: 2.70/ 17.05 GFLOPS | Progress: (4/20
) | 2.64 s
[Task 22/25] Current/Best: 8.69/ 21.06 GFLOPS | Progress: (8/20) | 4.68 s
[Task 22/25] Current/Best: 19.70/ 21.06 GFLOPS | Progress: (12/20) | 7.07 s
[Task 22/25] Current/Best: 15.33/ 21.06 GFLOPS | Progress: (16/20) | 9.25 s
[Task 22/25] Current/Best: 14.45/ 21.06 GFLOPS | Progress: (20/20) | 10.99 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 23/25] Current/Best: 17.52/ 20.69 GFLOPS | Progress: (4/20) | 3.22 s
[Task 23/25] Current/Best: 15.51/ 20.69 GFLOPS | Progress: (8/20) | 6.58 s
[Task 23/25] Current/Best: 20.82/ 21.75 GFLOPS | Progress: (12/20) | 8.43 s
[Task 23/25] Current/Best: 6.31/ 21.75 GFLOPS | Progress: (16/20) | 15.59 s
[Task 23/25] Current/Best: 7.91/ 21.75 GFLOPS | Progress: (20/20) | 19.82 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 24/25] Current/Best: 8.35/ 8.35 GFLOPS | Progress: (4/20) | 11.70 s
[Task 24/25] Current/Best: 3.51/ 8.35 GFLOPS | Progress: (8/20) | 22.96 s
[Task 24/25] Current/Best: 4.18/ 8.35 GFLOPS | Progress: (12/20) | 33.70 s Done.
Done.
-
[Task 24/25] Current/Best: 7.27/ 8.86 GFLOPS | Progress: (16/20) | 39.96 s
[Task 24/25] Current/Best: 3.31/ 8.94 GFLOPS | Progress: (20/20) | 45.78 s Done.
-
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.55/ 2.86 GFLOPS | Progress: (4/20) | 11.51 s
[Task 25/25] Current/Best: 5.84/ 8.07 GFLOPS | Progress: (8/20) | 22.77 s
[Task 25/25] Current/Best: 5.98/ 8.07 GFLOPS | Progress: (12/20) | 34.17 s
[Task 25/25] Current/Best: 5.75/ 9.26 GFLOPS | Progress: (16/20) | 35.93 s
[Task 25/25] Current/Best: 2.94/ 9.26 GFLOPS | Progress: (20/20) | 46.66 s
+
[Task 24/25] Current/Best: 7.03/ 8.92 GFLOPS | Progress: (16/20) | 39.50 s
[Task 24/25] Current/Best: 3.26/ 8.92 GFLOPS | Progress: (20/20) | 45.60 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/20) | 0.00 s
[Task 25/25] Current/Best: 1.56/ 2.90 GFLOPS | Progress: (4/20) | 11.53 s
[Task 25/25] Current/Best: 5.72/ 7.89 GFLOPS | Progress: (8/20) | 22.77 s
[Task 25/25] Current/Best: 5.96/ 7.89 GFLOPS | Progress: (12/20) | 34.04 s
[Task 25/25] Current/Best: 5.82/ 8.98 GFLOPS | Progress: (16/20) | 35.94 s
[Task 25/25] Current/Best: 2.93/ 8.98 GFLOPS | Progress: (20/20) | 46.63 s
The output from this tuning process will look something like this:
@@ -660,8 +660,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 412.13002305000145, 'median': 412.10994939999637, 'std': 1.0728322745304126}
- unoptimized: {'mean': 494.48566579999914, 'median': 494.308517449997, 'std': 1.0335504241642963}
+ optimized: {'mean': 412.25458756999615, 'median': 412.13977365000574, 'std': 0.7233945995532131}
+ unoptimized: {'mean': 494.4295934299953, 'median': 494.5921276000263, 'std': 0.5236284867723745}
@@ -681,7 +681,7 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 10 minutes 13.333 seconds)
+ **Total running time of the script:** ( 10 minutes 25.317 seconds)
.. _sphx_glr_download_tutorial_autotvm_relay_x86.py:
diff --git a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
index 79b102c41..e93f09491 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.192e-07 secs/op
+ 1.287e-07 secs/op
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 119b8a988..9071af63f 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -15,12 +15,11 @@ Introduction to TOPI
This is an introductory tutorial to TVM Operator Inventory (TOPI).
TOPI provides numpy-style generic operations and schedules with higher abstractions than TVM.
-In this tutorial, we will see how TOPI can save us from writing boilerplates code in TVM.
+In this tutorial, we will see how TOPI can save us from writing boilerplate code in TVM.
.. code-block:: default
- from __future__ import absolute_import, print_function
import tvm
import tvm.testing
@@ -233,7 +232,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0xfe4aad0)), stage(b, placeholder(b, 0xd420170)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min= [...]
+ [stage(a, placeholder(a, 0x4c1bd10)), stage(b, placeholder(b, 0x4d25f50)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min= [...]
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index d6fb35c93..8b68d58e2 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,17 +5,17 @@
Computation times
=================
-**12:51.575** total execution time for **tutorial** files:
+**13:04.882** total execution time for **tutorial** files:
-- **10:13.333**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **00:59.134**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:45.218**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:28.125**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:23.300**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:01.366**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.743**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.224**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.036**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
-- **00:00.035**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
-- **00:00.033**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.029**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **10:25.317**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:00.128**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:44.333**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **00:28.085**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:25.071**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:00.836**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.730**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.229**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.046**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.038**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
+- **00:00.035**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **00:00.033**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index 98401a9bf..819849973 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -252,13 +252,13 @@ helper function to run a profile of the TVM generated code.
.. code-block:: none
- Numpy running time: 0.000007
- naive: 0.000006
+ Numpy running time: 0.000009
+ naive: 0.000008
-Updating the Schedule to Use Paralleism
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Updating the Schedule to Use Parallelism
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Now that we've illustrated the fundamentals of TE, let's go deeper into what
schedules do, and how they can be used to optimize tensor expressions for
@@ -344,7 +344,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000006
+ parallel: 0.000008
@@ -447,10 +447,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 6.777090000014141e-06 1.0
- naive 5.8412999999999995e-06 0.8619186110834902
- parallel 6.0707e-06 0.8957679475980594
- vector 2.47215e-05 3.6478045886875363
+ numpy 8.690399999977671e-06 1.0
+ naive 8.4343e-06 0.9705307005456216
+ parallel 8.0627e-06 0.9277708736100428
+ vector 2.4574199999999996e-05 2.8277409555444093
@@ -839,7 +839,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.018761
+ Numpy running time: 0.018735
@@ -897,7 +897,7 @@ optimizations.
/workspace/python/tvm/driver/build_module.py:264: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
- none: 3.260645
+ none: 3.348010
@@ -996,7 +996,7 @@ schedule.
.. code-block:: none
- blocking: 0.308868
+ blocking: 0.299571
@@ -1088,7 +1088,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.343562
+ vectorization: 0.335709
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1160,7 +1160,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.114552
+ loop permutation: 0.118393
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1206,7 +1206,7 @@ can observe the array access pattern of B (after flattening), which is
regular but discontinuous. We expect that after some transformation we can
get a continuous access pattern. By reordering a ``[16][16]`` array to a
``[16/4][16][4]`` array the access pattern of B will be sequential when
-grabing the corresponding value from the packed array.
+grabbing the corresponding value from the packed array.
To accomplish this, we are going to have to start with a new default
schedule, taking into account the new packing of B. It's worth taking a
@@ -1257,7 +1257,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.107916
+ array packing: 0.110317
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1348,7 +1348,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.110790
+ block caching: 0.110864
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1432,7 +1432,7 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.144465
+ parallelization: 0.144669
@main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1511,13 +1511,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 3.2606449347 1.0
- blocking 0.3088675775 0.09472591578831865
- vectorization 0.3435617395 0.10536619177506669
- loop permutation 0.11455243290000001 0.03513183287175044
- array packing 0.10791621 0.0330965843142099
- block caching 0.1107904824 0.03397808857412235
- parallelization 0.1444653366 0.044305755300919236
+ none 3.3480103597000004 1.0
+ blocking 0.2995706819 0.08947722668541658
+ vectorization 0.3357090278 0.10027120341111528
+ loop permutation 0.1183928922 0.0353621642349424
+ array packing 0.11031703450000001 0.032950027821862835
+ block caching 0.1108637539 0.03311332462840228
+ parallelization 0.14466928880000002 0.04321052603103748
@@ -1537,7 +1537,7 @@ algorithms that can search the potential optimization. With the knowledge you
have from this introduction to TE, we can now begin to explore how TVM can
automate the schedule optimization process.
-This tutorial provided a walkthrough of TVM Tensor Expresstion (TE) workflow
+This tutorial provided a walk-through of TVM Tensor Expression (TE) workflow
using a vector add and a matrix multiplication examples. The general workflow
is
@@ -1552,6 +1552,11 @@ 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 0.128 seconds)
+
+
.. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt b/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt
index 4c88822c1..ff36b5c06 100644
--- a/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt
+++ b/docs/_sources/tutorial/tensor_ir_blitz_course.rst.txt
@@ -17,7 +17,7 @@ TensorIR is a domain specific language for deep learning programs serving two br
- An implementation for transforming and optimizing programs on various hardware backends.
-- An abstraction for automatic tensorized program optimization.
+- An abstraction for automatic _tensorized_ program optimization.
.. code-block:: default
@@ -244,7 +244,7 @@ schedule on the initial IRModule created from either TVMScript or Tensor Express
sequence of schedule primitives will help to improve the performance. And at last, we can lower
and build it into a runnable module.
-Here we just demostrate a very simple tranformation. First we create schedule on the input `ir_module`.
+Here we just demonstrate a very simple transformation. First we create schedule on the input `ir_module`.
.. code-block:: default
@@ -275,7 +275,7 @@ Tile the loop into 3 loops and print the result.
# Get block by its name
block_b = sch.get_block("B")
- # Get loops surronding the block
+ # Get loops surrounding the block
(i,) = sch.get_loops(block_b)
# Tile the loop nesting.
i_0, i_1, i_2 = sch.split(i, factors=[2, 2, 2])
diff --git a/docs/commit_hash b/docs/commit_hash
index 1b5ab5ddc..f548575cc 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-d490620085792f802d606209008698e65fb12c0e
+f5f9600614c4aa933c863001459d92b13d9b72fc
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index 4188b3910..9b32c3cf3 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -401,7 +401,7 @@
</div>
<img alt="../../_images/sphx_glr_from_mxnet_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_from_mxnet_001.png" />
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip5dbcd572-c086-45f1-81d8-9ca43debd0e0 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip69be23b3-820e-43a1-95fd-b3934a3c4108 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
</pre></div>
</div>
diff --git a/docs/how_to/compile_models/from_oneflow.html b/docs/how_to/compile_models/from_oneflow.html
index b487147a2..19a6d8639 100644
--- a/docs/how_to/compile_models/from_oneflow.html
+++ b/docs/how_to/compile_models/from_oneflow.html
@@ -406,42 +406,54 @@ python3 -m pip install -f https://release.oneflow.info <span class="nv">oneflow<
<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
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diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 8a87fa681..2b6dcd2a4 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -469,7 +469,7 @@ A quick solution is</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 5.539 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 8.144 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
<div class="sphx-glr-download 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
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--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -387,9 +387,10 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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</pre></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 a26b72376..ee71b49e4 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -612,7 +612,7 @@ 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 0.224 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 2.154 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
<div class="sphx-glr-download 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 983d2308e..b52b0fa68 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -300,18 +300,18 @@
<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>05:36.540</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:29.087</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>01:05.539</strong>: <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></li>
-<li><p><strong>01:00.224</strong>: <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></li>
-<li><p><strong>00:57.002</strong>: <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></li>
-<li><p><strong>00:40.945</strong>: <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></li>
-<li><p><strong>00:31.870</strong>: <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></li>
-<li><p><strong>00:22.716</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
-<li><p><strong>00:21.762</strong>: <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></li>
-<li><p><strong>00:19.043</strong>: <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></li>
-<li><p><strong>00:14.532</strong>: <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></li>
-<li><p><strong>00:02.906</strong>: <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></li>
+<li><p><strong>01:08.144</strong>: <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></li>
+<li><p><strong>01:02.154</strong>: <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></li>
+<li><p><strong>00:58.529</strong>: <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></li>
+<li><p><strong>00:34.150</strong>: <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></li>
+<li><p><strong>00:24.336</strong>: <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></li>
+<li><p><strong>00:22.939</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
+<li><p><strong>00:21.806</strong>: <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></li>
+<li><p><strong>00:20.351</strong>: <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></li>
+<li><p><strong>00:14.185</strong>: <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></li>
+<li><p><strong>00:02.493</strong>: <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></li>
</ul>
</div>
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 0acd817a4..16a126e91 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -627,7 +627,7 @@ to the remote android device.</p>
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 16.2326 15.9737 16.9055 15.8591 0.4070
+ 16.0267 15.9703 16.3084 15.9199 0.1226
</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 96df8e94b..3702de2de 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,15 +409,16 @@ be unstable.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>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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+100%|##########| 170M/170M [00:01<00:00, 170MB/s]
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
for i in range(dim)
/usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -515,7 +516,7 @@ 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> ( 2 minutes 58.760 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 59.421 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
<div class="sphx-glr-download 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 b5a36c03e..b9ee8fa1f 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,9 +450,10 @@ training. Other models require a full post training calibration.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>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%|##########| 13.6M/13.6M [00:00<00:00, 30.5MB/s]
</pre></div>
</div>
</div>
@@ -546,7 +547,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
<p class="sphx-glr-script-out">Out:</p>
<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)
- 89.7738 89.6539 91.2964 89.0311 0.5350
+ 90.5046 90.2703 100.2387 90.1407 1.1541
</pre></div>
</div>
<div class="admonition note">
@@ -585,7 +586,7 @@ 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 6.339 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 7.039 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
<div class="sphx-glr-download 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 78fca97cc..8e9b3e293 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -545,7 +545,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
<p class="sphx-glr-script-out">Out:</p>
<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)
- 117.8574 117.7878 123.0413 117.0000 0.6198
+ 120.5413 120.4076 126.1949 119.6308 0.7128
</pre></div>
</div>
<div class="admonition note">
@@ -573,7 +573,7 @@ network for ARM CPU</span></a>.</p></li>
</ul>
</div></blockquote>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 51.540 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 58.943 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.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_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index ad0f170e9..7252f0336 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -482,7 +482,7 @@ for calibration. But the accuracy might be impacted.</p>
DeprecationWarning,
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.855 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 12.976 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-quantized-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.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_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_sparse.html b/docs/how_to/deploy_models/deploy_sparse.html
index 3427b405f..d9d033562 100644
--- a/docs/how_to/deploy_models/deploy_sparse.html
+++ b/docs/how_to/deploy_models/deploy_sparse.html
@@ -355,7 +355,7 @@ weights should be set to 0, the most straight forward is by picking the
weights with the smallest value. Typically, weights are pruned to a desired
sparsity percentage. For example, a 95% sparse model would have only 5% of
its weights non-zero. Pruning to very high sparsities often requires
-finetuning or full retraining as it tends to be a lossy approximation.
+fine-tuning or full retraining as it tends to be a lossy approximation.
Although parameter size benefits are quite easy to obtain from a pruned model
through simple compression, leveraging sparsity to yield runtime speedups
is more complicated.</p>
diff --git a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index ada951bb5..a93d691e4 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,24 +415,23 @@ to your device.</p>
Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
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</pre></div>
</div>
<p>Create TVM runtime and do inference
@@ -477,7 +476,7 @@ Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from h
</pre></div>
</div>
<img alt="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" />
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 19.378 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 18.823 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.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_ssd_gluoncv.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 44228bc6f..b6d4e1a7c 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
<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>10:20.023</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>10:29.094</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:58.760</strong>: <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></li>
-<li><p><strong>02:19.378</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
-<li><p><strong>01:51.540</strong>: <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></li>
-<li><p><strong>01:12.855</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
-<li><p><strong>01:06.339</strong>: <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></li>
-<li><p><strong>00:28.900</strong>: <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></li>
-<li><p><strong>00:22.056</strong>: <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></li>
-<li><p><strong>00:00.197</strong>: <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></li>
+<li><p><strong>02:59.421</strong>: <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></li>
+<li><p><strong>02:18.823</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
+<li><p><strong>01:58.943</strong>: <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></li>
+<li><p><strong>01:12.976</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
+<li><p><strong>01:07.039</strong>: <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></li>
+<li><p><strong>00:29.058</strong>: <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></li>
+<li><p><strong>00:22.634</strong>: <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></li>
+<li><p><strong>00:00.201</strong>: <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></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index 274244a84..2a0cd236e 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -590,7 +590,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zipf72c5caa-2564-4689-b8f2-56e2ea6329cf from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip5f463e3d-4b2a-4d5e-966b-aca87eb90c16 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
</pre></div>
</div>
<p>It’s easy to execute MobileNet with native TVM:</p>
@@ -652,10 +652,10 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Check failed: (lower) is false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Check failed: (lower) is false: FloatImm lowering function for target llvm type 150 not found
</pre></div>
</div>
-<p>When we attempt to run the model, we get a familiar error telling us that more functions need to be registerd for myfloat.</p>
+<p>When we attempt to run the model, we get a familiar error telling us that more functions need to be registered for myfloat.</p>
<p>Because this is a neural network, many more operations are required.
Here, we register all the needed functions:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">datatype</span><span class="o">.</span><span class="n">register_op</span><span class="p">(</span>
diff --git a/docs/how_to/extend_tvm/low_level_custom_pass.html b/docs/how_to/extend_tvm/low_level_custom_pass.html
index b09500b60..ca129570e 100644
--- a/docs/how_to/extend_tvm/low_level_custom_pass.html
+++ b/docs/how_to/extend_tvm/low_level_custom_pass.html
@@ -448,7 +448,7 @@ function will be skipped.</p>
<a href="../../reference/api/python/tir.html#tvm.tir.stmt_functor.post_order_visit" title="View documentation for tvm.tir.stmt_functor.post_order_visit"><span class="n">tvm</span><span class="o">.</span><span class="n">tir</span><span class="o">.</span><span class="n">stmt_functor</span><span class="o">.</span><span class="n">post_order_visit</span></a><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">body</span><span class="p">,</span> <span cla [...]
<span class="k">if</span> <span class="ow">not</span> <span class="n">loops</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">sf</span>
+ <span class="k">return</span> <span class="n">f</span>
<span class="c1"># The last list arugment indicates what kinds of nodes will be transformed.</span>
<span class="c1"># Thus, in this case only `For` nodes will call `vectorize8`</span>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 691cbf7eb..bff688238 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -300,11 +300,11 @@
<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>00:40.821</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:40.261</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:36.996</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
-<li><p><strong>00:02.456</strong>: <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></li>
-<li><p><strong>00:01.156</strong>: <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></li>
+<li><p><strong>00:36.576</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
+<li><p><strong>00:02.362</strong>: <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></li>
+<li><p><strong>00:01.109</strong>: <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></li>
<li><p><strong>00:00.213</strong>: <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></li>
</ul>
</div>
diff --git a/docs/how_to/extend_tvm/use_pass_infra.html b/docs/how_to/extend_tvm/use_pass_infra.html
index e43a5b1a8..0db9d3372 100644
--- a/docs/how_to/extend_tvm/use_pass_infra.html
+++ b/docs/how_to/extend_tvm/use_pass_infra.html
@@ -349,7 +349,7 @@ respectively. Or users can rely on <a class="reference internal" href="../../ref
on a Relay/tir program where the dependencies between passes can be resolved by the
pass infra. For more details about each type of these passes, please refer to
the <a class="reference internal" href="../../arch/pass_infra.html#pass-infra"><span class="std std-ref">Pass Infrastructure</span></a></p>
-<p>This tutorial mainly demostrates how developers can use the pass infra to perform
+<p>This tutorial mainly demonstrates how developers can use the pass infra to perform
a certain optimization and create an optimization pipeline for a Relay program.
The same approach can be used for tir as well.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -432,7 +432,7 @@ eliminate the common expressions that used by <cite>z</cite> and <cite>z1</cite>
}
</pre></div>
</div>
-<p>Some optimizations, such as fusion, are parameteric as well. For example,
+<p>Some optimizations, such as fusion, are parametric as well. For example,
opt level 0 will not allow operators to be fused together. Users can pass the
<cite>fuse_opt_level</cite> to enable this.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span> <span class="o">=</span> <a href="../../reference/api/python/relay/transform.html#tvm.relay.transform.FuseOps" title="View documentation for tvm.relay.transform.FuseOps"><span class="n">relay</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">FuseOps</span></a><span class="p">(</span><span class="n">fuse_opt_level</span><span cl [...]
@@ -476,7 +476,7 @@ canonicalize a Relay program.</p>
these issues explicitly by specifying the required passes of each pass and
packing them as a whole to execute. For example, the same passes can now be
applied using the sequential style as the following. <a class="reference internal" href="../../reference/api/python/ir.html#tvm.transform.Sequential" title="tvm.transform.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">tvm.transform.Sequential</span></code></a> is
-similiar to <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential">torch.nn.sequential</a>
+similar to <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential">torch.nn.sequential</a>
and <a class="reference external" href="https://mxnet.apache.org/api/python/docs/_modules/mxnet/gluon/block.html">mxnet.gluon.block</a>.
For example, <cite>torch.nn.sequential</cite> is used to contain a sequence of PyTorch
<cite>Modules</cite> that will be added to build a network. It focuses on the network
@@ -791,7 +791,7 @@ done
<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
<p>This tutorial has covered how we can write and invoke passes in TVM more
conveniently using the pass infra. Different ways of invoking a pass are also
-disucssed. Using <a class="reference internal" href="../../reference/api/python/ir.html#tvm.transform.Sequential" title="tvm.transform.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">tvm.transform.Sequential</span></code></a> can largely help
+discussed. Using <a class="reference internal" href="../../reference/api/python/ir.html#tvm.transform.Sequential" title="tvm.transform.Sequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">tvm.transform.Sequential</span></code></a> can largely help
users to ease the work of handling multiple optimization passes and their
dependencies. In addition, an example is provided to illustrate
how we can debug a pass using the <code class="docutils literal notranslate"><span class="pre">PrintIR</span></code> and tracing.</p>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 146909e7e..9fdceb4a7 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -345,7 +345,7 @@ pass execution, analyze per-pass effects, and observe various events.</p>
instances to <a class="reference internal" href="../../reference/api/python/ir.html#tvm.transform.PassContext" title="tvm.transform.PassContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">tvm.transform.PassContext</span></code></a>. We provide a pass instrument
for collecting timing information (<a class="reference internal" href="../../reference/api/python/ir.html#tvm.instrument.PassTimingInstrument" title="tvm.ir.instrument.PassTimingInstrument"><code class="xref py py-class docutils literal notranslate"><span class="pre">tvm.ir.instrument.PassTimingInstrument</span></code></a>),
but an extension mechanism is available via the <a class="reference internal" href="../../reference/api/python/ir.html#tvm.instrument.pass_instrument" title="tvm.instrument.pass_instrument"><code class="xref py py-func docutils literal notranslate"><span class="pre">tvm.instrument.pass_instrument()</span></code></a> decorator.</p>
-<p>This tutorial demostrates how developers can use <code class="docutils literal notranslate"><span class="pre">PassContext</span></code> to instrument
+<p>This tutorial demonstrates how developers can use <code class="docutils literal notranslate"><span class="pre">PassContext</span></code> to instrument
passes. Please also refer to the <a class="reference internal" href="../../arch/pass_infra.html#pass-infra"><span class="std std-ref">Pass Infrastructure</span></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tvm</span>
<span class="kn">import</span> <span class="nn">tvm.relay</span> <span class="k">as</span> <span class="nn">relay</span>
@@ -486,10 +486,10 @@ profile the execution time of each passes.</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 6572us [6572us] (45.25%; 45.25%)
-FoldScaleAxis: 7953us [6us] (54.75%; 54.75%)
- FoldConstant: 7946us [1582us] (54.71%; 99.92%)
- InferType: 6364us [6364us] (43.82%; 80.09%)
+InferType: 6541us [6541us] (44.78%; 44.78%)
+FoldScaleAxis: 8065us [6us] (55.22%; 55.22%)
+ FoldConstant: 8059us [1554us] (55.18%; 99.93%)
+ InferType: 6505us [6505us] (44.54%; 80.72%)
</pre></div>
</div>
</div>
@@ -512,10 +512,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 6396us [6396us] (44.75%; 44.75%)
-FoldScaleAxis: 7897us [6us] (55.25%; 55.25%)
- FoldConstant: 7891us [1621us] (55.21%; 99.92%)
- InferType: 6270us [6270us] (43.87%; 79.46%)
+InferType: 6270us [6270us] (44.51%; 44.51%)
+FoldScaleAxis: 7816us [5us] (55.49%; 55.49%)
+ FoldConstant: 7811us [1613us] (55.45%; 99.94%)
+ InferType: 6198us [6198us] (44.00%; 79.35%)
</pre></div>
</div>
<p>Register empty list to clear existing instruments.</p>
@@ -770,7 +770,7 @@ PassBadExitCtx bad exit_pass_ctx!!!
Catching ValueError: PassBadExitCtx bad exit_pass_ctx
</pre></div>
</div>
-<p>Exceptions occured in <code class="docutils literal notranslate"><span class="pre">should_run</span></code>, <code class="docutils literal notranslate"><span class="pre">run_before_pass</span></code>, <code class="docutils literal notranslate"><span class="pre">run_after_pass</span></code>
+<p>Exceptions occurred in <code class="docutils literal notranslate"><span class="pre">should_run</span></code>, <code class="docutils literal notranslate"><span class="pre">run_before_pass</span></code>, <code class="docutils literal notranslate"><span class="pre">run_after_pass</span></code>
are not handled explicitly – we rely on the context manager (the <code class="docutils literal notranslate"><span class="pre">with</span></code> syntax)
to exit <code class="docutils literal notranslate"><span class="pre">PassContext</span></code> safely.</p>
<p>We use <code class="docutils literal notranslate"><span class="pre">run_before_pass</span></code> as an example:</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index 7466fd0d4..f1d78d042 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -401,7 +401,7 @@ programmers. Thus how to maximize the data reuse in the shared memory is
critical to achieve high performance in GPU kernels.</p>
<a class="reference internal image-reference" href="https://github.com/dmlc/web-data/raw/main/tvm/tutorial/gpu_memory_hierarchy.png"><img alt="https://github.com/dmlc/web-data/raw/main/tvm/tutorial/gpu_memory_hierarchy.png" class="align-center" src="https://github.com/dmlc/web-data/raw/main/tvm/tutorial/gpu_memory_hierarchy.png" style="width: 271px; height: 319px;" /></a>
<p>In this example, we load both Apad and W into buffer AA and WW, which are
-stored in the shared memory. These bufferes will be later shared by all
+stored in the shared memory. These buffers will be later shared by all
threads within the same thread block to compute the convolution. Each thread
then loads its own part from shared buffer into their local registers, AL and
WL. BL is a local cache of output B, which is also stored in the thread local
@@ -534,7 +534,7 @@ latency of convolution.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.181335 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 54.131947 ms
</pre></div>
</div>
<div class="sphx-glr-footer class 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 ec21bf862..54293a267 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -613,7 +613,7 @@ one time.</p>
<p class="admonition-title">Note</p>
<p><em>Warp-level Operation</em></p>
<p>Note that all TensorCore instructions are warp-level instructions, which means all 32 threads
-in a warp should do this instruction simultaneously. Making theadIdx.x extent=32 is one of the
+in a warp should do this instruction simultaneously. Making threadIdx.x extent=32 is one of the
easiest way to solve this. Then We can bind threadIdx.x to any loops except those contain
TensorCore intrinsics directly or indirectly. Also note that it is not the unique solution.
The only thing we should do is to make sure all threads in a warp can call TensorCore at the same time.</p>
@@ -878,7 +878,7 @@ be able to run on our build server</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 7.453295 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 6.993976 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 bf44e7c7d..5f78567de 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -431,8 +431,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018660
-Baseline: 3.258906
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019189
+Baseline: 3.452864
</pre></div>
</div>
<p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -494,7 +494,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.315168
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.300515
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -563,7 +563,7 @@ vastly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.341248
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.334529
</pre></div>
</div>
<p>Here is the generated IR after vectorization.</p>
@@ -626,7 +626,7 @@ the access pattern for A matrix is more cache friendly.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.118415
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.116622
</pre></div>
</div>
<p>Here is the generated IR after loop permutation.</p>
@@ -711,7 +711,7 @@ flattening.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110422
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110502
</pre></div>
</div>
<p>Here is the generated IR after array packing.</p>
@@ -777,7 +777,7 @@ write to C when all the block results are ready.</p>
<span class="n">s</span><span class="p">[</span><span class="n">CC</span><span class="p">]</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">ko</span><span class="p">,</span> <span class="n">mc</span><span class="p">,</span> <span class="n">ki</span><span class="p">,</span> <span class="n">nc</span><span class="p">)</span>
<span class="n">s</span><span class="p">[</span><span class="n">CC</span><span class="p">]</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">nc</span><span class="p">)</span>
-<span class="c1"># TODO: Add separate optimization step to discuss loop unrolloing</span>
+<span class="c1"># TODO: Add separate optimization step to discuss loop unrolling</span>
<span class="c1"># unrolling is a loop optimization strategy which can reduce branch</span>
<span class="c1"># prediction failures and increases the chance of concurrent execution</span>
<span class="c1"># unroll kfactor loops</span>
@@ -799,7 +799,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110941
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.111951
</pre></div>
</div>
<p>Here is the generated IR after blocking.</p>
@@ -891,7 +891,7 @@ write to C when all the block results are ready.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.145064
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.145564
</pre></div>
</div>
<p>Here is the generated IR after parallelization.</p>
@@ -950,7 +950,7 @@ write to C when all the block results are ready.</p>
our generated code can achieve 60% of the <cite>numpy</cite> performance with MKL.
Note that the outputs on the web page reflect the running times on a non-exclusive
Docker container, thereby they are <em>unreliable</em>. It is highly encouraged to run the
-tutorial by yourself to observe the performance gain acheived by TVM.</p>
+tutorial by yourself to observe the performance gain achieved by TVM.</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-optimize-operators-opt-gemm-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/96137df89d8034b548f407123ec50ce9/opt_gemm.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">opt_gemm.py</span></code></a></p>
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index 73bb36048..50ba0e786 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -300,11 +300,11 @@
<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>00:34.855</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.355</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:32.178</strong>: <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></li>
-<li><p><strong>00:01.456</strong>: <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></li>
-<li><p><strong>00:01.221</strong>: <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></li>
+<li><p><strong>00:32.584</strong>: <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></li>
+<li><p><strong>00:01.479</strong>: <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></li>
+<li><p><strong>00:01.291</strong>: <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></li>
</ul>
</div>
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 77c856222..4164e2dc0 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -300,14 +300,14 @@
<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>05:13.382</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>05:11.293</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
<ul class="simple">
-<li><p><strong>02:34.359</strong>: <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></li>
-<li><p><strong>01:20.451</strong>: <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></li>
-<li><p><strong>00:43.345</strong>: <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></li>
-<li><p><strong>00:17.691</strong>: <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></li>
-<li><p><strong>00:08.877</strong>: <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></li>
-<li><p><strong>00:08.657</strong>: <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></li>
+<li><p><strong>02:32.762</strong>: <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></li>
+<li><p><strong>01:20.411</strong>: <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></li>
+<li><p><strong>00:43.020</strong>: <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></li>
+<li><p><strong>00:17.522</strong>: <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></li>
+<li><p><strong>00:08.978</strong>: <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></li>
+<li><p><strong>00:08.601</strong>: <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></li>
</ul>
</div>
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 a1ade8138..0a225d0c2 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
@@ -984,7 +984,7 @@ cooperative fetching, unrolling and operator fusion.</p>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.360 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.358 ms
</pre></div>
</div>
</div>
@@ -1549,7 +1549,7 @@ In the example below we resume the status and do more 5 trials.</p>
Get devices for measurement successfully!
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 34.359 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 32.762 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_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_conv2d_layer_cuda.py</span></code></a></p>
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 4e74b8269..cea45f7cc 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -878,7 +878,7 @@ 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)
- 9.8982 9.9180 9.9450 9.8317 0.0483
+ 9.8067 9.8261 9.8339 9.7602 0.0331
</pre></div>
</div>
</div>
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 8d37cc283..1d1b9904a 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -897,7 +897,7 @@ 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)
- 747.8577 749.1900 749.2567 745.1264 1.9315
+ 755.5129 755.9471 756.3586 754.2330 0.9205
</pre></div>
</div>
</div>
@@ -919,7 +919,7 @@ 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 20.451 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 20.411 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
<div class="sphx-glr-download 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_autoscheduler/tune_sparse_x86.html b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
index 6559c39c7..8893d6346 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -600,75 +600,77 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute}
- preflattened_buffer_map = {placeholder_6: placeholder_15: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_8: placeholder_16: Buffer(placeholder_13, int32, [33], []), placeholder_7: placeholder_17: Buffer(placeholder_12, int32, [4916], []), placeholder_9: placeholder_18: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_19: Buffer(placeholder_10, float32, [128, 256], [])} {
- for (i0.outer.i1.outer.fused: int32, 0, 256) "parallel" {
- allocate(compute_4: Pointer(global float32), float32, [256]), storage_scope = global {
- for (nb_j.inner: int32, 0, 2) {
- for (i.inner.init: int32, 0, 8) {
- let cse_var_1: int32 = ((i.inner.init*32) + (nb_j.inner*16))
- {
- compute_5: Buffer(compute_4, float32, [256], [])[cse_var_1] = 0f32
- compute_5[(cse_var_1 + 1)] = 0f32
- compute_5[(cse_var_1 + 2)] = 0f32
- compute_5[(cse_var_1 + 3)] = 0f32
- compute_5[(cse_var_1 + 4)] = 0f32
- compute_5[(cse_var_1 + 5)] = 0f32
- compute_5[(cse_var_1 + 6)] = 0f32
- compute_5[(cse_var_1 + 7)] = 0f32
- compute_5[(cse_var_1 + 8)] = 0f32
- compute_5[(cse_var_1 + 9)] = 0f32
- compute_5[(cse_var_1 + 10)] = 0f32
- compute_5[(cse_var_1 + 11)] = 0f32
- compute_5[(cse_var_1 + 12)] = 0f32
- compute_5[(cse_var_1 + 13)] = 0f32
- compute_5[(cse_var_1 + 14)] = 0f32
- compute_5[(cse_var_1 + 15)] = 0f32
- }
- }
- for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
- for (i.inner: int32, 0, 8) {
- let cse_var_21: int32 = (elem_idx*16)
- let cse_var_20: int32 = ((i.inner*32) + (nb_j.inner*16))
- let cse_var_19: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
- let cse_var_18: int32 = (cse_var_20 + 1)
- let cse_var_17: int32 = (cse_var_20 + 11)
- let cse_var_16: int32 = (cse_var_20 + 12)
- let cse_var_15: int32 = (cse_var_20 + 13)
- let cse_var_14: int32 = (cse_var_20 + 14)
- let cse_var_13: int32 = (cse_var_20 + 15)
- let cse_var_12: int32 = (cse_var_20 + 2)
- let cse_var_11: int32 = (cse_var_20 + 3)
- let cse_var_10: int32 = (cse_var_20 + 4)
- let cse_var_9: int32 = (cse_var_20 + 5)
- let cse_var_8: int32 = (cse_var_20 + 6)
- let cse_var_7: int32 = (cse_var_20 + 7)
- let cse_var_6: int32 = (cse_var_20 + 8)
- let cse_var_5: int32 = (cse_var_20 + 9)
- let cse_var_4: int32 = ((floordiv(i0.outer.i1.outer.fused, 16)*2048) + (i.inner*256))
- let cse_var_3: int32 = (cse_var_20 + 10)
+ preflattened_buffer_map = {placeholder_9: placeholder_15: Buffer(placeholder_14, float32, [128, 512], []), compute_1: compute_3: Buffer(compute_2, float32, [128, 512], []), placeholder_5: placeholder_16: Buffer(placeholder_10, float32, [128, 256], []), placeholder_6: placeholder_17: Buffer(placeholder_11, float32, [4916, 16, 1], []), placeholder_7: placeholder_18: Buffer(placeholder_12, int32, [4916], []), placeholder_8: placeholder_19: Buffer(placeholder_13, int32, [33], [])} {
+ for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
+ allocate(compute_4: Pointer(global float32), float32, [2048]), storage_scope = global {
+ for (i.outer.inner: int32, 0, 16) {
+ for (nb_j.inner: int32, 0, 2) {
+ for (i.inner.init: int32, 0, 4) {
+ let cse_var_1: int32 = (((i.outer.inner*128) + (i.inner.init*32)) + (nb_j.inner*16))
{
- compute_5[cse_var_20] = (compute_5[cse_var_20] + (placeholder_1[((placeholder_3[cse_var_19]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
- compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_19]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_19] + elem_idx)])], 0f32)))
+ compute_5: Buffer(compute_4, float32, [2048], [])[cse_var_1] = 0f32
+ compute_5[(cse_var_1 + 1)] = 0f32
+ compute_5[(cse_var_1 + 2)] = 0f32
+ compute_5[(cse_var_1 + 3)] = 0f32
+ compute_5[(cse_var_1 + 4)] = 0f32
+ compute_5[(cse_var_1 + 5)] = 0f32
+ compute_5[(cse_var_1 + 6)] = 0f32
+ compute_5[(cse_var_1 + 7)] = 0f32
+ compute_5[(cse_var_1 + 8)] = 0f32
+ compute_5[(cse_var_1 + 9)] = 0f32
+ compute_5[(cse_var_1 + 10)] = 0f32
+ compute_5[(cse_var_1 + 11)] = 0f32
+ compute_5[(cse_var_1 + 12)] = 0f32
+ compute_5[(cse_var_1 + 13)] = 0f32
+ compute_5[(cse_var_1 + 14)] = 0f32
+ compute_5[(cse_var_1 + 15)] = 0f32
+ }
+ }
+ for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+ for (i.inner: int32, 0, 4) {
+ let cse_var_21: int32 = (elem_idx*16)
+ let cse_var_20: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+ let cse_var_19: int32 = (((i.outer.inner*128) + (i.inner*32)) + (nb_j.inner*16))
+ let cse_var_18: int32 = (cse_var_19 + 1)
+ let cse_var_17: int32 = (cse_var_19 + 11)
+ let cse_var_16: int32 = (cse_var_19 + 12)
+ let cse_var_15: int32 = (cse_var_19 + 13)
+ let cse_var_14: int32 = (cse_var_19 + 14)
+ let cse_var_13: int32 = (cse_var_19 + 15)
+ let cse_var_12: int32 = (cse_var_19 + 2)
+ let cse_var_11: int32 = (cse_var_19 + 3)
+ let cse_var_10: int32 = (cse_var_19 + 4)
+ let cse_var_9: int32 = (cse_var_19 + 5)
+ let cse_var_8: int32 = (cse_var_19 + 6)
+ let cse_var_7: int32 = (cse_var_19 + 7)
+ let cse_var_6: int32 = (cse_var_19 + 8)
+ let cse_var_5: int32 = (cse_var_19 + 9)
+ let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.outer.inner*1024)) + (i.inner*256))
+ let cse_var_3: int32 = (cse_var_19 + 10)
+ {
+ compute_5[cse_var_19] = (compute_5[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_18] = (compute_5[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_12] = (compute_5[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_11] = (compute_5[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_10] = (compute_5[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_9] = (compute_5[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_8] = (compute_5[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_7] = (compute_5[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_6] = (compute_5[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_5] = (compute_5[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_3] = (compute_5[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_17] = (compute_5[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_16] = (compute_5[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_15] = (compute_5[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_14] = (compute_5[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ compute_5[cse_var_13] = (compute_5[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+ }
}
}
}
}
- for (i0.inner: int32, 0, 8) {
- let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
+ for (i0.inner: int32, 0, 64) {
+ let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
compute[ramp(cse_var_22, 1, 32)] = max((compute_5[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
}
}
@@ -708,7 +710,7 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.884 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 2.160 ms
</pre></div>
</div>
<div class="admonition note">
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 9e8a92493..8e7abc40f 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -300,13 +300,13 @@
<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>00:44.438</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:45.767</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:43.546</strong>: <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></li>
-<li><p><strong>00:00.234</strong>: <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></li>
-<li><p><strong>00:00.223</strong>: <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></li>
-<li><p><strong>00:00.218</strong>: <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></li>
-<li><p><strong>00:00.216</strong>: <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></li>
+<li><p><strong>00:44.888</strong>: <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></li>
+<li><p><strong>00:00.237</strong>: <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></li>
+<li><p><strong>00:00.216</strong>: <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></li>
+<li><p><strong>00:00.215</strong>: <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></li>
+<li><p><strong>00:00.212</strong>: <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></li>
</ul>
</div>
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 ad8958324..0d7d21eaf 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -382,7 +382,7 @@ the techniques used in these tutorials. Then we rely on the efficient auto-tuner
to search through this space and pick some good configurations.</p>
<p>If you are familiar with writing cuda schedule, you can find the following
template is very general. Actually this template can be easily modified
-to tune other operators such as depthwise convolution and gemm.
+to tune other operators such as depthwise convolution and GEMM.
In order to fully understand this template, you should be familiar with
the schedule primitives and auto tuning API. You can refer to the above
tutorials and <a class="reference internal" href="../../tutorial/autotvm_matmul_x86.html#tutorial-autotvm-matmul-x86"><span class="std std-ref">autotvm tutorial</span></a></p>
@@ -1142,8 +1142,8 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 4, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
-No: 6 GFLOPS: 95.92/95.92 result: MeasureResult(costs=(0.0024134619583333337,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6140697002410889, timestamp=1654644371.017709) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
-No: 7 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 6 GFLOPS: 42.64/42.64 result: MeasureResult(costs=(0.005429114789473685,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5012328624725342, timestamp=1654669018.3772974) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+No: 7 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1266,7 +1266,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
-No: 8 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 8 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1389,7 +1389,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
-No: 9 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 9 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1512,7 +1512,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
-No: 10 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 10 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
res = future.result()
File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1530,7 +1530,7 @@ No: 10 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
TimeoutError
[('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
-No: 11 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 11 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1653,7 +1653,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
-No: 12 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 12 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1776,7 +1776,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
-No: 13 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 13 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1899,7 +1899,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
-No: 14 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 14 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2022,7 +2022,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
-No: 15 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 15 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2145,7 +2145,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
-No: 16 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 16 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2268,7 +2268,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
-No: 17 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 17 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2391,7 +2391,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 64, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
-No: 18 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 18 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2514,7 +2514,7 @@ Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
raise InstantiationError("Skipped because of invalid gpu kernel")
tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
-No: 19 GFLOPS: 0.00/95.92 result: Traceback (most recent call last):
+No: 19 GFLOPS: 0.00/42.64 result: Traceback (most recent call last):
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
yield remote, remote.load_module(os.path.split(build_result.filename)[1])
File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2602,7 +2602,7 @@ tvm._ffi.base.TVMError: Traceback (most recent call last):
15: _PyEval_EvalFrameDefault
14: 0x0000000000537c30
13: _PyObject_FastCallKeywords
- 12: 0x00007fb37c553fa2
+ 12: 0x00007f46e1a5dfa2
11: _ctypes_callproc
10: ffi_call
9: ffi_call_unix64
@@ -2667,7 +2667,7 @@ Traceback (most recent call last):
21: _PyFunction_FastCallKeywords
20: _PyEval_EvalFrameDefault
19: _PyFunction_FastCall [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
-No: 20 GFLOPS: 144.97/144.97 result: MeasureResult(costs=(0.00159690949,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.464625597000122, timestamp=1654644397.5587177) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+No: 20 GFLOPS: 144.29/144.29 result: MeasureResult(costs=(0.0016044719299999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4524359703063965, timestamp=1654669045.0340207) [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
</pre></div>
</div>
<p>Finally we can inspect the best config from log file, check correctness,
@@ -2706,7 +2706,7 @@ and measure running time.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best config:
[('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
-Time cost of this operator: 0.001964
+Time cost of this operator: 0.001957
</pre></div>
</div>
<div class="sphx-glr-footer class 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/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 9134d472b..f6f57bf5d 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -556,10 +556,10 @@ the tuned operator.</p>
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 314.1 98.745 (1, 2, 10, 10, 3) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.07 0.965 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.922 0.29 (1, 1, 10, 10, 3) 1 1
-Total_time - 318.092 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 314.6 98.747 (1, 2, 10, 10, 3) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.091 0.97 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.901 0.283 (1, 1, 10, 10, 3) 1 1
+Total_time - 318.592 - - - -
</pre></div>
</div>
</div>
@@ -611,10 +611,10 @@ 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
--------- --- -------- ------- ----- ------ -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 154.1 98.277 (1, 6, 10, 10, 1) 2 1
-tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.738 1.108 (1, 6, 10, 10) 1 1
-tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.963 0.614 (1, 1, 10, 10, 3) 1 1
-Total_time - 156.801 - - - -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 226.2 98.8 (1, 1, 10, 10, 6) 2 1
+tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.935 0.845 (1, 6, 10, 10) 1 1
+tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.812 0.355 (1, 3, 10, 10, 1) 1 1
+Total_time - 228.947 - - - -
</pre></div>
</div>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
diff --git a/docs/how_to/work_with_microtvm/micro_train.html b/docs/how_to/work_with_microtvm/micro_train.html
index 34bfbe95e..54d82cf5b 100644
--- a/docs/how_to/work_with_microtvm/micro_train.html
+++ b/docs/how_to/work_with_microtvm/micro_train.html
@@ -552,8 +552,8 @@ objects to other stuff? We can display some examples from our datasets using <co
</div>
<img alt="../../_images/sphx_glr_micro_train_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_micro_train_001.png" />
<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpfxj8a1el/images/target contains 8144 images
-/tmp/tmpfxj8a1el/images/random contains 5000 images
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/tmp/tmpv4vn17m7/images/target contains 8144 images
+/tmp/tmpv4vn17m7/images/random contains 5000 images
</pre></div>
</div>
</div>
@@ -666,11 +666,11 @@ the time on our validation set).</p>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/3
-328/328 - 54s - loss: 0.2189 - accuracy: 0.9242 - val_loss: 0.1366 - val_accuracy: 0.9592
+328/328 - 54s - loss: 0.2052 - accuracy: 0.9296 - val_loss: 0.1563 - val_accuracy: 0.9543
Epoch 2/3
-328/328 - 52s - loss: 0.0989 - accuracy: 0.9638 - val_loss: 0.1167 - val_accuracy: 0.9615
+328/328 - 52s - loss: 0.0980 - accuracy: 0.9630 - val_loss: 0.1520 - val_accuracy: 0.9585
Epoch 3/3
-328/328 - 52s - loss: 0.0664 - accuracy: 0.9753 - val_loss: 0.1076 - val_accuracy: 0.9637
+328/328 - 52s - loss: 0.0603 - accuracy: 0.9768 - val_loss: 0.1156 - val_accuracy: 0.9656
</pre></div>
</div>
</div>
@@ -959,7 +959,7 @@ 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>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes 36.910 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes 10.613 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-train-py">
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b52cec46baf4f78d6bcd94cbe269c8a6/micro_train.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_train.py</span></code></a></p>
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 9a173ab56..fb4ac5b45 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -300,14 +300,14 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>05:22.774</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>05:57.419</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
<ul class="simple">
-<li><p><strong>04:36.910</strong>: <a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></li>
-<li><p><strong>00:41.661</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
-<li><p><strong>00:03.587</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
+<li><p><strong>05:10.613</strong>: <a class="reference internal" href="micro_train.html#sphx-glr-how-to-work-with-microtvm-micro-train-py"><span class="std std-ref">Training Vision Models for microTVM on Arduino</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_train.py</span></code>)</p></li>
+<li><p><strong>00:42.484</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
+<li><p><strong>00:03.716</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
<li><p><strong>00:00.209</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
-<li><p><strong>00:00.206</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
-<li><p><strong>00:00.201</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:00.199</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
+<li><p><strong>00:00.199</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_relay/build_gcn.html b/docs/how_to/work_with_relay/build_gcn.html
index 89c080532..44a5f386c 100644
--- a/docs/how_to/work_with_relay/build_gcn.html
+++ b/docs/how_to/work_with_relay/build_gcn.html
@@ -671,7 +671,7 @@ this method is temporary and will be updated in next few weeks when we have spar
</div>
<div class="section" id="compile-and-run-with-tvm">
<h2>Compile and run with TVM<a class="headerlink" href="#compile-and-run-with-tvm" title="Permalink to this headline">¶</a></h2>
-<p>Export the weigths from PyTorch model to Python Dict</p>
+<p>Export the weights from PyTorch model to Python Dict</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model_params</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">param_tensor</span> <span class="ow">in</span> <span class="n">torch_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">():</span>
<span class="n">model_params</span><span class="p">[</span><span class="n">param_tensor</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()[</span><span class="n">param_tensor</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index 9138803dc..e4ca5d5b4 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -300,11 +300,11 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:11.906</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:12.095</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:09.955</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
-<li><p><strong>00:01.724</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
-<li><p><strong>00:00.226</strong>: <a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></li>
+<li><p><strong>00:10.170</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
+<li><p><strong>00:01.710</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
+<li><p><strong>00:00.215</strong>: <a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_relay/using_relay_viz.html b/docs/how_to/work_with_relay/using_relay_viz.html
index 0d86ac825..3eeda20cc 100644
--- a/docs/how_to/work_with_relay/using_relay_viz.html
+++ b/docs/how_to/work_with_relay/using_relay_viz.html
@@ -339,7 +339,7 @@
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/chiwwang">Chi-Wei Wang</a></p>
<p>Relay IR module can contain lots of operations. Although an individual
operation is usually easy to understand, putting them together can cause
-a complicated, hard-to-read graph. Things can get even worse with optimiztion-passes
+a complicated, hard-to-read graph. Things can get even worse with optimization-passes
coming into play.</p>
<p>This utility visualizes an IR module as nodes and edges. It defines a set of interfaces including
parser, plotter(renderer), graph, node, and edges.
@@ -420,7 +420,7 @@ We should see <code class="docutils literal notranslate"><span class="pre">main<
<h2>Customize Parser for Interested Relay Types<a class="headerlink" href="#customize-parser-for-interested-relay-types" title="Permalink to this headline">¶</a></h2>
<p>Sometimes we want to emphasize interested information, or parse things differently for a specific usage.
It is possible to provide customized parsers as long as it obeys the interface.
-Here demostrate how to customize parsers for <code class="docutils literal notranslate"><span class="pre">relay.var</span></code>.
+Here demonstrate how to customize parsers for <code class="docutils literal notranslate"><span class="pre">relay.var</span></code>.
We need to implement abstract interface <a class="reference internal" href="../../reference/api/python/contrib.html#tvm.contrib.relay_viz.interface.VizParser" title="tvm.contrib.relay_viz.interface.VizParser"><code class="xref py py-class docutils literal notranslate"><span class="pre">tvm.contrib.relay_viz.interface.VizParser</span></code></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">YourAwesomeParser</span><span class="p">(</span><span class="n">VizParser</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -478,7 +478,7 @@ We add a hook duplicating above <code class="docutils literal notranslate"><span
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">viz_node</span><span class="p">)</span>
<span class="c1"># if it's AwesomeVar, duplicate it.</span>
<span class="k">if</span> <span class="n">viz_node</span><span class="o">.</span><span class="n">type_name</span> <span class="o">==</span> <span class="s2">"AwesomeVar"</span><span class="p">:</span>
- <span class="n">duplicated_id</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"duplciated_</span><span class="si">{</span><span class="n">viz_node</span><span class="o">.</span><span class="n">identity</span><span class="si">}</span><span class="s2">"</span>
+ <span class="n">duplicated_id</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"duplicated_</span><span class="si">{</span><span class="n">viz_node</span><span class="o">.</span><span class="n">identity</span><span class="si">}</span><span class="s2">"</span>
<span class="n">duplicated_type</span> <span class="o">=</span> <span class="s2">"double AwesomeVar"</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><a href="../../reference/api/python/contrib.html#tvm.contrib.relay_viz.interface.VizNode" title="View documentation for tvm.contrib.relay_viz.interface.VizNode"><span class="n">VizNode</span></a><span class="p">(</span><span class="n">duplicated_id</span><span class="p">,</span> <span class="n">duplicated_type</span><span class="p">,</span> <span cl [...]
<span class="c1"># connect the duplicated var to the original one</span>
diff --git a/docs/how_to/work_with_schedules/extern_op.html b/docs/how_to/work_with_schedules/extern_op.html
index e951acdf1..633f695bb 100644
--- a/docs/how_to/work_with_schedules/extern_op.html
+++ b/docs/how_to/work_with_schedules/extern_op.html
@@ -347,7 +347,7 @@ it is also helpful to incorporate manual written code into
the pipeline. For example, we might want to use cuDNN for
some of the convolution kernels and define the rest of the stages.</p>
<p>TVM supports these black box function calls natively.
-Specfically, TVM support all the tensor functions that are DLPack compatible.
+Specifically, TVM support all the tensor functions that are DLPack compatible.
Which means we can call any function with POD types(pointer, int, float)
or pointer to DLTensor as argument.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">print_function</span>
@@ -371,7 +371,7 @@ of output tensors. In the second argument we provide the list of inputs.</p>
The compute function takes list of symbolic placeholder for the inputs,
list of symbolic placeholder for the outputs and returns the executing statement.</p>
<p>In this case we simply call a registered TVM function, which invokes a CBLAS call.
-TVM does not control internal of the extern array function and treats it as blackbox.
+TVM does not control internal of the extern array function and treats it as black-box.
We can further mix schedulable TVM calls that add a bias term to the result.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">1024</span>
<span class="n">l</span> <span class="o">=</span> <span class="mi">128</span>
diff --git a/docs/how_to/work_with_schedules/intrin_math.html b/docs/how_to/work_with_schedules/intrin_math.html
index 791fee274..f9e930ac7 100644
--- a/docs/how_to/work_with_schedules/intrin_math.html
+++ b/docs/how_to/work_with_schedules/intrin_math.html
@@ -348,7 +348,7 @@ For example <code class="code docutils literal notranslate"><span class="pre">ex
<p>These functions are target system dependent and may have different
names of different target platforms. In this tutorial, we will learn
how we can invoke these target specific functions, and how we can unify
-the interface via tvm’s intrinsic API.</p>
+the interface via TVM’s intrinsic API.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">print_function</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
diff --git a/docs/how_to/work_with_schedules/scan.html b/docs/how_to/work_with_schedules/scan.html
index a29d9b222..c5b495dc3 100644
--- a/docs/how_to/work_with_schedules/scan.html
+++ b/docs/how_to/work_with_schedules/scan.html
@@ -378,7 +378,7 @@ update over the time domain.</p>
<div class="section" id="schedule-the-scan-cell">
<h2>Schedule the Scan Cell<a class="headerlink" href="#schedule-the-scan-cell" title="Permalink to this headline">¶</a></h2>
<p>We can schedule the body of the scan by scheduling the update and
-init part seperately. Note that it is invalid to schedule the
+init part separately. Note that it is invalid to schedule the
first iteration dimension of the update part.
To split on the time iteration, user can schedule on scan_op.scan_axis instead.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <a href="../../reference/api/python/te.html#tvm.te.create_schedule" title="View documentation for tvm.te.create_schedule"><span class="n">te</span><span class="o">.</span><span class="n">create_schedule</span></a><span class="p">(</span><span class="n">s_scan</span><span class="o">.</span><span class="n">op</span><span class="p">)</span>
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index cdbbbf3d1..36d00f685 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -300,16 +300,16 @@
<div class="section" id="computation-times">
<span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:05.833</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.959</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
<ul class="simple">
-<li><p><strong>00:02.134</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
-<li><p><strong>00:01.198</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
-<li><p><strong>00:00.745</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
-<li><p><strong>00:00.729</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
-<li><p><strong>00:00.312</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
-<li><p><strong>00:00.251</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
-<li><p><strong>00:00.239</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
-<li><p><strong>00:00.225</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
+<li><p><strong>00:02.170</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
+<li><p><strong>00:01.236</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
+<li><p><strong>00:00.746</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
+<li><p><strong>00:00.736</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
+<li><p><strong>00:00.315</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
+<li><p><strong>00:00.284</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
+<li><p><strong>00:00.242</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
+<li><p><strong>00:00.230</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
</ul>
</div>
diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index a3b873071..a06dba5db 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -552,7 +552,7 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
buffer_map = {A_1: A, B_1: B, C_1: C}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [1024, 64], []), B_1: B_3: Buffer(B_2, float32, [512, 64], []), C_1: C_3: Buffer(C_2, float32, [1024, 512], [])} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpcsetdwf3/input0.cc'\nsource_filename = \"/tmp/tmpcsetdwf3/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = allo [...]
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpt78h14xo/input0.cc'\nsource_filename = \"/tmp/tmpt78h14xo/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = allo [...]
for (i, 0, 1024) {
for (j.outer: int32, 0, 32) {
@tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/reference/api/doxygen/apply__history__best_8h_source.html b/docs/reference/api/doxygen/apply__history__best_8h_source.html
index 067b33c0e..5f870c9ab 100644
--- a/docs/reference/api/doxygen/apply__history__best_8h_source.html
+++ b/docs/reference/api/doxygen/apply__history__best_8h_source.html
@@ -78,7 +78,7 @@ $(function() {
<div class="ttc" id="array_8h_html"><div class="ttname"><a href="array_8h.html">array.h</a></div><div class="ttdoc">Runtime Array container types. </div></div>
<div class="ttc" id="classtvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each fie...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode_html_a86fc3705d9a37c98f75a9a843878178a"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html#a86fc3705d9a37c98f75a9a843878178a">tvm::meta_schedule::ApplyHistoryBestNode::Query</a></div><div class="ttdeci">Optional< IRModule > Query(runtime::String task_name, IRModule mod, Target target, Optional< Array< IRModule >> dispatched)</div><div class="ttdoc">Query the best en [...]
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1Database_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1Database.html">tvm::meta_schedule::Database</a></div><div class="ttdoc">Managed reference to DatabaseNode. </div><div class="ttdef"><b>Definition:</b> database.h:288</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1Database_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1Database.html">tvm::meta_schedule::Database</a></div><div class="ttdoc">Managed reference to DatabaseNode. </div><div class="ttdef"><b>Definition:</b> database.h:289</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Array_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Array.html">tvm::runtime::Array</a></div><div class="ttdoc">Array, container representing a contiguous sequence of ObjectRefs. </div><div class="ttdef"><b>Definition:</b> array.h:270</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode_html_a6be4c52d4ff271c11d2f2daf53861778"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html#a6be4c52d4ff271c11d2f2daf53861778">tvm::meta_schedule::ApplyHistoryBestNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> apply_history_best.h:46</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1String_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1String.html">tvm::runtime::String</a></div><div class="ttdoc">Reference to string objects. </div><div class="ttdef"><b>Definition:</b> string.h:129</div></div>
diff --git a/docs/reference/api/doxygen/classes.html b/docs/reference/api/doxygen/classes.html
index daabb3a79..8d708ab15 100644
--- a/docs/reference/api/doxygen/classes.html
+++ b/docs/reference/api/doxygen/classes.html
@@ -70,8 +70,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html">AccessAnalyzer</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html">CorrelationAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1support_1_1Span_1_1iterator__base.h [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html">AccessAnalyzerNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html">CostModel</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_ [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool1DAttrs.html">AdaptivePool1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html">CostModel</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1auto__scheduler_1_1AttachMapNode_1_1It [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html">IterMap [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.html"> [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool2DAttrs.html">AdaptivePool2DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::meta_schedule</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html">IterMap [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1AdaptivePool3DAttrs.html">AdaptivePool3DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModelNode.html">CostModelNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.html"> [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Add.html">Add</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1CountNode.html">CountNode</a> (<a class="el" href="namespacetvm_1_1runtime_1_1profiling.html">tvm::runtime::profiling</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapResult.html">IterMapResult</a> (<a class="el" hr [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1AddNode.html">AddNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html">CropAndResizeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IterMapResultNode.html">IterMapResultNode</a> (<a class="el" href="name [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ADT.html">ADT</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_d"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  d  </div></td></tr></table>
@@ -178,8 +178,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="structtvm_1_1tir_1_1BlockInfo.html">BlockInfo</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html">FloorModNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ModuleNode.html">ModuleNode</a> (<a class="el" href="namespacetvm_1_1runtime.html"> [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockNode.html">BlockNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html">FollowFusedSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Mul.html">Mul</a> (<a class="el" href [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html">BlockRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html">FollowFusedSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1MulNode.html">MulNode</ [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html">BlockRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html">FollowSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">Multi [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRV.html">BlockRV</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html">FollowSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBox [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html">BlockRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html">FollowSplitStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html">Multi [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRV.html">BlockRV</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html">FollowSplitStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html">MultiBox [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html">BlockRVNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1For.html">For</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html">Mutator</a> (<a class="el" href="namespacetvm_1_1meta__schedule.html">tvm::me [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockScope.html">BlockScope</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1ForNode.html">ForNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1MutatorNode.html">MutatorNode</a> (<a class="el" href="namespacetvm_1_1meta__schedule [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html">BlockScopeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1micro__rpc_1_1FrameBuffer.html">FrameBuffer</a> (<a class="el" href="namespacetvm_1_1runtime_1_1micro__rpc.html">tvm::runtime::micro_rpc</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_n"></a><table border="0" cellspacing= [...]
@@ -201,8 +201,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html">BufferRealize</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_g"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  g  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html">NullOptType</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ScatterNDAttrs.html">ScatterNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="unionTVMValue.html">TVMValue</a>   </td></tr>
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html">BufferRealizeNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_o"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  o  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Type.html">Type</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html">BufferRegion</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1te.html">tvm: [...]
+</td><td valign="top"><a class="el" href="classtvm_1_1te_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Type.html">Type</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td></tr>
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html">BufferRegion</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html">GatherAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Schedule.html">Schedule</a> (<a class="el" href="namespacetvm_1_1tir.html">tv [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html">BufferRegionNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GatherNDAttrs.html">GatherNDAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html">ObjAllocatorBase</a> (<a class="el" hr [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStore.html">BufferStore</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>) &# [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html">BufferStoreNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1GenericFunc.html">GenericFunc</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html">ObjectEqual</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::ru [...]
@@ -255,8 +255,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html">ComputeAtStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html">ComputeDAG</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfo.html">PassInfo</a> (<a class="el" href="nam [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html">ComputeDAGNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMap.html">IndexMap</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoNode</a> (<a class [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMapNode.html">IndexMapNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html">PassInstr [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNod [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html">ComputeInlineStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1IndexMapNode.html">IndexMapNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html">PassInstr [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html">ComputeInlineStepNode</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html">InitOpAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNod [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOp.html">ComputeOp</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassNode.html">PassNode</a> (<a class="el" href="namespacetvm_1_1 [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html">ComputeOpNode</a> (<a class="el" href="namespacetvm_1_1te.html">tvm::te</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html">InstanceNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Pattern.html">Pattern</a> (<a class="el" href="namespacetvm_1_1r [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html">ComputeRootStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)   </td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html">Instruction</a> (<a class="el" href="namespacetvm_1_1runtime_1_1vm.html">tvm::runtime::vm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConst [...]
@@ -272,8 +272,8 @@ $(function() {
<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">Pattern [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1Integer.html">Integer</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html">PatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm: [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1Constructor.html">Constructor</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> (<a class="el" href="namespacetvm_1_1rela [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode</a> (<a class="el" href [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html">PercentNode</a> (<a cla [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html">InterpreterClosureObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html">PatternWildcardNode</a> (<a class="el" href [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html">ConstructorValue</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html">IntGroupBounds</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1PercentNode.html">PercentNode</a> (<a cla [...]
<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html">ConstructorValueObj</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html">IntGroupBoundsNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html">PlaceholderOp</a> (<a cl [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html">PlaceholderOpNode</a> (<a class="el" href="namespacetvm_1_1te.ht [...]
<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html">NDArray::ContainerBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)   </td><td valign="top"><a class="el" href="classtvm_1_1PointerType.html">PointerType</a> (<a class="el" href="namespacetvm.html">tvm< [...]
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord-members.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord-members.html
index 7b5b020f6..25f3e943e 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord-members.html
@@ -87,7 +87,7 @@ $(function() {
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a4744bf4a1b48f202d41b51dc5e08e6ee">operator<</a>(const ObjectRef &other) const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#affdf1b8cdb36e140de7b3ad7064e4617">operator==</a>(const ObjectRef &other) const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#ae31a5b9f40781d60a2901994ead700e8">same_as</a>(const ObjectRef &other) const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#a8495d5cbf2d11eaca3a5c1f6a25e5ea7">TuningRecord</a>(tir::Trace trace, Array< FloatImm > run_secs, Workload workload, Target target, Array< ArgInfo > args_info)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html">tvm::meta_schedule::TuningRecord</a></td><td class="entry"><span class="mlabel">explicit</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#aa4699af50f91bda306e6c199766c4757">TuningRecord</a>(tir::Trace trace, Workload workload, Optional< Array< FloatImm >> run_secs, Optional< Target > target, Optional< Array< ArgInfo >> args_info)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html">tvm::meta_schedule::TuningRecord</a></td><td class="entry"><span class=" [...]
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#adb7a5f83ed6236b9b9efc59219c95d01">TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS</a>(TuningRecord, runtime::ObjectRef, TuningRecordNode)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html">tvm::meta_schedule::TuningRecord</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a4e7cdb1574b93a59e784d70aa47b8da7">unique</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html#a0ae0da21d247cd87ea94fe3777c4405e">use_count</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord.html
index 379d8292d..da0d9e0df 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecord.html
@@ -90,9 +90,9 @@ Collaboration diagram for tvm::meta_schedule::TuningRecord:</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
-<tr class="memitem:a8495d5cbf2d11eaca3a5c1f6a25e5ea7"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#a8495d5cbf2d11eaca3a5c1f6a25e5ea7">TuningRecord</a> (<a class="el" href="classtvm_1_1tir_1_1Trace.html">tir::Trace</a> trace, <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> > run_sec [...]
-<tr class="memdesc:a8495d5cbf2d11eaca3a5c1f6a25e5ea7"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classtvm_1_1Constructor.html" title="Managed reference to ConstructorNode. ">Constructor</a> of a tuning record. <a href="#a8495d5cbf2d11eaca3a5c1f6a25e5ea7">More...</a><br /></td></tr>
-<tr class="separator:a8495d5cbf2d11eaca3a5c1f6a25e5ea7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa4699af50f91bda306e6c199766c4757"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#aa4699af50f91bda306e6c199766c4757">TuningRecord</a> (<a class="el" href="classtvm_1_1tir_1_1Trace.html">tir::Trace</a> trace, <a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html">Workload</a> workload, <a class="el" href="classtvm_1_1runtime_1_1Optional.h [...]
+<tr class="memdesc:aa4699af50f91bda306e6c199766c4757"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classtvm_1_1Constructor.html" title="Managed reference to ConstructorNode. ">Constructor</a> of a tuning record. <a href="#aa4699af50f91bda306e6c199766c4757">More...</a><br /></td></tr>
+<tr class="separator:aa4699af50f91bda306e6c199766c4757"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:adb7a5f83ed6236b9b9efc59219c95d01"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#adb7a5f83ed6236b9b9efc59219c95d01">TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS</a> (<a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html">TuningRecord</a>, <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">runtime::ObjectRef</a>, <a class="el" h [...]
<tr class="separator:adb7a5f83ed6236b9b9efc59219c95d01"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="inherit_header pub_methods_classtvm_1_1runtime_1_1ObjectRef"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classtvm_1_1runtime_1_1ObjectRef')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></td></tr>
@@ -168,8 +168,8 @@ Additional Inherited Members</h2></td></tr>
<div class="textblock"><p>The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a>. </p>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </dd></dl>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a8495d5cbf2d11eaca3a5c1f6a25e5ea7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8495d5cbf2d11eaca3a5c1f6a25e5ea7">◆ </a></span>TuningRecord()</h2>
+<a id="aa4699af50f91bda306e6c199766c4757"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aa4699af50f91bda306e6c199766c4757">◆ </a></span>TuningRecord()</h2>
<div class="memitem">
<div class="memproto">
@@ -186,25 +186,25 @@ Additional Inherited Members</h2></td></tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> > </td>
- <td class="paramname"><em>run_secs</em>, </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html">Workload</a> </td>
+ <td class="paramname"><em>workload</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html">Workload</a> </td>
- <td class="paramname"><em>workload</em>, </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> >> </td>
+ <td class="paramname"><em>run_secs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1Target.html">Target</a> </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1Target.html">Target</a> > </td>
<td class="paramname"><em>target</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html">ArgInfo</a> > </td>
+ <td class="paramtype"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html">ArgInfo</a> >> </td>
<td class="paramname"><em>args_info</em> </td>
</tr>
<tr>
@@ -224,8 +224,8 @@ Additional Inherited Members</h2></td></tr>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">trace</td><td>The trace of the tuning record. </td></tr>
- <tr><td class="paramname">run_secs</td><td>The running time of the tuning record. </td></tr>
<tr><td class="paramname">workload</td><td>The workload of the tuning record. </td></tr>
+ <tr><td class="paramname">run_secs</td><td>The running time of the tuning record. </td></tr>
<tr><td class="paramname">target</td><td>The target of the tuning record. </td></tr>
<tr><td class="paramname">args_info</td><td>The argument information of the tuning record. </td></tr>
</table>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode-members.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode-members.html
index 9b8b8c1ed..6803959ac 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode-members.html
@@ -78,7 +78,7 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a74e9f076b50b8b335b4a321e9b0bf03c">_type_has_method_visit_attrs</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#af6aed95d70af7e44ce376a8d7be6c5f1">_type_index</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#aa90a4ab949484eac753ff45a7693a829">_type_key</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#abd204834fbe398290cdd419e26851b6a">args_info</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a09f1921d607d7ef4397876c15d2f0b08">args_info</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#ac2e3ed776662e66112c37ec4fcb091d1">AsJSON</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a70fb5361147634605d6595bb89381f03">DecRef</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">protected</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#af4407d2b59132e803ff791482dbe0145">deleter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
@@ -95,9 +95,9 @@ $(function() {
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ae341e561272ff43cdcbc927bc29ac50d">operator=</a>(Object &&other)</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a0d492efee331e2239a093f4b2017c10f">ref_counter_</a></td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a55549a6c23987890246248682560a03d">RefCounterType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a5ee669c272da1447f3f666d9faf5e449">run_secs</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a00aeeedbe4d1d6008224d98c62739581">run_secs</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#ad94d79729ac85aa7c976e23d39066383">RuntimeTypeIndex</a>()</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#ab9cbbf8eb7941995e9c7552948eac02b">target</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a45a380cfa2edfd63056fb1a00f9aac35">target</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a8cc2d64f796593a1a774eef259f17b29">trace</a></td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a6270ef8a153eca3d2571ee2d34feb4ff">TVM_DECLARE_FINAL_OBJECT_INFO</a>(TuningRecordNode, runtime::Object)</td><td class="entry"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html#a481f01923b14e1851ebd38506e9c66ea">type_index</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode.html b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode.html
index b3ab472a1..e384b8a40 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode.html
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode.html
@@ -85,7 +85,7 @@ Inheritance diagram for tvm::meta_schedule::TuningRecordNode:</div>
<div class="dynheader">
Collaboration diagram for tvm::meta_schedule::TuningRecordNode:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1meta__schedule_1_1TuningRecordNode__coll__graph.svg" width="1408" height="1360"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1meta__schedule_1_1TuningRecordNode__coll__graph.svg" width="1478" height="1360"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
<table class="memberdecls">
@@ -126,18 +126,18 @@ Public Attributes</h2></td></tr>
<tr class="memitem:a8cc2d64f796593a1a774eef259f17b29"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1tir_1_1Trace.html">tir::Trace</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a8cc2d64f796593a1a774eef259f17b29">trace</a></td></tr>
<tr class="memdesc:a8cc2d64f796593a1a774eef259f17b29"><td class="mdescLeft"> </td><td class="mdescRight">The trace tuned. <a href="#a8cc2d64f796593a1a774eef259f17b29">More...</a><br /></td></tr>
<tr class="separator:a8cc2d64f796593a1a774eef259f17b29"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a5ee669c272da1447f3f666d9faf5e449"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a5ee669c272da1447f3f666d9faf5e449">run_secs</a></td></tr>
-<tr class="memdesc:a5ee669c272da1447f3f666d9faf5e449"><td class="mdescLeft"> </td><td class="mdescRight">The profiling result in seconds. <a href="#a5ee669c272da1447f3f666d9faf5e449">More...</a><br /></td></tr>
-<tr class="separator:a5ee669c272da1447f3f666d9faf5e449"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a42c87f1ec62dae6806c3fe9629c5e7f0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html">Workload</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a42c87f1ec62dae6806c3fe9629c5e7f0">workload</a> {nullptr}</td></tr>
<tr class="memdesc:a42c87f1ec62dae6806c3fe9629c5e7f0"><td class="mdescLeft"> </td><td class="mdescRight">The workload. <a href="#a42c87f1ec62dae6806c3fe9629c5e7f0">More...</a><br /></td></tr>
<tr class="separator:a42c87f1ec62dae6806c3fe9629c5e7f0"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab9cbbf8eb7941995e9c7552948eac02b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1Target.html">Target</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#ab9cbbf8eb7941995e9c7552948eac02b">target</a></td></tr>
-<tr class="memdesc:ab9cbbf8eb7941995e9c7552948eac02b"><td class="mdescLeft"> </td><td class="mdescRight">The target for tuning. <a href="#ab9cbbf8eb7941995e9c7552948eac02b">More...</a><br /></td></tr>
-<tr class="separator:ab9cbbf8eb7941995e9c7552948eac02b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abd204834fbe398290cdd419e26851b6a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html">ArgInfo</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#abd204834fbe398290cdd419e26851b6a">args_info</a></td></tr>
-<tr class="memdesc:abd204834fbe398290cdd419e26851b6a"><td class="mdescLeft"> </td><td class="mdescRight">The argument information. <a href="#abd204834fbe398290cdd419e26851b6a">More...</a><br /></td></tr>
-<tr class="separator:abd204834fbe398290cdd419e26851b6a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a00aeeedbe4d1d6008224d98c62739581"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a> > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a00aeeedbe4d1d6008224d98c62739581">run_secs</a></td></tr>
+<tr class="memdesc:a00aeeedbe4d1d6008224d98c62739581"><td class="mdescLeft"> </td><td class="mdescRight">The profiling result in seconds. <a href="#a00aeeedbe4d1d6008224d98c62739581">More...</a><br /></td></tr>
+<tr class="separator:a00aeeedbe4d1d6008224d98c62739581"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a45a380cfa2edfd63056fb1a00f9aac35"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1Target.html">Target</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a45a380cfa2edfd63056fb1a00f9aac35">target</a></td></tr>
+<tr class="memdesc:a45a380cfa2edfd63056fb1a00f9aac35"><td class="mdescLeft"> </td><td class="mdescRight">The target for tuning. <a href="#a45a380cfa2edfd63056fb1a00f9aac35">More...</a><br /></td></tr>
+<tr class="separator:a45a380cfa2edfd63056fb1a00f9aac35"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a09f1921d607d7ef4397876c15d2f0b08"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a>< <a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a>< <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html">ArgInfo</a> > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a09f1921d607d7ef4397876c15d2f0b08" [...]
+<tr class="memdesc:a09f1921d607d7ef4397876c15d2f0b08"><td class="mdescLeft"> </td><td class="mdescRight">The argument information. <a href="#a09f1921d607d7ef4397876c15d2f0b08">More...</a><br /></td></tr>
+<tr class="separator:a09f1921d607d7ef4397876c15d2f0b08"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-attribs"></a>
Static Public Attributes</h2></td></tr>
@@ -305,14 +305,14 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<a id="abd204834fbe398290cdd419e26851b6a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#abd204834fbe398290cdd419e26851b6a">◆ </a></span>args_info</h2>
+<a id="a09f1921d607d7ef4397876c15d2f0b08"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a09f1921d607d7ef4397876c15d2f0b08">◆ </a></span>args_info</h2>
<div class="memitem">
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<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html">ArgInfo</a>> tvm::meta_schedule::TuningRecordNode::args_info</td>
+ <td class="memname"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a><<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html">ArgInfo</a>> > tvm::meta_schedule::TuningRecordNode::args_info</td>
</tr>
</table>
</div><div class="memdoc">
@@ -321,14 +321,14 @@ Additional Inherited Members</h2></td></tr>
</div>
</div>
-<a id="a5ee669c272da1447f3f666d9faf5e449"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5ee669c272da1447f3f666d9faf5e449">◆ </a></span>run_secs</h2>
+<a id="a00aeeedbe4d1d6008224d98c62739581"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a00aeeedbe4d1d6008224d98c62739581">◆ </a></span>run_secs</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a>> tvm::meta_schedule::TuningRecordNode::run_secs</td>
+ <td class="memname"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a><<a class="el" href="classtvm_1_1runtime_1_1Array.html">Array</a><<a class="el" href="classtvm_1_1FloatImm.html">FloatImm</a>> > tvm::meta_schedule::TuningRecordNode::run_secs</td>
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</table>
</div><div class="memdoc">
@@ -337,14 +337,14 @@ Additional Inherited Members</h2></td></tr>
</div>
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-<a id="ab9cbbf8eb7941995e9c7552948eac02b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab9cbbf8eb7941995e9c7552948eac02b">◆ </a></span>target</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname"><a class="el" href="classtvm_1_1Target.html">Target</a> tvm::meta_schedule::TuningRecordNode::target</td>
+ <td class="memname"><a class="el" href="classtvm_1_1runtime_1_1Optional.html">Optional</a><<a class="el" href="classtvm_1_1Target.html">Target</a>> tvm::meta_schedule::TuningRecordNode::target</td>
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+<polygon fill="none" stroke="#404040" points="497.1423,-96.8943 500.1513,-90.341 507.3579,-90.598 504.3489,-97.1513 497.1423,-96.8943"/>
+<text text-anchor="middle" x="505" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +workload</text>
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<!-- Node5 -->
<g id="node4" class="node">
<title>Node5</title>
<g id="a_node4"><a xlink:href="classtvm_1_1runtime_1_1ObjectRef.html" target="_top" xlink:title="Base class of all object reference. ">
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-<text text-anchor="middle" x="651.5" y="-773.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
-<polyline fill="none" stroke="#000000" points="584.5,-766.5 718.5,-766.5 "/>
-<text text-anchor="start" x="592.5" y="-754.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
-<polyline fill="none" stroke="#000000" points="584.5,-747.5 718.5,-747.5 "/>
-<text text-anchor="start" x="592.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="592.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
-<text text-anchor="start" x="592.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
-<text text-anchor="start" x="592.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
-<text text-anchor="start" x="592.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
-<text text-anchor="start" x="592.5" y="-680.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
-<text text-anchor="start" x="592.5" y="-669.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
-<text text-anchor="start" x="592.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="592.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="592.5" y="-636.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
-<text text-anchor="start" x="592.5" y="-625.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
-<text text-anchor="start" x="592.5" y="-614.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
-<text text-anchor="start" x="592.5" y="-603.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
-<text text-anchor="start" x="592.5" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
-<text text-anchor="start" x="592.5" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
-<text text-anchor="start" x="592.5" y="-570.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
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+<text text-anchor="middle" x="697.5" y="-773.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectRef</text>
+<polyline fill="none" stroke="#000000" points="630.5,-766.5 764.5,-766.5 "/>
+<text text-anchor="start" x="638.5" y="-754.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="630.5,-747.5 764.5,-747.5 "/>
+<text text-anchor="start" x="638.5" y="-735.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="638.5" y="-724.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectRef()</text>
+<text text-anchor="start" x="638.5" y="-713.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ same_as()</text>
+<text text-anchor="start" x="638.5" y="-702.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator==()</text>
+<text text-anchor="start" x="638.5" y="-691.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator!=()</text>
+<text text-anchor="start" x="638.5" y="-680.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator<()</text>
+<text text-anchor="start" x="638.5" y="-669.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ defined()</text>
+<text text-anchor="start" x="638.5" y="-658.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="638.5" y="-647.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="638.5" y="-636.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ unique()</text>
+<text text-anchor="start" x="638.5" y="-625.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ use_count()</text>
+<text text-anchor="start" x="638.5" y="-614.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ as()</text>
+<text text-anchor="start" x="638.5" y="-603.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># get_mutable()</text>
+<text text-anchor="start" x="638.5" y="-592.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># DowncastNoCheck()</text>
+<text text-anchor="start" x="638.5" y="-581.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># FFIClearAfterMove()</text>
+<text text-anchor="start" x="638.5" y="-570.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"># GetDataPtr()</text>
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<!-- Node5->Node4 -->
<g id="edge4" class="edge">
<title>Node5->Node4</title>
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+<path fill="none" stroke="#191970" d="M621.1048,-643.8708C566.6835,-618.8763 494.8933,-579.0331 446.5,-526 410.3186,-486.3496 385.7319,-429.9231 371.1439,-388.1392"/>
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<!-- Node7 -->
<g id="node6" class="node">
<title>Node7</title>
-<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::FloatImm \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 24 more...\l}">
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-<text text-anchor="start" x="464" y="-409" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="middle" x="512.5" y="-398" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::FloatImm ></text>
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-<polyline fill="none" stroke="#000000" points="456,-372 569,-372 "/>
-<text text-anchor="start" x="464" y="-360" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-349" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-327" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-305" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="464" y="-272" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="464" y="-261" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="464" y="-250" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
+<g id="a_node6"><a xlink:href="classtvm_1_1runtime_1_1Optional.html" target="_top" xlink:title="{tvm::runtime::Optional\l\< tvm::runtime::Array\l\< tvm::FloatImm \> \>\n|+ _type_is_nullable\l|+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ operator=()\l+ operator=()\land 15 more...\l}">
+<polygon fill="#ffffff" stroke="#000000" points="455.5,-237.5 455.5,-426.5 583.5,-426.5 583.5,-237.5 455.5,-237.5"/>
+<text text-anchor="start" x="463.5" y="-414.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
+<text text-anchor="start" x="463.5" y="-403.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Array</text>
+<text text-anchor="middle" x="519.5" y="-392.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::FloatImm > ></text>
+<polyline fill="none" stroke="#000000" points="455.5,-385.5 583.5,-385.5 "/>
+<text text-anchor="start" x="463.5" y="-373.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="455.5,-366.5 583.5,-366.5 "/>
+<text text-anchor="start" x="463.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="463.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="463.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="463.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
</a>
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<!-- Node5->Node7 -->
<g id="edge7" class="edge">
<title>Node5->Node7</title>
-<path fill="none" stroke="#191970" d="M590.6324,-553.9316C586.3845,-544.5443 582.2902,-535.1578 578.5,-526 564.555,-492.306 551.1963,-454.4929 540.1899,-421.2607"/>
-<polygon fill="none" stroke="#191970" points="587.581,-555.6748 594.9257,-563.3106 593.9458,-552.7612 587.581,-555.6748"/>
+<path fill="none" stroke="#191970" d="M624.4582,-577.4002C613.3678,-560.6904 602.6055,-543.1668 593.5,-526 576.9467,-494.7916 562.2227,-459.0474 550.3532,-426.8134"/>
+<polygon fill="none" stroke="#191970" points="621.7958,-579.7131 630.2807,-586.058 627.6045,-575.8067 621.7958,-579.7131"/>
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<!-- Node8 -->
<g id="node7" class="node">
<title>Node8</title>
-<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1Array.html" target="_top" xlink:title="{tvm::runtime::Array\l\< tvm::meta_schedule\l::ArgInfo \>\n||+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ Array()\l+ operator=()\l+ operator=()\land 24 more...\l}">
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-<text text-anchor="start" x="595.5" y="-414.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Array</text>
-<text text-anchor="start" x="595.5" y="-403.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule</text>
-<text text-anchor="middle" x="651.5" y="-392.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ArgInfo ></text>
-<polyline fill="none" stroke="#000000" points="587.5,-385.5 715.5,-385.5 "/>
-<text text-anchor="middle" x="651.5" y="-373.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
-<polyline fill="none" stroke="#000000" points="587.5,-366.5 715.5,-366.5 "/>
-<text text-anchor="start" x="595.5" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Array()</text>
-<text text-anchor="start" x="595.5" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="595.5" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
-<text text-anchor="start" x="595.5" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 24 more...</text>
+<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1Optional.html" target="_top" xlink:title="{tvm::runtime::Optional\l\< tvm::runtime::Array\l\< tvm::meta_schedule::ArgInfo \> \>\n|+ _type_is_nullable\l|+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ Optional()\l+ operator=()\l+ operator=()\land 15 more...\l}">
+<polygon fill="#ffffff" stroke="#000000" points="602,-237.5 602,-426.5 793,-426.5 793,-237.5 602,-237.5"/>
+<text text-anchor="start" x="610" y="-414.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::Optional</text>
+<text text-anchor="start" x="610" y="-403.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Array</text>
+<text text-anchor="middle" x="697.5" y="-392.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::meta_schedule::ArgInfo > ></text>
+<polyline fill="none" stroke="#000000" points="602,-385.5 793,-385.5 "/>
+<text text-anchor="start" x="610" y="-373.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ _type_is_nullable</text>
+<polyline fill="none" stroke="#000000" points="602,-366.5 793,-366.5 "/>
+<text text-anchor="start" x="610" y="-354.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-343.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-332.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-321.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-310.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-299.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-288.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-277.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="610" y="-266.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="610" y="-255.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="610" y="-244.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
</a>
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</g>
<!-- Node5->Node8 -->
<g id="edge9" class="edge">
<title>Node5->Node8</title>
-<path fill="none" stroke="#191970" d="M651.5,-553.1125C651.5,-511.5481 651.5,-465.6904 651.5,-426.5637"/>
-<polygon fill="none" stroke="#191970" points="648.0001,-553.3 651.5,-563.3001 655.0001,-553.3001 648.0001,-553.3"/>
+<path fill="none" stroke="#191970" d="M697.5,-553.1125C697.5,-511.5481 697.5,-465.6904 697.5,-426.5637"/>
+<polygon fill="none" stroke="#191970" points="694.0001,-553.3 697.5,-563.3001 701.0001,-553.3001 694.0001,-553.3"/>
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<!-- Node9 -->
<g id="node8" class="node">
<title>Node9</title>
-<g id="a_node8"><a xlink:href="classtvm_1_1tir_1_1Trace.html" target="_top" xlink:title="Managed reference to TraceNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="733.5,-276 733.5,-388 875.5,-388 875.5,-276 733.5,-276"/>
-<text text-anchor="middle" x="804.5" y="-376" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::tir::Trace</text>
-<polyline fill="none" stroke="#000000" points="733.5,-369 875.5,-369 "/>
-<text text-anchor="middle" x="804.5" y="-357" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
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-<text text-anchor="start" x="741.5" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Trace()</text>
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+<text text-anchor="start" x="819.5" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="819.5" y="-305" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
+<text text-anchor="start" x="819.5" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Optional()</text>
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+<text text-anchor="start" x="819.5" y="-261" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator=()</text>
+<text text-anchor="start" x="819.5" y="-250" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 15 more...</text>
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<title>Node10</title>
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+<text text-anchor="middle" x="1028.5" y="-376" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::tir::Trace</text>
+<polyline fill="none" stroke="#000000" points="957.5,-369 1099.5,-369 "/>
+<text text-anchor="middle" x="1028.5" y="-357" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="957.5,-350 1099.5,-350 "/>
+<text text-anchor="start" x="965.5" y="-338" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ Trace()</text>
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+<text text-anchor="start" x="965.5" y="-316" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ TVM_DEFINE_MUTABLE</text>
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+<text text-anchor="start" x="965.5" y="-294" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">_REF_METHODS()</text>
+<text text-anchor="start" x="965.5" y="-283" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ApplyJSONToSchedule()</text>
</a>
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<g id="edge13" class="edge">
<title>Node5->Node10</title>
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<g id="node5" class="node">
<title>Node6</title>
<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_top" xlink:title="{tvm::runtime::ObjectPtr\l\< tvm::runtime::Object \>\n||+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ObjectPtr()\l+ ~ObjectPtr()\l+ swap()\l+ get()\l+ operator-\>()\land 11 more...\l}">
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-<text text-anchor="start" x="589.5" y="-999.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectPtr</text>
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-<text text-anchor="start" x="589.5" y="-950.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-939.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-928.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-917.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-906.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-895.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-884.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
-<text text-anchor="start" x="589.5" y="-873.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
-<text text-anchor="start" x="589.5" y="-862.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
-<text text-anchor="start" x="589.5" y="-851.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
-<text text-anchor="start" x="589.5" y="-840.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
+<polygon fill="#ffffff" stroke="#000000" points="627.5,-833.5 627.5,-1011.5 767.5,-1011.5 767.5,-833.5 627.5,-833.5"/>
+<text text-anchor="start" x="635.5" y="-999.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::ObjectPtr</text>
+<text text-anchor="middle" x="697.5" y="-988.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">< tvm::runtime::Object ></text>
+<polyline fill="none" stroke="#000000" points="627.5,-981.5 767.5,-981.5 "/>
+<text text-anchor="middle" x="697.5" y="-969.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> </text>
+<polyline fill="none" stroke="#000000" points="627.5,-962.5 767.5,-962.5 "/>
+<text text-anchor="start" x="635.5" y="-950.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-939.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-928.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-917.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-906.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-895.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-884.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ ~ObjectPtr()</text>
+<text text-anchor="start" x="635.5" y="-873.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ swap()</text>
+<text text-anchor="start" x="635.5" y="-862.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ get()</text>
+<text text-anchor="start" x="635.5" y="-851.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ operator->()</text>
+<text text-anchor="start" x="635.5" y="-840.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">and 11 more...</text>
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<title>Node6->Node5</title>
-<path fill="none" stroke="#404040" d="M651.5,-833.3167C651.5,-821.8765 651.5,-810.0062 651.5,-798.1402"/>
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-<text text-anchor="middle" x="671" y="-807" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
+<path fill="none" stroke="#404040" d="M697.5,-833.3167C697.5,-821.8765 697.5,-810.0062 697.5,-798.1402"/>
+<polygon fill="none" stroke="#404040" points="697.5001,-797.7944 693.5,-791.7944 697.5,-785.7944 701.5,-791.7943 697.5001,-797.7944"/>
+<text text-anchor="middle" x="717" y="-807" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> #data_</text>
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<!-- Node7->Node2 -->
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<title>Node7->Node2</title>
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-<text text-anchor="middle" x="586" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +run_secs</text>
+<path fill="none" stroke="#404040" d="M548.8886,-237.3952C562.7919,-192.639 578.9284,-140.694 590.9036,-102.1446"/>
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+<text text-anchor="middle" x="614" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +run_secs</text>
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<title>Node8->Node2</title>
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+<path fill="none" stroke="#404040" d="M681.9257,-237.3362C673.6327,-196.9505 661.726,-149.8215 645.5,-109 644.5235,-106.5434 643.4628,-104.0685 642.3378,-101.5944"/>
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<title>Node9->Node2</title>
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-<text text-anchor="middle" x="723" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +trace</text>
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<title>Node10->Node2</title>
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-<text text-anchor="middle" x="872" y="-112" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000"> +target</text>
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diff --git a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode__inherit__graph.svg b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode__inherit__graph.svg
index 911dd2459..701f2456f 100644
--- a/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode__inherit__graph.svg
+++ b/docs/reference/api/doxygen/classtvm_1_1meta__schedule_1_1TuningRecordNode__inherit__graph.svg
@@ -17,8 +17,8 @@
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<text text-anchor="start" x="8" y="-103.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ trace</text>
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+<text text-anchor="start" x="8" y="-92.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ workload</text>
+<text text-anchor="start" x="8" y="-81.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ run_secs</text>
<text text-anchor="start" x="8" y="-70.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">+ target</text>
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-<a href="database_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * or more co [...]
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<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_a93d2f3debadefcc713bbc045127ca8f7"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#a93d2f3debadefcc713bbc045127ca8f7">tvm::meta_schedule::WorkloadNode::TVM_DECLARE_FINAL_OBJECT_INFO</a></div><div class="ttdeci">TVM_DECLARE_FINAL_OBJECT_INFO(WorkloadNode, runtime::Object)</div></div>
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+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a7d7d50ae9b6c43e064b38db91bc8123d"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a7d7d50ae9b6c43e064b38db91bc8123d">tvm::meta_schedule::PyDatabaseNode::f_commit_tuning_record</a></div><div class="ttdeci">FCommitTuningRecord f_commit_tuning_record</div><div class="ttdoc">The packed function to the CommitTuningRecord function. </div><div class="ttdef"><b>Definition:</b> database.h:238</d [...]
<div class="ttc" id="trace_8h_html"><div class="ttname"><a href="trace_8h.html">trace.h</a></div></div>
<div class="ttc" id="object_8h_html_a98fa3013ab23958a9f05200330e35805"><div class="ttname"><a href="object_8h.html#a98fa3013ab23958a9f05200330e35805">TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS</a></div><div class="ttdeci">#define TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS(TypeName, ParentType, ObjectName)</div><div class="ttdef"><b>Definition:</b> object.h:758</div></div>
<div class="ttc" id="structtvm_1_1meta__schedule_1_1WorkloadHash_html"><div class="ttname"><a href="structtvm_1_1meta__schedule_1_1WorkloadHash.html">tvm::meta_schedule::WorkloadHash</a></div><div class="ttdoc">The hash method for Workload. </div><div class="ttdef"><b>Definition:</b> database.h:90</div></div>
<div class="ttc" id="string_8h_html"><div class="ttname"><a href="string_8h.html">string.h</a></div><div class="ttdoc">Runtime String container types. </div></div>
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<div class="ttc" id="ir_2expr_8h_html"><div class="ttname"><a href="ir_2expr_8h.html">expr.h</a></div><div class="ttdoc">Base expr nodes in TVM. </div></div>
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<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_aad19a5cb8e2c623e355ca5e2a98c4c18"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#aad19a5cb8e2c623e355ca5e2a98c4c18">tvm::meta_schedule::WorkloadNode::THashCode</a></div><div class="ttdeci">size_t THashCode</div><div class="ttdoc">The type of structural hash. </div><div class="ttdef"><b>Definition:</b> database.h:40</div></div>
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-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a52fb1116090619e95fb6b28352308eed"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a52fb1116090619e95fb6b28352308eed">tvm::meta_schedule::PyDatabaseNode::f_get_top_k</a></div><div class="ttdeci">FGetTopK f_get_top_k</div><div class="ttdoc">The packed function to the GetTopK function. </div><div class="ttdef"><b>Definition:</b> database.h:239</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1DatabaseNode_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">tvm::meta_schedule::DatabaseNode</a></div><div class="ttdef"><b>Definition:</b> database.h:162</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a52fb1116090619e95fb6b28352308eed"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a52fb1116090619e95fb6b28352308eed">tvm::meta_schedule::PyDatabaseNode::f_get_top_k</a></div><div class="ttdeci">FGetTopK f_get_top_k</div><div class="ttdoc">The packed function to the GetTopK function. </div><div class="ttdef"><b>Definition:</b> database.h:240</div></div>
<div class="ttc" id="structtvm_1_1meta__schedule_1_1WorkloadHash_html_a7cb09ddc6c76d9d00ddbeab8502d97cb"><div class="ttname"><a href="structtvm_1_1meta__schedule_1_1WorkloadHash.html#a7cb09ddc6c76d9d00ddbeab8502d97cb">tvm::meta_schedule::WorkloadHash::operator()</a></div><div class="ttdeci">size_t operator()(const Workload &a) const</div><div class="ttdef"><b>Definition:</b> database.h:91</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1Object_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Object.html">tvm::runtime::Object</a></div><div class="ttdoc">base class of all object containers. </div><div class="ttdef"><b>Definition:</b> object.h:167</div></div>
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+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html">tvm::meta_schedule::PyDatabaseNode</a></div><div class="ttdoc">The database with customized methods on the python-side. </div><div class="ttdef"><b>Definition:</b> database.h:201</div></div>
<div class="ttc" id="classtvm_1_1StructuralEqual_html"><div class="ttname"><a href="classtvm_1_1StructuralEqual.html">tvm::StructuralEqual</a></div><div class="ttdoc">Content-aware structural equality comparator for objects. </div><div class="ttdef"><b>Definition:</b> structural_equal.h:81</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuningRecordNode_html_a8cc2d64f796593a1a774eef259f17b29"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a8cc2d64f796593a1a774eef259f17b29">tvm::meta_schedule::TuningRecordNode::trace</a></div><div class="ttdeci">tir::Trace trace</div><div class="ttdoc">The trace tuned. </div><div class="ttdef"><b>Definition:</b> database.h:105</div></div>
<div class="ttc" id="arg__info_8h_html"><div class="ttname"><a href="arg__info_8h.html">arg_info.h</a></div></div>
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+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_ac7ae1a05fe5c7858f5860133a82bc7b7"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#ac7ae1a05fe5c7858f5860133a82bc7b7">tvm::meta_schedule::PyDatabaseNode::f_size</a></div><div class="ttdeci">FSize f_size</div><div class="ttdoc">The packed function to the Size function. </div><div class="ttdef"><b>Definition:</b> database.h:242</div></div>
<div class="ttc" id="classtvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class to get the attributes of an AST/IR node. The content is going to be called for each fie...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
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<div class="ttc" id="structtvm_1_1meta__schedule_1_1WorkloadEqual_html_aa29256c62e52bef8c9a0f7881d3fc601"><div class="ttname"><a href="structtvm_1_1meta__schedule_1_1WorkloadEqual.html#aa29256c62e52bef8c9a0f7881d3fc601">tvm::meta_schedule::WorkloadEqual::operator()</a></div><div class="ttdeci">bool operator()(const Workload &a, const Workload &b) const</div><div class="ttdef"><b>Definition:</b> database.h:96</div></div>
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<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_a3929f2761c168c25de6be2247b913911"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#a3929f2761c168c25de6be2247b913911">tvm::meta_schedule::WorkloadNode::mod</a></div><div class="ttdeci">IRModule mod</div><div class="ttdoc">The workload&#39;s IRModule. </div><div class="ttdef"><b>Definition:</b> database.h:42</div></div>
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<div class="ttc" id="classtvm_1_1runtime_1_1String_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1String.html">tvm::runtime::String</a></div><div class="ttdoc">Reference to string objects. </div><div class="ttdef"><b>Definition:</b> string.h:129</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1TypedPackedFunc_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1TypedPackedFunc.html">tvm::runtime::TypedPackedFunc< bool(const IRModule &)></a></div></div>
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<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_ab533ae06bb310ffbd8acb954e253b7db"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#ab533ae06bb310ffbd8acb954e253b7db">tvm::meta_schedule::WorkloadNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> database.h:46</div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuningRecordNode_html_ab9cbbf8eb7941995e9c7552948eac02b"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#ab9cbbf8eb7941995e9c7552948eac02b">tvm::meta_schedule::TuningRecordNode::target</a></div><div class="ttdeci">Target target</div><div class="ttdoc">The target for tuning. </div><div class="ttdef"><b>Definition:</b> database.h:111</div></div>
-<div class="ttc" id="classtvm_1_1Target_html"><div class="ttname"><a href="classtvm_1_1Target.html">tvm::Target</a></div><div class="ttdoc">Managed reference class to TargetNode. </div><div class="ttdef"><b>Definition:</b> target.h:141</div></div>
<div class="ttc" id="classtvm_1_1runtime_1_1ObjectRef_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1ObjectRef.html">tvm::runtime::ObjectRef</a></div><div class="ttdoc">Base class of all object reference. </div><div class="ttdef"><b>Definition:</b> object.h:511</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuningRecordNode_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html">tvm::meta_schedule::TuningRecordNode</a></div><div class="ttdoc">The class of tuning records. </div><div class="ttdef"><b>Definition:</b> database.h:102</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_ad6ac6e9052f30846d72489ced86a7690"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#ad6ac6e9052f30846d72489ced86a7690">tvm::meta_schedule::WorkloadNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> database.h:51</div></div>
<div class="ttc" id="object_8h_html"><div class="ttname"><a href="object_8h.html">object.h</a></div><div class="ttdoc">A managed object in the TVM runtime. </div></div>
<div class="ttc" id="classtvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode. </div><div class="ttdef"><b>Definition:</b> module.h:360</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_aa0ee452b287813b9509e649397846a3c"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#aa0ee452b287813b9509e649397846a3c">tvm::meta_schedule::WorkloadNode::AsJSON</a></div><div class="ttdeci">ObjectRef AsJSON() const</div><div class="ttdoc">Export the workload to a JSON string. </div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_add146bf1e2006f72ed1534b2004bcb06"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#add146bf1e2006f72ed1534b2004bcb06">tvm::meta_schedule::PyDatabaseNode::f_has_workload</a></div><div class="ttdeci">FHasWorkload f_has_workload</div><div class="ttdoc">The packed function to the HasWorkload function. </div><div class="ttdef"><b>Definition:</b> database.h:233</div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a00614b22bd1cae147dc0d83cdd071187"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a00614b22bd1cae147dc0d83cdd071187">tvm::meta_schedule::PyDatabaseNode::CommitTuningRecord</a></div><div class="ttdeci">void CommitTuningRecord(const TuningRecord &record) final</div><div class="ttdoc">Add a tuning record to the database. </div><div class="ttdef"><b>Definition:</b> database.h:264</div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_aa0bd64983cde2fc6ef1547448352981c"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#aa0bd64983cde2fc6ef1547448352981c">tvm::meta_schedule::PyDatabaseNode::f_commit_workload</a></div><div class="ttdeci">FCommitWorkload f_commit_workload</div><div class="ttdoc">The packed function to the CommitWorkload function. </div><div class="ttdef"><b>Definition:</b> database.h:235</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_add146bf1e2006f72ed1534b2004bcb06"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#add146bf1e2006f72ed1534b2004bcb06">tvm::meta_schedule::PyDatabaseNode::f_has_workload</a></div><div class="ttdeci">FHasWorkload f_has_workload</div><div class="ttdoc">The packed function to the HasWorkload function. </div><div class="ttdef"><b>Definition:</b> database.h:234</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a00614b22bd1cae147dc0d83cdd071187"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a00614b22bd1cae147dc0d83cdd071187">tvm::meta_schedule::PyDatabaseNode::CommitTuningRecord</a></div><div class="ttdeci">void CommitTuningRecord(const TuningRecord &record) final</div><div class="ttdoc">Add a tuning record to the database. </div><div class="ttdef"><b>Definition:</b> database.h:265</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_aa0bd64983cde2fc6ef1547448352981c"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#aa0bd64983cde2fc6ef1547448352981c">tvm::meta_schedule::PyDatabaseNode::f_commit_workload</a></div><div class="ttdeci">FCommitWorkload f_commit_workload</div><div class="ttdoc">The packed function to the CommitWorkload function. </div><div class="ttdef"><b>Definition:</b> database.h:236</div></div>
<div class="ttc" id="target_8h_html"><div class="ttname"><a href="target_8h.html">target.h</a></div><div class="ttdoc">Compilation target object. </div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a12d64aa44d3d2ab37a6600d17be3e708"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a12d64aa44d3d2ab37a6600d17be3e708">tvm::meta_schedule::PyDatabaseNode::CommitWorkload</a></div><div class="ttdeci">Workload CommitWorkload(const IRModule &mod) final</div><div class="ttdoc">Look up or add workload to the database if missing. </div><div class="ttdef"><b>Definition:</b> database.h:259</d [...]
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a12d64aa44d3d2ab37a6600d17be3e708"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a12d64aa44d3d2ab37a6600d17be3e708">tvm::meta_schedule::PyDatabaseNode::CommitWorkload</a></div><div class="ttdeci">Workload CommitWorkload(const IRModule &mod) final</div><div class="ttdoc">Look up or add workload to the database if missing. </div><div class="ttdef"><b>Definition:</b> database.h:260</d [...]
<div class="ttc" id="classtvm_1_1tir_1_1Trace_html"><div class="ttname"><a href="classtvm_1_1tir_1_1Trace.html">tvm::tir::Trace</a></div><div class="ttdoc">Managed reference to TraceNode. </div><div class="ttdef"><b>Definition:</b> trace.h:141</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1WorkloadNode_html_a96812f1418c68d5aeb11331fd76aab1c"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1WorkloadNode.html#a96812f1418c68d5aeb11331fd76aab1c">tvm::meta_schedule::WorkloadNode::shash</a></div><div class="ttdeci">THashCode shash</div><div class="ttdoc">The workload&#39;s structural hash. </div><div class="ttdef"><b>Definition:</b> database.h:44</div></div>
+<div class="ttc" id="classtvm_1_1runtime_1_1Optional_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Optional.html">tvm::runtime::Optional</a></div><div class="ttdoc">Optional container that to represent to a Nullable variant of T. </div><div class="ttdef"><b>Definition:</b> optional.h:51</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1Workload_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1Workload.html">tvm::meta_schedule::Workload</a></div><div class="ttdoc">Managed reference to WorkloadNode. </div><div class="ttdef"><b>Definition:</b> database.h:65</div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_adebe3a6bfb55e5ce0807b97f12a6c39e"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#adebe3a6bfb55e5ce0807b97f12a6c39e">tvm::meta_schedule::PyDatabaseNode::GetTopK</a></div><div class="ttdeci">Array< TuningRecord > GetTopK(const Workload &workload, int top_k) final</div><div class="ttdoc">Get the top K tuning records of given workload from the database. </div><div class="ttdef">< [...]
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_adebe3a6bfb55e5ce0807b97f12a6c39e"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#adebe3a6bfb55e5ce0807b97f12a6c39e">tvm::meta_schedule::PyDatabaseNode::GetTopK</a></div><div class="ttdeci">Array< TuningRecord > GetTopK(const Workload &workload, int top_k) final</div><div class="ttdoc">Get the top K tuning records of given workload from the database. </div><div class="ttdef">< [...]
<div class="ttc" id="reflection_8h_html"><div class="ttname"><a href="reflection_8h.html">reflection.h</a></div><div class="ttdoc">Reflection and serialization of compiler IR/AST nodes. </div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a1a24b977e3d25a7ffab704e12457fb66"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a1a24b977e3d25a7ffab704e12457fb66">tvm::meta_schedule::PyDatabaseNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> database.h:243</div></div>
+<div class="ttc" id="classtvm_1_1meta__schedule_1_1PyDatabaseNode_html_a1a24b977e3d25a7ffab704e12457fb66"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1PyDatabaseNode.html#a1a24b977e3d25a7ffab704e12457fb66">tvm::meta_schedule::PyDatabaseNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> database.h:244</div></div>
<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuningRecord_html"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuningRecord.html">tvm::meta_schedule::TuningRecord</a></div><div class="ttdoc">The managed reference of TuningRecordNode. </div><div class="ttdef"><b>Definition:</b> database.h:138</div></div>
-<div class="ttc" id="classtvm_1_1meta__schedule_1_1TuningRecordNode_html_a5ee669c272da1447f3f666d9faf5e449"><div class="ttname"><a href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a5ee669c272da1447f3f666d9faf5e449">tvm::meta_schedule::TuningRecordNode::run_secs</a></div><div class="ttdeci">Array< FloatImm > run_secs</div><div class="ttdoc">The profiling result in seconds. </div><div class="ttdef"><b>Definition:</b> database.h:107</div></div>
<div class="ttc" id="object_8h_html_ac2b7418e9549512b5db0126cf2a716f1"><div class="ttname"><a href="object_8h.html#ac2b7418e9549512b5db0126cf2a716f1">TVM_DECLARE_BASE_OBJECT_INFO</a></div><div class="ttdeci">#define TVM_DECLARE_BASE_OBJECT_INFO(TypeName, ParentType)</div><div class="ttdoc">helper macro to declare a base object type that can be inherited. </div><div class="ttdef"><b>Definition:</b> object.h:648</div></div>
<div class="ttc" id="object_8h_html_a782d0de62fbf75736e29c1e79c22c7f1"><div class="ttname"><a href="object_8h.html#a782d0de62fbf75736e29c1e79c22c7f1">TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS</a></div><div class="ttdeci">#define TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS(TypeName, ParentType, ObjectName)</div><div class="ttdef"><b>Definition:</b> object.h:728</div></div>
<div class="ttc" id="packed__func_8h_html"><div class="ttname"><a href="packed__func_8h.html">packed_func.h</a></div><div class="ttdoc">Type-erased function used across TVM API. </div></div>
diff --git a/docs/reference/api/doxygen/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index f56b8c699..ea6e57558 100644
--- a/docs/reference/api/doxygen/functions_a.html
+++ b/docs/reference/api/doxygen/functions_a.html
@@ -355,7 +355,7 @@ $(function() {
<li>args_info
: <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html#a6891e92cac8712bb690401ed121ae7e8">tvm::meta_schedule::MeasureCandidateNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html#a85b26e2093d125aa38d8d658c4fe154b">tvm::meta_schedule::RunnerInputNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#abd204834fbe398290cdd419e26851b6a">tvm::meta_schedule::TuningRecordNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a09f1921d607d7ef4397876c15d2f0b08">tvm::meta_schedule::TuningRecordNode</a>
</li>
<li>arguments
: <a class="el" href="classtvm_1_1OpNode.html#af753e9ea21f09017bb61d5722b2ce181">tvm::OpNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_t.html b/docs/reference/api/doxygen/functions_func_t.html
index 9e517e95a..af4bd3ccc 100644
--- a/docs/reference/api/doxygen/functions_func_t.html
+++ b/docs/reference/api/doxygen/functions_func_t.html
@@ -198,7 +198,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html#ac869d3a3e888c77677b62d064fec0f6e">tvm::auto_scheduler::TuningOptions</a>
</li>
<li>TuningRecord()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#a8495d5cbf2d11eaca3a5c1f6a25e5ea7">tvm::meta_schedule::TuningRecord</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#aa4699af50f91bda306e6c199766c4757">tvm::meta_schedule::TuningRecord</a>
</li>
<li>Tuple()
: <a class="el" href="classtvm_1_1relay_1_1Tuple.html#a284e236318986fd385a02aa68bd3e938">tvm::relay::Tuple</a>
diff --git a/docs/reference/api/doxygen/functions_func_v.html b/docs/reference/api/doxygen/functions_func_v.html
index 43d073fda..5af06eef9 100644
--- a/docs/reference/api/doxygen/functions_func_v.html
+++ b/docs/reference/api/doxygen/functions_func_v.html
@@ -426,7 +426,7 @@ $(function() {
<li>VisitType_()
: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#a9f728a703d68743e8b305acb68e5eb87">tvm::TypeFunctor< R(const Type &n, Args...)></a>
, <a class="el" href="classtvm_1_1TypeMutator.html#a89bd7a76f5a736defc7f3b0dda664761">tvm::TypeMutator</a>
-, <a class="el" href="classtvm_1_1TypeVisitor.html#a292b19b578526ea74b1434dc50514a18">tvm::TypeVisitor</a>
+, <a class="el" href="classtvm_1_1TypeVisitor.html#a11378b4db6f704c04a97bec1c8ea8261">tvm::TypeVisitor</a>
</li>
<li>VisitTypeDefault_()
: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#a91553f9e04c39b3821a70ae4f7b0c597">tvm::TypeFunctor< R(const Type &n, Args...)></a>
diff --git a/docs/reference/api/doxygen/functions_r.html b/docs/reference/api/doxygen/functions_r.html
index 856ad158e..bdc644b0c 100644
--- a/docs/reference/api/doxygen/functions_r.html
+++ b/docs/reference/api/doxygen/functions_r.html
@@ -569,7 +569,7 @@ $(function() {
</li>
<li>run_secs
: <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html#a23d5a15c8d72c17c59ec4e7f2d3fabf3">tvm::meta_schedule::RunnerResultNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a5ee669c272da1447f3f666d9faf5e449">tvm::meta_schedule::TuningRecordNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a00aeeedbe4d1d6008224d98c62739581">tvm::meta_schedule::TuningRecordNode</a>
</li>
<li>RunAfterPass()
: <a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html#a49332b9ab696afefa427b676dbaba1b0">tvm::instrument::PassInstrumentNode</a>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index c33f92b59..8e2b4b14e 100644
--- a/docs/reference/api/doxygen/functions_t.html
+++ b/docs/reference/api/doxygen/functions_t.html
@@ -74,7 +74,7 @@ $(function() {
, <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html#afc001f3e427cfc8c05236b615cfd2868">tvm::meta_schedule::BuilderInputNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html#acd4fdc1e71a2368bc0c8cf213b04a4bd">tvm::meta_schedule::ExtractedTaskNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#ae2a4edfbc9e6246748fd0e10202cdb66">tvm::meta_schedule::TuneContextNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#ab9cbbf8eb7941995e9c7552948eac02b">tvm::meta_schedule::TuningRecordNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a45a380cfa2edfd63056fb1a00f9aac35">tvm::meta_schedule::TuningRecordNode</a>
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a46879dbe84105fb621a6167f8d73b223">tvm::runtime::vm::Instruction</a>
</li>
<li>Target()
@@ -364,7 +364,7 @@ $(function() {
: <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html#ac869d3a3e888c77677b62d064fec0f6e">tvm::auto_scheduler::TuningOptions</a>
</li>
<li>TuningRecord()
-: <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#a8495d5cbf2d11eaca3a5c1f6a25e5ea7">tvm::meta_schedule::TuningRecord</a>
+: <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html#aa4699af50f91bda306e6c199766c4757">tvm::meta_schedule::TuningRecord</a>
</li>
<li>Tuple()
: <a class="el" href="classtvm_1_1relay_1_1Tuple.html#a284e236318986fd385a02aa68bd3e938">tvm::relay::Tuple</a>
diff --git a/docs/reference/api/doxygen/functions_v.html b/docs/reference/api/doxygen/functions_v.html
index 0590d5e1f..2d1c39247 100644
--- a/docs/reference/api/doxygen/functions_v.html
+++ b/docs/reference/api/doxygen/functions_v.html
@@ -154,7 +154,7 @@ $(function() {
, <a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html#acfa1269806fbf19e7badd424c19c64bf">tvm::relay::PatternVarNode</a>
</li>
<li>Var()
-: <a class="el" href="classtvm_1_1relay_1_1Var.html#a06ef8ae1d07a5b8a3c25ca7775d17762">tvm::relay::Var</a>
+: <a class="el" href="classtvm_1_1relay_1_1Var.html#a45372a62057ee9332a391e29845505ff">tvm::relay::Var</a>
</li>
<li>var
: <a class="el" href="classtvm_1_1tir_1_1IterVarNode.html#a09036ef2df09e7caf21e66dcb62675a6">tvm::tir::IterVarNode</a>
@@ -551,7 +551,7 @@ $(function() {
</li>
<li>VisitStmt_()
: <a class="el" href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8_01args_08_4.html#a5943b89b1ee4b775e84d71eb43055f48">tvm::tir::StmtFunctor< R(const Stmt &n, Args... args)></a>
-, <a class="el" href="classtvm_1_1tir_1_1StmtMutator.html#a3b116212aaf79bc898f3446a35f7fd3e">tvm::tir::StmtMutator</a>
+, <a class="el" href="classtvm_1_1tir_1_1StmtMutator.html#a7fefa4227a37b988b91141f746181394">tvm::tir::StmtMutator</a>
, <a class="el" href="classtvm_1_1tir_1_1StmtVisitor.html#afcb1a0ec03b7a7da4304c5b790b27210">tvm::tir::StmtVisitor</a>
</li>
<li>VisitStmtDefault_()
@@ -566,9 +566,9 @@ $(function() {
, <a class="el" href="classtvm_1_1TypeMutator.html#a84e824911927d98e20a338eab8b75a45">tvm::TypeMutator</a>
</li>
<li>VisitType_()
-: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#ae4061c0c04da5b6c889842506c2d5091">tvm::TypeFunctor< R(const Type &n, Args...)></a>
-, <a class="el" href="classtvm_1_1TypeMutator.html#ad4ad7209f8789568e5e57870f0b758f0">tvm::TypeMutator</a>
-, <a class="el" href="classtvm_1_1TypeVisitor.html#ac8845fbf58c1a1f0ebc23c7ee403aaab">tvm::TypeVisitor</a>
+: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#a05485baebc1e25710714f65b68124f73">tvm::TypeFunctor< R(const Type &n, Args...)></a>
+, <a class="el" href="classtvm_1_1TypeMutator.html#a8171dc89a947d6224e83e86ce5d06d11">tvm::TypeMutator</a>
+, <a class="el" href="classtvm_1_1TypeVisitor.html#ae699be9a6ed94a635c315506e0c2a6d2">tvm::TypeVisitor</a>
</li>
<li>VisitTypeDefault_()
: <a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html#a91553f9e04c39b3821a70ae4f7b0c597">tvm::TypeFunctor< R(const Type &n, Args...)></a>
diff --git a/docs/reference/api/doxygen/functions_vars_a.html b/docs/reference/api/doxygen/functions_vars_a.html
index e7aeccee0..10c37cdce 100644
--- a/docs/reference/api/doxygen/functions_vars_a.html
+++ b/docs/reference/api/doxygen/functions_vars_a.html
@@ -165,7 +165,7 @@ $(function() {
<li>args_info
: <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html#a6891e92cac8712bb690401ed121ae7e8">tvm::meta_schedule::MeasureCandidateNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html#a85b26e2093d125aa38d8d658c4fe154b">tvm::meta_schedule::RunnerInputNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#abd204834fbe398290cdd419e26851b6a">tvm::meta_schedule::TuningRecordNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a09f1921d607d7ef4397876c15d2f0b08">tvm::meta_schedule::TuningRecordNode</a>
</li>
<li>arguments
: <a class="el" href="classtvm_1_1OpNode.html#af753e9ea21f09017bb61d5722b2ce181">tvm::OpNode</a>
diff --git a/docs/reference/api/doxygen/functions_vars_r.html b/docs/reference/api/doxygen/functions_vars_r.html
index 4dcd191dc..0bd810faf 100644
--- a/docs/reference/api/doxygen/functions_vars_r.html
+++ b/docs/reference/api/doxygen/functions_vars_r.html
@@ -263,7 +263,7 @@ $(function() {
</li>
<li>run_secs
: <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html#a23d5a15c8d72c17c59ec4e7f2d3fabf3">tvm::meta_schedule::RunnerResultNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a5ee669c272da1447f3f666d9faf5e449">tvm::meta_schedule::TuningRecordNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a00aeeedbe4d1d6008224d98c62739581">tvm::meta_schedule::TuningRecordNode</a>
</li>
<li>runner
: <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html#a0f7a4a84637fd0309b52f61b65bee158">tvm::auto_scheduler::ProgramMeasurerNode</a>
diff --git a/docs/reference/api/doxygen/functions_vars_t.html b/docs/reference/api/doxygen/functions_vars_t.html
index 9bc1762c4..7cc653083 100644
--- a/docs/reference/api/doxygen/functions_vars_t.html
+++ b/docs/reference/api/doxygen/functions_vars_t.html
@@ -73,7 +73,7 @@ $(function() {
, <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html#afc001f3e427cfc8c05236b615cfd2868">tvm::meta_schedule::BuilderInputNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html#acd4fdc1e71a2368bc0c8cf213b04a4bd">tvm::meta_schedule::ExtractedTaskNode</a>
, <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html#ae2a4edfbc9e6246748fd0e10202cdb66">tvm::meta_schedule::TuneContextNode</a>
-, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#ab9cbbf8eb7941995e9c7552948eac02b">tvm::meta_schedule::TuningRecordNode</a>
+, <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html#a45a380cfa2edfd63056fb1a00f9aac35">tvm::meta_schedule::TuningRecordNode</a>
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1Instruction.html#a46879dbe84105fb621a6167f8d73b223">tvm::runtime::vm::Instruction</a>
, <a class="el" href="classtvm_1_1VirtualDeviceNode.html#a8b2d427d9e21886ccaeaae5e9cc55aaf">tvm::VirtualDeviceNode</a>
</li>
diff --git a/docs/reference/api/doxygen/hierarchy.html b/docs/reference/api/doxygen/hierarchy.html
index 96e5dc8e8..70caffbb6 100644
--- a/docs/reference/api/doxygen/hierarchy.html
+++ b/docs/reference/api/doxygen/hierarchy.html
@@ -902,317 +902,318 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_107_81_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::auto_scheduler::MeasureCallback > ></a></td><td class="desc"></td></tr>
<tr id="row_107_82_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::FloatImm > ></a></td><td class="desc"></td></tr>
<tr id="row_107_83_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::Integer > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::BuilderResult > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::MeasureCandidate > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::RunnerFuture > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Map< tvm::runtime::String, tvm::runtime::NDArray > ></a></td><td class="desc"></td></tr>
-<tr id="row_107_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::NDArray ></a></td><td class="desc"></td></tr>
-<tr id="row_107_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
-<tr id="row_107_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::String ></a></td><td class="desc"></td></tr>
-<tr id="row_107_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::Target ></a></td><td class="desc"></td></tr>
-<tr id="row_107_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::IterVar ></a></td><td class="desc"></td></tr>
-<tr id="row_107_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
-<tr id="row_107_94_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_94_" class="arrow" onclick="toggleFolder('107_94_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AffineType.html" target="_self">tvm::AffineType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1AffineTypeNode.html" title="AffineType representation. ">Affi [...]
-<tr id="row_107_94_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineType.html" target="_self">tvm::TensorAffineType</a></td><td class="desc">Managed reference to AffineTypes </td></tr>
-<tr id="row_107_94_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineType.html" target="_self">tvm::TupleAffineType</a></td><td class="desc">Managed reference to TupleAffineTypes </td></tr>
-<tr id="row_107_95_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html" target="_self">tvm::arith::ConstIntBound</a></td><td class="desc">Reference class to <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html" title="Constant integer up and lower bound(inclusive). Useful for value bound analysis. ">Con [...]
-<tr id="row_107_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html" target="_self">tvm::arith::IntConstraints</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html" title="Represent integer constrains including (integer) variables, their ranges and the [...]
-<tr id="row_107_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html" target="_self">tvm::arith::IntConstraintsTransform</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html" title="We can have different set of variables to represent th [...]
-<tr id="row_107_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html" target="_self">tvm::arith::IntGroupBounds</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusiv [...]
-<tr id="row_107_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSet.html" target="_self">tvm::arith::IntSet</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" title="Base class of all Integer set containers. represent a set of integers in one dimension. ">IntSetNode</a> [...]
-<tr id="row_107_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMapResult.html" target="_self">tvm::arith::IterMapResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapResultNode.html" title="Result of DetectIterMap. ">IterMapResultNode</a> </td></tr>
-<tr id="row_107_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMark.html" target="_self">tvm::arith::IterMark</a></td><td class="desc">Managed reference to IterMarkExprNode </td></tr>
-<tr id="row_107_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSet.html" target="_self">tvm::arith::ModularSet</a></td><td class="desc">Reference of <a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" title="Range of a linear integer function. Use to do specify the possible index values. ">ModularSetNode</a [...]
-<tr id="row_107_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrFieldInfo.html" target="_self">tvm::AttrFieldInfo</a></td><td class="desc"><a class="el" href="classtvm_1_1AttrFieldInfo.html" title="AttrFieldInfo. ">AttrFieldInfo</a> </td></tr>
-<tr id="row_107_104_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_104_" class="arrow" onclick="toggleFolder('107_104_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Attrs.html" target="_self">tvm::Attrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseAttrsNode.html" title="Base class of all attribute class. ">Base [...]
-<tr id="row_107_104_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">tvm::DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode< [...]
-<tr id="row_107_105_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html" target="_self">tvm::auto_scheduler::AccessAnalyzer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html" title="Static analyzer for a ComputeDAG. ">AccessAnalyzerN [...]
-<tr id="row_107_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html" target="_self">tvm::auto_scheduler::AttachMap</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html" title="stores the compute_at relation between stages This stores a bi-dir [...]
-<tr id="row_107_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html" target="_self">tvm::auto_scheduler::BuildResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html" title="Store the result of a build. ">BuildResultNode</a> </td></tr>
-<tr id="row_107_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html" target="_self">tvm::auto_scheduler::ComputeDAG</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html" title="The auto-scheduler's computational graph and related progra [...]
-<tr id="row_107_109_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_109_" class="arrow" onclick="toggleFolder('107_109_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html" target="_self">tvm::auto_scheduler::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CostMo [...]
-<tr id="row_107_109_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModel.html" target="_self">tvm::auto_scheduler::PythonBasedModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" title="A wrapper for cost model defined by python [...]
-<tr id="row_107_109_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html" target="_self">tvm::auto_scheduler::RandomModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" title="The cost model returning random value for all predictions. [...]
-<tr id="row_107_110_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html" target="_self">tvm::auto_scheduler::HardwareParams</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html" title="The parameters of target hardware used to guide the [...]
-<tr id="row_107_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator.html" target="_self">tvm::auto_scheduler::Iterator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html" title="An iterator of a for-loop Similar to tvm::IterVar in include/tvm/tir/ [...]
-<tr id="row_107_112_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_112_" class="arrow" onclick="toggleFolder('107_112_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html" target="_self">tvm::auto_scheduler::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__schedul [...]
-<tr id="row_107_112_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html" target="_self">tvm::auto_scheduler::PythonBasedMeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallbackNode.html" title="A wrapper for [...]
-<tr id="row_107_112_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html" target="_self">tvm::auto_scheduler::RecordToFile</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" title="Callback for logging the input and results of measureme [...]
-<tr id="row_107_113_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html" target="_self">tvm::auto_scheduler::MeasureInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html" title="Store the input of a measurement. ">MeasureInputNode</a> [...]
-<tr id="row_107_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResult.html" target="_self">tvm::auto_scheduler::MeasureResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResultNode.html" title="Store the results of a measurement. ">MeasureResultNod [...]
-<tr id="row_107_115_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_115_" class="arrow" onclick="toggleFolder('107_115_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html" target="_self">tvm::auto_scheduler::ProgramBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
-<tr id="row_107_115_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilder.html" target="_self">tvm::auto_scheduler::LocalBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilderNode.html" title="LocalBuilder use local CPU cores to build programs in p [...]
-<tr id="row_107_116_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurer.html" target="_self">tvm::auto_scheduler::ProgramMeasurer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html" title="Measurer that measures the time costs of tvm pro [...]
-<tr id="row_107_117_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_117_" class="arrow" onclick="toggleFolder('107_117_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html" target="_self">tvm::auto_scheduler::ProgramRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1 [...]
-<tr id="row_107_117_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunner.html" target="_self">tvm::auto_scheduler::LocalRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html" title="LocalRunner that uses local CPU/GPU to measure the time co [...]
-<tr id="row_107_117_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" target="_self">tvm::auto_scheduler::RPCRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" title="RPCRunner that uses RPC call to measures the time cost of progra [...]
-<tr id="row_107_118_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html" target="_self">tvm::auto_scheduler::RecordReader</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" title="Log reader to load step logs from a file. ">RecordReaderN [...]
-<tr id="row_107_119_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_119_" class="arrow" onclick="toggleFolder('107_119_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallback.html" target="_self">tvm::auto_scheduler::SearchCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
-<tr id="row_107_119_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStates.html" target="_self">tvm::auto_scheduler::PreloadMeasuredStates</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStatesNode.html" title="Preload measured states from [...]
-<tr id="row_107_120_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" target="_self">tvm::auto_scheduler::SearchPolicy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" title="The base class of search policies. ">SearchPolicyNode</a> [...]
-<tr id="row_107_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTask.html" target="_self">tvm::auto_scheduler::SearchTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" title="The computation information and hardware parameters for a speci [...]
-<tr id="row_107_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html" target="_self">tvm::auto_scheduler::Stage</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" title="A op stage in the compute declaration. Similar to te::Stage in include/tvm/te/ [...]
-<tr id="row_107_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1State.html" target="_self">tvm::auto_scheduler::State</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" title="A state in the search process. It consists of the current loop structure and a [...]
-<tr id="row_107_124_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_124_" class="arrow" onclick="toggleFolder('107_124_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Step.html" target="_self">tvm::auto_scheduler::Step</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" t [...]
-<tr id="row_107_124_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStep.html" target="_self">tvm::auto_scheduler::AnnotationStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html" title="Annotation step that corresponds to vectorize, pa [...]
-<tr id="row_107_124_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html" target="_self">tvm::auto_scheduler::CacheReadStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html" title="Cache read step that corresponds to te::Schedule::ca [...]
-<tr id="row_107_124_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html" target="_self">tvm::auto_scheduler::CacheWriteStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html" title="Cache write step that corresponds to te::Schedule [...]
-<tr id="row_107_124_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html" target="_self">tvm::auto_scheduler::ComputeAtStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html" title="Compute at step that corresponds to te::Stage::compu [...]
-<tr id="row_107_124_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html" target="_self">tvm::auto_scheduler::ComputeInlineStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html" title="Compute inline step that corresponds to [...]
-<tr id="row_107_124_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html" target="_self">tvm::auto_scheduler::ComputeRootStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html" title="Compute root step that corresponds to te::Stag [...]
-<tr id="row_107_124_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html" target="_self">tvm::auto_scheduler::FollowFusedSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html" title="Similar to FollowSplitStep, but [...]
-<tr id="row_107_124_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html" target="_self">tvm::auto_scheduler::FollowSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html" title="Similar to SplitStepNode, but uses split facto [...]
-<tr id="row_107_124_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html" target="_self">tvm::auto_scheduler::FuseStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html" title="Fuse step that corresponds to te::Stage::fuse. ">FuseStepNode</a> < [...]
-<tr id="row_107_124_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.html" target="_self">tvm::auto_scheduler::PragmaStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStepNode.html" title="Pragma step that corresponds to te::Stage::pragma. ">PragmaSt [...]
-<tr id="row_107_124_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStep.html" target="_self">tvm::auto_scheduler::ReorderStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" title="Reorder step that corresponds to te::Stage::reorder. ">Re [...]
-<tr id="row_107_124_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html" target="_self">tvm::auto_scheduler::RfactorStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" title="Reduction factor step that corresponds to te::Schedule::r [...]
-<tr id="row_107_124_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html" target="_self">tvm::auto_scheduler::SplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" title="Split step that corresponds to te::Stage::split with additional [...]
-<tr id="row_107_124_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html" target="_self">tvm::auto_scheduler::StorageAlignStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" title="Storage align step that corresponds to te: [...]
-<tr id="row_107_125_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html" target="_self">tvm::auto_scheduler::TuningOptions</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" title="Tuning and measurement options. ">TuningOptionsNode</a [...]
-<tr id="row_107_126_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_126_" class="arrow" onclick="toggleFolder('107_126_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseExpr.html" target="_self">tvm::BaseExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseExprNode.html" title="Base type of all the expressions. "> [...]
-<tr id="row_107_126_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_126_0_" class="arrow" onclick="toggleFolder('107_126_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExpr.html" target="_self">tvm::PrimExpr</a></td><td class="desc">Reference to <a class="el" href="classtvm_1_1PrimExprNode.html" title="Base node of all primitive expressions [...]
-<tr id="row_107_126_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_0_0_" class="arrow" onclick="toggleFolder('107_126_0_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html" target="_self">tvm::arith::IterMapExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.htm [...]
-<tr id="row_107_126_0_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html" target="_self">tvm::arith::IterSplitExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html" title="Split of an iterator. ">IterSplitExprNode</a> </td></tr>
-<tr id="row_107_126_0_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html" target="_self">tvm::arith::IterSumExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSumExprNode.html" title="Fuse multiple iterators by summing them with scaling. ">IterSumExprNode</a> </td></tr>
-<tr id="row_107_126_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">tvm::FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
-<tr id="row_107_126_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_0_2_" class="arrow" onclick="toggleFolder('107_126_0_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">tvm::IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literal [...]
-<tr id="row_107_126_0_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">tvm::Bool</a></td><td class="desc">Boolean constant </td></tr>
-<tr id="row_107_126_0_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">tvm::Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
-<tr id="row_107_126_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">tvm::tir::Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b ">AddNode</a> </td></tr>
-<tr id="row_107_126_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">tvm::tir::And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a && b ">AndNode</a> </td></tr>
-<tr id="row_107_126_0_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">tvm::tir::Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape. ">AnyNode</a> </td></tr>
-<tr id="row_107_126_0_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">tvm::tir::Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value. ">BroadcastNode</a> </td></tr>
-<tr id="row_107_126_0_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">tvm::tir::BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer. ">BufferLoadNode</a> </td></tr>
-<tr id="row_107_126_0_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">tvm::tir::Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node. ">CallNode</a> </td></tr>
-<tr id="row_107_126_0_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">tvm::tir::Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another. ">CastNode</a> </td></tr>
-<tr id="row_107_126_0_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">tvm::tir::Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics. ">DivNode</a> </td></tr>
-<tr id="row_107_126_0_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">tvm::tir::EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b ">EQNode</a> </td></tr>
-<tr id="row_107_126_0_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">tvm::tir::FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b) ">FloorDivNode</a> </td></tr>
-<tr id="row_107_126_0_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">tvm::tir::FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv. ">FloorModNode</a> </td></tr>
-<tr id="row_107_126_0_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">tvm::tir::GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a >= b ">GENode</a> </td></tr>
-<tr id="row_107_126_0_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">tvm::tir::GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a > b ">GTNode</a> </td></tr>
-<tr id="row_107_126_0_16_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">tvm::tir::LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a <= b ">LENode</a> </td></tr>
-<tr id="row_107_126_0_17_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">tvm::tir::Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body. ">LetNode</a> </td></tr>
-<tr id="row_107_126_0_18_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Load.html" target="_self">tvm::tir::Load</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoadNode.html" title="Load the value from buffer_var. ">LoadNode</a> </td></tr>
-<tr id="row_107_126_0_19_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">tvm::tir::LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a < b ">LTNode</a> </td></tr>
-<tr id="row_107_126_0_20_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">tvm::tir::Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b) ">MaxNode</a> </td></tr>
-<tr id="row_107_126_0_21_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">tvm::tir::Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b) ">MinNode</a> </td></tr>
-<tr id="row_107_126_0_22_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">tvm::tir::Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics. ">ModNode</a> </td></tr>
-<tr id="row_107_126_0_23_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">tvm::tir::Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b ">MulNode</a> </td></tr>
-<tr id="row_107_126_0_24_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">tvm::tir::NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b ">NENode</a> </td></tr>
-<tr id="row_107_126_0_25_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">tvm::tir::Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a ">NotNode</a> </td></tr>
-<tr id="row_107_126_0_26_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">tvm::tir::Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b ">OrNode</a> </td></tr>
-<tr id="row_107_126_0_27_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">tvm::tir::ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer. ">ProducerLoadNode</a> </td></tr>
-<tr id="row_107_126_0_28_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">tvm::tir::Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to const [...]
-<tr id="row_107_126_0_29_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">tvm::tir::Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator. ">ReduceNode</a> </td></tr>
-<tr id="row_107_126_0_30_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">tvm::tir::Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value. ">SelectNode</a> </td></tr>
-<tr id="row_107_126_0_31_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">tvm::tir::Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...">ShuffleNode [...]
-<tr id="row_107_126_0_32_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">tvm::tir::StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts. ">StringImmNode</a> </td></tr>
-<tr id="row_107_126_0_33_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">tvm::tir::Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b ">SubNode</a> </td></tr>
-<tr id="row_107_126_0_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_0_34_" class="arrow" onclick="toggleFolder('107_126_0_34_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Var.html" target="_self">tvm::tir::Var</a></td><td class="desc">Named variable in TIR </td></tr>
-<tr id="row_107_126_0_34_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVar.html" target="_self">tvm::tir::SizeVar</a></td><td class="desc">Named variable represents a tensor index size </td></tr>
-<tr id="row_107_126_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_126_1_" class="arrow" onclick="toggleFolder('107_126_1_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExpr.html" target="_self">tvm::RelayExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayExprNode.html" title="Base node of all non-primit [...]
-<tr id="row_107_126_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_126_1_0_" class="arrow" onclick="toggleFolder('107_126_1_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseFunc.html" target="_self">tvm::BaseFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseFuncNode.html" title="Base node of all functio [...]
-<tr id="row_107_126_1_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">tvm::relay::Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
-<tr id="row_107_126_1_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">tvm::tir::PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements. ">PrimFuncNode</a> </td></tr>
-<tr id="row_107_126_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">tvm::Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
-<tr id="row_107_126_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">tvm::GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
-<tr id="row_107_126_1_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">tvm::Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
-<tr id="row_107_126_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">tvm::relay::Call</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">tvm::relay::Constant</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">tvm::relay::If</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">tvm::relay::Let</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">tvm::relay::Match</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">tvm::relay::RefCreate</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">tvm::relay::RefRead</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">tvm::relay::RefWrite</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">tvm::relay::TempExpr</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">tvm::relay::Tuple</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">tvm::relay::TupleGetItem</a></td><td class="desc"></td></tr>
-<tr id="row_107_126_1_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">tvm::relay::Var</a></td><td class="desc"></td></tr>
-<tr id="row_107_127_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">tvm::CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> </td></tr>
-<tr id="row_107_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Diagnostic.html" target="_self">tvm::Diagnostic</a></td><td class="desc"></td></tr>
-<tr id="row_107_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContext.html" target="_self">tvm::DiagnosticContext</a></td><td class="desc"></td></tr>
-<tr id="row_107_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRenderer.html" target="_self">tvm::DiagnosticRenderer</a></td><td class="desc"></td></tr>
-<tr id="row_107_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">tvm::EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM's global environment. ">EnvFuncNode</a> </td></tr>
-<tr id="row_107_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">tvm::GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_107_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html" target="_self">tvm::instrument::PassInstrument</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" title="PassInstrumentNode forms an instrument implementation. It [...]
-<tr id="row_107_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">tvm::IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
-<tr id="row_107_135_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">tvm::MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
-<tr id="row_107_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBest.html" target="_self">tvm::meta_schedule::ApplyHistoryBest</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html" title="An integration context that allows application o [...]
-<tr id="row_107_137_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_137_" class="arrow" onclick="toggleFolder('107_137_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html" target="_self">tvm::meta_schedule::ArgInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.h [...]
-<tr id="row_107_137_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfo.html" target="_self">tvm::meta_schedule::TensorInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" title="The tensor argument information. ">TensorInfoNode</a> </td></tr>
-<tr id="row_107_138_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html" target="_self">tvm::meta_schedule::Builder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" title="The abstract builder interface. ">BuilderNode</a> </td></tr>
-<tr id="row_107_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html" target="_self">tvm::meta_schedule::BuilderInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" title="The builder's input, containing an IRModule and the targ [...]
-<tr id="row_107_140_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html" target="_self">tvm::meta_schedule::BuilderResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" title="The builder's output, containing the artifact path or [...]
-<tr id="row_107_141_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html" target="_self">tvm::meta_schedule::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html" title="Cost model. ">CostModelNode</a> </td></tr>
-<tr id="row_107_142_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html" target="_self">tvm::meta_schedule::Database</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">DatabaseNode</a> </td></tr>
-<tr id="row_107_143_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_self">tvm::meta_schedule::ExtractedTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html" title="A tuning task extracted from the high-level IR. ">Extract [...]
-<tr id="row_107_144_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html" target="_self">tvm::meta_schedule::FeatureExtractor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html" title="Extractor for features from measure candidates f [...]
-<tr id="row_107_145_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html" target="_self">tvm::meta_schedule::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html" title="Rules to apply after measure results is available. [...]
-<tr id="row_107_146_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_self">tvm::meta_schedule::MeasureCandidate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" title="The schedule (with input shapes) to be measured. [...]
-<tr id="row_107_147_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html" target="_self">tvm::meta_schedule::Mutator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MutatorNode.html" title="Mutator is designed to mutate the trace to explore the design space. ">Muta [...]
-<tr id="row_107_148_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Postproc.html" target="_self">tvm::meta_schedule::Postproc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1PostprocNode.html" title="Rules to apply a postprocessor to a schedule. ">PostprocNode</a> </td></tr>
-<tr id="row_107_149_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" target="_self">tvm::meta_schedule::Runner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" title="The abstract runner interface. ">RunnerNode</a> </td></tr>
-<tr id="row_107_150_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_self">tvm::meta_schedule::RunnerFuture</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" title="A class to asynchronously fetch runner's output. ">Runne [...]
-<tr id="row_107_151_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_self">tvm::meta_schedule::RunnerInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" title="Runner's input containing path of artifact, type of device [...]
-<tr id="row_107_152_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_self">tvm::meta_schedule::RunnerResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" title="Runner's output containing measurement result of Measure [...]
-<tr id="row_107_153_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRule.html" target="_self">tvm::meta_schedule::ScheduleRule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRuleNode.html" title="Rules to modify a block in a schedule. ">ScheduleRuleNode</a [...]
-<tr id="row_107_154_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_self">tvm::meta_schedule::SearchStrategy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" title="The search strategy for measure candidates generation. [...]
-<tr id="row_107_155_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_self">tvm::meta_schedule::SpaceGenerator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" title="The abstract class for design space generation. ">Spac [...]
-<tr id="row_107_156_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_self">tvm::meta_schedule::TaskScheduler</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedule [...]
-<tr id="row_107_157_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_self">tvm::meta_schedule::TuneContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" title="The auto tuning context. ">TuneContextNode</a> </td></tr>
-<tr id="row_107_158_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_self">tvm::meta_schedule::TuningRecord</a></td><td class="desc">The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </td></tr>
-<tr id="row_107_159_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" target="_self">tvm::meta_schedule::Workload</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" title="A workload, i.e. an IRModule and its structural hash. ">WorkloadNode</a> [...]
-<tr id="row_107_160_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1Source.html" target="_self">tvm::parser::Source</a></td><td class="desc"></td></tr>
-<tr id="row_107_161_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1SourceMap.html" target="_self">tvm::parser::SourceMap</a></td><td class="desc"></td></tr>
-<tr id="row_107_162_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PoolInfo.html" target="_self">tvm::PoolInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_163_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Range.html" target="_self">tvm::Range</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> constainer </td></tr>
-<tr id="row_107_164_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">tvm::relay::Clause</a></td><td class="desc"></td></tr>
-<tr id="row_107_165_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">tvm::relay::ConstructorValue</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_166_" class="arrow" onclick="toggleFolder('107_166_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">tvm::relay::DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
-<tr id="row_107_166_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AltPattern.html" target="_self">tvm::relay::AltPattern</a></td><td class="desc">A pattern which matches either of two patterns </td></tr>
-<tr id="row_107_166_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html" target="_self">tvm::relay::AttrPattern</a></td><td class="desc">A pattern which matches attributes in another pattern </td></tr>
-<tr id="row_107_166_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">tvm::relay::CallPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">tvm::relay::ConstantPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">tvm::relay::DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_107_166_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">tvm::relay::DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
-<tr id="row_107_166_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">tvm::relay::ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
-<tr id="row_107_166_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html" target="_self">tvm::relay::FunctionPattern</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
-<tr id="row_107_166_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfPattern.html" target="_self">tvm::relay::IfPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetPattern.html" target="_self">tvm::relay::LetPattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> binding that binds a local var </td></tr>
-<tr id="row_107_166_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">tvm::relay::ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_107_166_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">tvm::relay::TupleGetItemPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">tvm::relay::TuplePattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">tvm::relay::TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_107_166_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">tvm::relay::VarPattern</a></td><td class="desc"></td></tr>
-<tr id="row_107_166_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">tvm::relay::WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
-<tr id="row_107_167_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">tvm::relay::DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
-<tr id="row_107_168_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Executor.html" target="_self">tvm::relay::Executor</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html" title="Executor information. ">ExecutorNode</a> </td></tr>
-<tr id="row_107_169_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">tvm::relay::Id</a></td><td class="desc"></td></tr>
-<tr id="row_107_170_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">tvm::relay::OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
-<tr id="row_107_171_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">tvm::relay::OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
-<tr id="row_107_172_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">tvm::relay::OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
-<tr id="row_107_173_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_173_" class="arrow" onclick="toggleFolder('107_173_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">tvm::relay::Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT mat [...]
-<tr id="row_107_173_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">tvm::relay::PatternConstructor</a></td><td class="desc"></td></tr>
-<tr id="row_107_173_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">tvm::relay::PatternTuple</a></td><td class="desc"></td></tr>
-<tr id="row_107_173_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">tvm::relay::PatternVar</a></td><td class="desc"></td></tr>
-<tr id="row_107_173_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">tvm::relay::PatternWildcard</a></td><td class="desc"></td></tr>
-<tr id="row_107_174_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">tvm::relay::RecClosure</a></td><td class="desc"></td></tr>
-<tr id="row_107_175_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">tvm::relay::RefValue</a></td><td class="desc"></td></tr>
-<tr id="row_107_176_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Runtime.html" target="_self">tvm::relay::Runtime</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1RuntimeNode.html" title="Runtime information. ">RuntimeNode</a> </td></tr>
-<tr id="row_107_177_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">tvm::runtime::ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
-<tr id="row_107_178_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< T, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs. ">Array</a>, container representing a [...]
-<tr id="row_107_179_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_179_" class="arrow" onclick="toggleFolder('107_179_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Closure.html" target="_self">tvm::runtime::Closure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_107_179_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">tvm::relay::InterpreterClosure</a></td><td class="desc"></td></tr>
-<tr id="row_107_179_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosure.html" target="_self">tvm::runtime::vm::VMClosure</a></td><td class="desc">Reference to closure </td></tr>
-<tr id="row_107_180_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< K, V, typename, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Map.html" title="Map container of NodeRef->NodeRef in DSL graph. Map implements copy on write semantics, which m [...]
-<tr id="row_107_181_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_181_" class="arrow" onclick="toggleFolder('107_181_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html" target="_self">tvm::runtime::metadata::MetadataBase</a></td><td class="desc">Reference class for the common <a class="el" href="classtvm_1_1ru [...]
-<tr id="row_107_181_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html" target="_self">tvm::runtime::metadata::Metadata</a></td><td class="desc"></td></tr>
-<tr id="row_107_181_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" target="_self">tvm::runtime::metadata::MetadataArray</a></td><td class="desc">Reference class for <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" title="Reference class for MetadataArray. ">MetadataA [...]
-<tr id="row_107_181_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1TensorInfo.html" target="_self">tvm::runtime::metadata::TensorInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_182_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Module.html" target="_self">tvm::runtime::Module</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM. ">Module</a> container of TVM </td></tr>
-<tr id="row_107_183_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray.html" target="_self">tvm::runtime::NDArray</a></td><td class="desc">Managed <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" title="Managed NDArray. The array is backed by reference counted blocks. ">NDArray</a>. The array is backed by referenc [...]
-<tr id="row_107_184_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< T ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Optional.html" title="Optional container that to represent to a Nullable variant of T. ">Optional</a> container that to represen [...]
-<tr id="row_107_185_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" target="_self">tvm::runtime::PackedFunc</a></td><td class="desc">Packed function is a type-erased function. The arguments are passed by packed format </td></tr>
-<tr id="row_107_186_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper.html" target="_self">tvm::runtime::profiling::DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </td></tr>
-<tr id="row_107_187_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html" target="_self">tvm::runtime::profiling::MetricCollector</a></td><td class="desc">Wrapper for <code><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" title="Interface for user defined profiling [...]
-<tr id="row_107_188_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Report.html" target="_self">tvm::runtime::profiling::Report</a></td><td class="desc"></td></tr>
-<tr id="row_107_189_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html" target="_self">tvm::runtime::ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </td></tr>
-<tr id="row_107_190_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1String.html" target="_self">tvm::runtime::String</a></td><td class="desc">Reference to string objects </td></tr>
-<tr id="row_107_191_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Timer.html" target="_self">tvm::runtime::Timer</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Timer.html" title="Timer for a specific device. ">Timer</a> for a specific device </td></tr>
-<tr id="row_107_192_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Storage.html" target="_self">tvm::runtime::vm::Storage</a></td><td class="desc">Reference to storage </td></tr>
-<tr id="row_107_193_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">tvm::SourceName</a></td><td class="desc">The source name of a file span </td></tr>
-<tr id="row_107_194_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">tvm::Span</a></td><td class="desc"></td></tr>
-<tr id="row_107_195_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">tvm::Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
-<tr id="row_107_196_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">tvm::TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target. ">TargetKindNode</a> </td></tr>
-<tr id="row_107_197_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">tvm::TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag. ">TargetTagNode</a> </td></tr>
-<tr id="row_107_198_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html" target="_self">tvm::te::IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
-<tr id="row_107_199_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_199_" class="arrow" onclick="toggleFolder('107_199_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html" target="_self">tvm::te::IterVarRelation</a></td><td class="desc">The schedule relation between IterVars can be <a class="el" href="classtvm_1_1te_1_1Split.h [...]
-<tr id="row_107_199_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Fuse.html" target="_self">tvm::te::Fuse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1FuseNode.html" title="Fuse two domains into one domain. ">FuseNode</a> </td></tr>
-<tr id="row_107_199_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Rebase.html" target="_self">tvm::te::Rebase</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1RebaseNode.html" title="Rebase the iteration to make min to be 0. This is useful to normalize the Schedule to make every leaf...">Rebas [...]
-<tr id="row_107_199_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Singleton.html" target="_self">tvm::te::Singleton</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SingletonNode.html" title="Singleton iterator [0, 1) ">SingletonNode</a> </td></tr>
-<tr id="row_107_199_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Split.html" target="_self">tvm::te::Split</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SplitNode.html" title="Split the parent domain into product of outer and iter. ">SplitNode</a> </td></tr>
-<tr id="row_107_199_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Transform.html" target="_self">tvm::te::Transform</a></td><td class="desc"></td></tr>
-<tr id="row_107_200_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_200_" class="arrow" onclick="toggleFolder('107_200_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Operation.html" target="_self">tvm::te::Operation</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operati [...]
-<tr id="row_107_200_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOp.html" target="_self">tvm::te::ComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" title="A Compute op that compute a tensor on certain domain. ">ComputeOpNode</a> </td></tr>
-<tr id="row_107_200_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOp.html" target="_self">tvm::te::ExternOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" title="External computation that cannot be splitted. ">ExternOpNode</a> </td></tr>
-<tr id="row_107_200_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOp.html" target="_self">tvm::te::HybridOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" title="A computation operator that generated by hybrid script. ">HybridOpNode</a> </td></tr>
-<tr id="row_107_200_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html" target="_self">tvm::te::PlaceholderOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html" title="A placeholder op represents an input placeholder. ">PlaceholderOpNode</a> </td></tr>
-<tr id="row_107_200_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOp.html" target="_self">tvm::te::ScanOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" title="Symbolic scan. ">ScanOpNode</a> </td></tr>
-<tr id="row_107_200_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOp.html" target="_self">tvm::te::TensorComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" title="A TenorCompute op that compute a tensor with an tensor intrinsic. ">TensorComputeOpN [...]
-<tr id="row_107_201_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Schedule.html" target="_self">tvm::te::Schedule</a></td><td class="desc">Global schedule container For operations and all the operations they depend on. The schedule per <a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. " [...]
-<tr id="row_107_202_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html" target="_self">tvm::te::SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </td></tr>
-<tr id="row_107_203_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Stage.html" target="_self">tvm::te::Stage</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">Stage</a>, contains scheduling for a stage of computation </td></tr>
-<tr id="row_107_204_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html" target="_self">tvm::te::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" title="Node to represent a Tensor intrinsic operator. ">TensorIntrinNode</a> </td></tr>
-<tr id="row_107_205_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html" target="_self">tvm::te::TensorIntrinCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html">TensorIntrinCallNode</a> </td></tr>
-<tr id="row_107_206_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">tvm::tir::BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode. ">Layout</a>, < [...]
-<tr id="row_107_207_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRV.html" target="_self">tvm::tir::BlockRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html" title="A random variable that evaluates to a TensorIR block. ">BlockRVNode</a> </td></tr>
-<tr id="row_107_208_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScope.html" target="_self">tvm::tir::BlockScope</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" title="An object with 1-to-1 correspondence with each block reference in the sref tree. This data structu [...]
-<tr id="row_107_209_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">tvm::tir::Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">Buffer</a> is a symbolic n-darray structur [...]
-<tr id="row_107_210_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html" target="_self">tvm::tir::BufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html" title="Representing the region of multi-dimensional buffer access. ">BufferRegionNode</a> </td></tr>
-<tr id="row_107_211_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">tvm::tir::CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element..." [...]
-<tr id="row_107_212_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_212_" class="arrow" onclick="toggleFolder('107_212_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">tvm::tir::DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base [...]
-<tr id="row_107_212_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Tensor.html" target="_self">tvm::te::Tensor</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> structure representing a possible input [...]
-<tr id="row_107_213_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Dependency.html" target="_self">tvm::tir::Dependency</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" title="A tuple (src, dst, kind) representing certain types of dependency. For example, (A, B, kRAW) means [...]
-<tr id="row_107_214_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMap.html" target="_self">tvm::tir::IndexMap</a></td><td class="desc"></td></tr>
-<tr id="row_107_215_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Instruction.html" target="_self">tvm::tir::Instruction</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionNode.html" title="Schedule instructions each corresponds to a schedule primitive. ">InstructionNode</a> </td></tr>
-<tr id="row_107_216_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html" target="_self">tvm::tir::InstructionKind</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html" title="Kind of an instruction, e.g. Split, Reorder, etc. Besides the name, every kind of i [...]
-<tr id="row_107_217_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVar.html" target="_self">tvm::tir::IterVar</a></td><td class="desc">Iteration Variable, represents an iteration over an integer interval </td></tr>
-<tr id="row_107_218_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">tvm::tir::Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">Layo [...]
-<tr id="row_107_219_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LoopRV.html" target="_self">tvm::tir::LoopRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoopRVNode.html" title="A random variable that evaluates to a TensorIR for loop. ">LoopRVNode</a> </td></tr>
-<tr id="row_107_220_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MatchBufferRegion.html" target="_self">tvm::tir::MatchBufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MatchBufferRegionNode.html" title="Match introduces a constraint that the source buffer region can be remapped t [...]
-<tr id="row_107_221_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Schedule.html" target="_self">tvm::tir::Schedule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html" title="The user-facing schedule class. ">ScheduleNode</a> </td></tr>
-<tr id="row_107_222_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ScheduleState.html" target="_self">tvm::tir::ScheduleState</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html" title="The state of scheduling, which exposes a Replace method as the primary interface for all [...]
-<tr id="row_107_223_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_223_" class="arrow" onclick="toggleFolder('107_223_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Stmt.html" target="_self">tvm::tir::Stmt</a></td><td class="desc">Container of all statements </td></tr>
-<tr id="row_107_223_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Allocate.html" target="_self">tvm::tir::Allocate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" title="Allocate a buffer that can be used in body. ">AllocateNode</a> </td></tr>
-<tr id="row_107_223_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AllocateConst.html" target="_self">tvm::tir::AllocateConst</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html" title="Allocate a buffer that can be used in body. ">AllocateConstNode</a> </td></tr>
-<tr id="row_107_223_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html" target="_self">tvm::tir::AssertStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" title="Assert condition, if an error occurs, return the error message. ">AssertStmtNode</a> </td></tr>
-<tr id="row_107_223_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html" target="_self">tvm::tir::AttrStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" title="Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary inform. [...]
-<tr id="row_107_223_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Block.html" target="_self">tvm::tir::Block</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockNode.html" title="A block is a basic schedule unit in TIR. ">BlockNode</a> </td></tr>
-<tr id="row_107_223_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html" target="_self">tvm::tir::BlockRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html" title="A block realization node represents execution of the block at the binding values. ...">Blo [...]
-<tr id="row_107_223_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html" target="_self">tvm::tir::BufferRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" title="Annotate the region where the buffer need to be read and write in the body. We only nee [...]
-<tr id="row_107_223_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStore.html" target="_self">tvm::tir::BufferStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" title="Store value to the high dimension buffer. ">BufferStoreNode</a> </td></tr>
-<tr id="row_107_223_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Evaluate.html" target="_self">tvm::tir::Evaluate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" title="Evaluates an expression. This is mostly used for putting a Call node into Stmt. ">EvaluateNode</a> </td></tr>
-<tr id="row_107_223_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1For.html" target="_self">tvm::tir::For</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ForNode.html" title="A for loop, with poissible type annotations. ">ForNode</a> </td></tr>
-<tr id="row_107_223_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html" target="_self">tvm::tir::IfThenElse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" title="IfThenElse statment. ">IfThenElseNode</a> </td></tr>
-<tr id="row_107_223_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmt.html" target="_self">tvm::tir::LetStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" title="Let binding, bind var to value, then run body. ">LetStmtNode</a> </td></tr>
-<tr id="row_107_223_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Prefetch.html" target="_self">tvm::tir::Prefetch</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" title="A prefetch hint for a buffer. ">PrefetchNode</a> </td></tr>
-<tr id="row_107_223_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerRealize.html" target="_self">tvm::tir::ProducerRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html" title="Annotate the bounds where the data produced by the producer need to be written a [...]
-<tr id="row_107_223_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerStore.html" target="_self">tvm::tir::ProducerStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerStoreNode.html" title="Store value into mult-dimensional array that will be read by the consumer of the produ [...]
-<tr id="row_107_223_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html" target="_self">tvm::tir::SeqStmt</a></td><td class="desc">Sequence statement </td></tr>
-<tr id="row_107_223_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Store.html" target="_self">tvm::tir::Store</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StoreNode.html" title="Store value to the buffer. ">StoreNode</a> </td></tr>
-<tr id="row_107_223_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1While.html" target="_self">tvm::tir::While</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1WhileNode.html" title="A While loop. ">WhileNode</a> </td></tr>
-<tr id="row_107_224_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRef.html" target="_self">tvm::tir::StmtSRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" title="An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref". ">Stmt [...]
-<tr id="row_107_225_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrin.html" target="_self">tvm::tir::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" title="Tensor intrinsics for tensorization. ">TensorIntrinNode</a> </td></tr>
-<tr id="row_107_226_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Trace.html" target="_self">tvm::tir::Trace</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TraceNode.html" title="An execution trace of a scheduling program. ">TraceNode</a> </td></tr>
-<tr id="row_107_227_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfo.html" target="_self">tvm::tir::usmp::AllocatedPoolInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_228_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfo.html" target="_self">tvm::tir::usmp::BufferInfo</a></td><td class="desc"></td></tr>
-<tr id="row_107_229_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysis.html" target="_self">tvm::tir::usmp::BufferInfoAnalysis</a></td><td class="desc"></td></tr>
-<tr id="row_107_230_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1PoolAllocation.html" target="_self">tvm::tir::usmp::PoolAllocation</a></td><td class="desc"></td></tr>
-<tr id="row_107_231_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_231_" class="arrow" onclick="toggleFolder('107_231_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Pass.html" target="_self">tvm::transform::Pass</a></td><td class="desc"></td></tr>
-<tr id="row_107_231_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Sequential.html" target="_self">tvm::transform::Sequential</a></td><td class="desc"></td></tr>
-<tr id="row_107_232_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassContext.html" target="_self">tvm::transform::PassContext</a></td><td class="desc"><a class="el" href="classtvm_1_1transform_1_1PassContext.html" title="PassContext that is used to configure the pass behavior. ">PassContext</a> that is used to configure t [...]
-<tr id="row_107_233_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassInfo.html" target="_self">tvm::transform::PassInfo</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html" title="Meta data that will be used to help optimization and analysis. ">PassInfoNode</ [...]
-<tr id="row_107_234_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_234_" class="arrow" onclick="toggleFolder('107_234_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">tvm::Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</ [...]
-<tr id="row_107_234_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorType.html" target="_self">tvm::BaseTensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseTensorTypeNode.html" title="Base of all Tensor types This container can hold TensorType or GenericTensorType. ...">BaseTensorTypeNo [...]
-<tr id="row_107_234_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">tvm::FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
-<tr id="row_107_234_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">tvm::GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
-<tr id="row_107_234_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">tvm::IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNod [...]
-<tr id="row_107_234_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">tvm::PointerType</a></td><td class="desc"></td></tr>
-<tr id="row_107_234_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">tvm::PrimType</a></td><td class="desc"></td></tr>
-<tr id="row_107_234_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">tvm::RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR. ">RelayRefTypeNode</a> </td></tr>
-<tr id="row_107_234_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">tvm::TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> < [...]
-<tr id="row_107_234_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">tvm::TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
-<tr id="row_107_234_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">tvm::TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
-<tr id="row_107_234_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_234_10_" class="arrow" onclick="toggleFolder('107_234_10_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">tvm::TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential [...]
-<tr id="row_107_234_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">tvm::TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
-<tr id="row_107_234_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">tvm::TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
-<tr id="row_107_234_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">tvm::TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
-<tr id="row_107_235_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::TypedEnvFunc< R(Args...)></a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNo [...]
-<tr id="row_107_236_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">tvm::TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
-<tr id="row_107_237_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDevice.html" target="_self">tvm::VirtualDevice</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1VirtualDeviceNode.html" title="Describes at compile time the constraints on where data is to be stored at runtime down to the ( [...]
-<tr id="row_107_238_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1WorkspaceMemoryPools.html" target="_self">tvm::WorkspaceMemoryPools</a></td><td class="desc"></td></tr>
+<tr id="row_107_84_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::ArgInfo > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_85_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::BuilderResult > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_86_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::MeasureCandidate > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_87_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Array< tvm::meta_schedule::RunnerFuture > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_88_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::Map< tvm::runtime::String, tvm::runtime::NDArray > ></a></td><td class="desc"></td></tr>
+<tr id="row_107_89_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::NDArray ></a></td><td class="desc"></td></tr>
+<tr id="row_107_90_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::ObjectRef ></a></td><td class="desc"></td></tr>
+<tr id="row_107_91_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::runtime::String ></a></td><td class="desc"></td></tr>
+<tr id="row_107_92_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::Target ></a></td><td class="desc"></td></tr>
+<tr id="row_107_93_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::IterVar ></a></td><td class="desc"></td></tr>
+<tr id="row_107_94_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< tvm::tir::Stmt ></a></td><td class="desc"></td></tr>
+<tr id="row_107_95_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_95_" class="arrow" onclick="toggleFolder('107_95_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AffineType.html" target="_self">tvm::AffineType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1AffineTypeNode.html" title="AffineType representation. ">Affi [...]
+<tr id="row_107_95_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorAffineType.html" target="_self">tvm::TensorAffineType</a></td><td class="desc">Managed reference to AffineTypes </td></tr>
+<tr id="row_107_95_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleAffineType.html" target="_self">tvm::TupleAffineType</a></td><td class="desc">Managed reference to TupleAffineTypes </td></tr>
+<tr id="row_107_96_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ConstIntBound.html" target="_self">tvm::arith::ConstIntBound</a></td><td class="desc">Reference class to <a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html" title="Constant integer up and lower bound(inclusive). Useful for value bound analysis. ">Con [...]
+<tr id="row_107_97_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html" target="_self">tvm::arith::IntConstraints</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsNode.html" title="Represent integer constrains including (integer) variables, their ranges and the [...]
+<tr id="row_107_98_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html" target="_self">tvm::arith::IntConstraintsTransform</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html" title="We can have different set of variables to represent th [...]
+<tr id="row_107_99_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntGroupBounds.html" target="_self">tvm::arith::IntGroupBounds</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntGroupBoundsNode.html" title="Represent integer grouped bounds which are classified into lower bounds (inclusiv [...]
+<tr id="row_107_100_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IntSet.html" target="_self">tvm::arith::IntSet</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IntSetNode.html" title="Base class of all Integer set containers. represent a set of integers in one dimension. ">IntSetNode</a> [...]
+<tr id="row_107_101_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMapResult.html" target="_self">tvm::arith::IterMapResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapResultNode.html" title="Result of DetectIterMap. ">IterMapResultNode</a> </td></tr>
+<tr id="row_107_102_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMark.html" target="_self">tvm::arith::IterMark</a></td><td class="desc">Managed reference to IterMarkExprNode </td></tr>
+<tr id="row_107_103_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1ModularSet.html" target="_self">tvm::arith::ModularSet</a></td><td class="desc">Reference of <a class="el" href="classtvm_1_1arith_1_1ModularSetNode.html" title="Range of a linear integer function. Use to do specify the possible index values. ">ModularSetNode</a [...]
+<tr id="row_107_104_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1AttrFieldInfo.html" target="_self">tvm::AttrFieldInfo</a></td><td class="desc"><a class="el" href="classtvm_1_1AttrFieldInfo.html" title="AttrFieldInfo. ">AttrFieldInfo</a> </td></tr>
+<tr id="row_107_105_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_105_" class="arrow" onclick="toggleFolder('107_105_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Attrs.html" target="_self">tvm::Attrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseAttrsNode.html" title="Base class of all attribute class. ">Base [...]
+<tr id="row_107_105_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">tvm::DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode< [...]
+<tr id="row_107_106_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzer.html" target="_self">tvm::auto_scheduler::AccessAnalyzer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AccessAnalyzerNode.html" title="Static analyzer for a ComputeDAG. ">AccessAnalyzerN [...]
+<tr id="row_107_107_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMap.html" target="_self">tvm::auto_scheduler::AttachMap</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AttachMapNode.html" title="stores the compute_at relation between stages This stores a bi-dir [...]
+<tr id="row_107_108_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResult.html" target="_self">tvm::auto_scheduler::BuildResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1BuildResultNode.html" title="Store the result of a build. ">BuildResultNode</a> </td></tr>
+<tr id="row_107_109_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAG.html" target="_self">tvm::auto_scheduler::ComputeDAG</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeDAGNode.html" title="The auto-scheduler's computational graph and related progra [...]
+<tr id="row_107_110_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_110_" class="arrow" onclick="toggleFolder('107_110_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CostModel.html" target="_self">tvm::auto_scheduler::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CostMo [...]
+<tr id="row_107_110_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModel.html" target="_self">tvm::auto_scheduler::PythonBasedModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedModelNode.html" title="A wrapper for cost model defined by python [...]
+<tr id="row_107_110_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModel.html" target="_self">tvm::auto_scheduler::RandomModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RandomModelNode.html" title="The cost model returning random value for all predictions. [...]
+<tr id="row_107_111_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParams.html" target="_self">tvm::auto_scheduler::HardwareParams</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1HardwareParamsNode.html" title="The parameters of target hardware used to guide the [...]
+<tr id="row_107_112_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Iterator.html" target="_self">tvm::auto_scheduler::Iterator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1IteratorNode.html" title="An iterator of a for-loop Similar to tvm::IterVar in include/tvm/tir/ [...]
+<tr id="row_107_113_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_113_" class="arrow" onclick="toggleFolder('107_113_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureCallback.html" target="_self">tvm::auto_scheduler::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__schedul [...]
+<tr id="row_107_113_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallback.html" target="_self">tvm::auto_scheduler::PythonBasedMeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PythonBasedMeasureCallbackNode.html" title="A wrapper for [...]
+<tr id="row_107_113_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFile.html" target="_self">tvm::auto_scheduler::RecordToFile</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordToFileNode.html" title="Callback for logging the input and results of measureme [...]
+<tr id="row_107_114_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInput.html" target="_self">tvm::auto_scheduler::MeasureInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureInputNode.html" title="Store the input of a measurement. ">MeasureInputNode</a> [...]
+<tr id="row_107_115_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResult.html" target="_self">tvm::auto_scheduler::MeasureResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1MeasureResultNode.html" title="Store the results of a measurement. ">MeasureResultNod [...]
+<tr id="row_107_116_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_116_" class="arrow" onclick="toggleFolder('107_116_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramBuilder.html" target="_self">tvm::auto_scheduler::ProgramBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
+<tr id="row_107_116_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilder.html" target="_self">tvm::auto_scheduler::LocalBuilder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalBuilderNode.html" title="LocalBuilder use local CPU cores to build programs in p [...]
+<tr id="row_107_117_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurer.html" target="_self">tvm::auto_scheduler::ProgramMeasurer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramMeasurerNode.html" title="Measurer that measures the time costs of tvm pro [...]
+<tr id="row_107_118_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_118_" class="arrow" onclick="toggleFolder('107_118_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ProgramRunner.html" target="_self">tvm::auto_scheduler::ProgramRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1 [...]
+<tr id="row_107_118_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunner.html" target="_self">tvm::auto_scheduler::LocalRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1LocalRunnerNode.html" title="LocalRunner that uses local CPU/GPU to measure the time co [...]
+<tr id="row_107_118_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunner.html" target="_self">tvm::auto_scheduler::RPCRunner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RPCRunnerNode.html" title="RPCRunner that uses RPC call to measures the time cost of progra [...]
+<tr id="row_107_119_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReader.html" target="_self">tvm::auto_scheduler::RecordReader</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RecordReaderNode.html" title="Log reader to load step logs from a file. ">RecordReaderN [...]
+<tr id="row_107_120_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_120_" class="arrow" onclick="toggleFolder('107_120_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchCallback.html" target="_self">tvm::auto_scheduler::SearchCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler [...]
+<tr id="row_107_120_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStates.html" target="_self">tvm::auto_scheduler::PreloadMeasuredStates</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PreloadMeasuredStatesNode.html" title="Preload measured states from [...]
+<tr id="row_107_121_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicy.html" target="_self">tvm::auto_scheduler::SearchPolicy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchPolicyNode.html" title="The base class of search policies. ">SearchPolicyNode</a> [...]
+<tr id="row_107_122_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTask.html" target="_self">tvm::auto_scheduler::SearchTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SearchTaskNode.html" title="The computation information and hardware parameters for a speci [...]
+<tr id="row_107_123_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Stage.html" target="_self">tvm::auto_scheduler::Stage</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StageNode.html" title="A op stage in the compute declaration. Similar to te::Stage in include/tvm/te/ [...]
+<tr id="row_107_124_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1State.html" target="_self">tvm::auto_scheduler::State</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StateNode.html" title="A state in the search process. It consists of the current loop structure and a [...]
+<tr id="row_107_125_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_125_" class="arrow" onclick="toggleFolder('107_125_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1Step.html" target="_self">tvm::auto_scheduler::Step</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StepNode.html" t [...]
+<tr id="row_107_125_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStep.html" target="_self">tvm::auto_scheduler::AnnotationStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1AnnotationStepNode.html" title="Annotation step that corresponds to vectorize, pa [...]
+<tr id="row_107_125_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStep.html" target="_self">tvm::auto_scheduler::CacheReadStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheReadStepNode.html" title="Cache read step that corresponds to te::Schedule::ca [...]
+<tr id="row_107_125_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html" target="_self">tvm::auto_scheduler::CacheWriteStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStepNode.html" title="Cache write step that corresponds to te::Schedule [...]
+<tr id="row_107_125_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStep.html" target="_self">tvm::auto_scheduler::ComputeAtStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeAtStepNode.html" title="Compute at step that corresponds to te::Stage::compu [...]
+<tr id="row_107_125_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStep.html" target="_self">tvm::auto_scheduler::ComputeInlineStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeInlineStepNode.html" title="Compute inline step that corresponds to [...]
+<tr id="row_107_125_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStep.html" target="_self">tvm::auto_scheduler::ComputeRootStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ComputeRootStepNode.html" title="Compute root step that corresponds to te::Stag [...]
+<tr id="row_107_125_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStep.html" target="_self">tvm::auto_scheduler::FollowFusedSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowFusedSplitStepNode.html" title="Similar to FollowSplitStep, but [...]
+<tr id="row_107_125_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStep.html" target="_self">tvm::auto_scheduler::FollowSplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FollowSplitStepNode.html" title="Similar to SplitStepNode, but uses split facto [...]
+<tr id="row_107_125_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStep.html" target="_self">tvm::auto_scheduler::FuseStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1FuseStepNode.html" title="Fuse step that corresponds to te::Stage::fuse. ">FuseStepNode</a> < [...]
+<tr id="row_107_125_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStep.html" target="_self">tvm::auto_scheduler::PragmaStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1PragmaStepNode.html" title="Pragma step that corresponds to te::Stage::pragma. ">PragmaSt [...]
+<tr id="row_107_125_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStep.html" target="_self">tvm::auto_scheduler::ReorderStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1ReorderStepNode.html" title="Reorder step that corresponds to te::Stage::reorder. ">Re [...]
+<tr id="row_107_125_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStep.html" target="_self">tvm::auto_scheduler::RfactorStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1RfactorStepNode.html" title="Reduction factor step that corresponds to te::Schedule::r [...]
+<tr id="row_107_125_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStep.html" target="_self">tvm::auto_scheduler::SplitStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1SplitStepNode.html" title="Split step that corresponds to te::Stage::split with additional [...]
+<tr id="row_107_125_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStep.html" target="_self">tvm::auto_scheduler::StorageAlignStep</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1StorageAlignStepNode.html" title="Storage align step that corresponds to te: [...]
+<tr id="row_107_126_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptions.html" target="_self">tvm::auto_scheduler::TuningOptions</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1auto__scheduler_1_1TuningOptionsNode.html" title="Tuning and measurement options. ">TuningOptionsNode</a [...]
+<tr id="row_107_127_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_127_" class="arrow" onclick="toggleFolder('107_127_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseExpr.html" target="_self">tvm::BaseExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseExprNode.html" title="Base type of all the expressions. "> [...]
+<tr id="row_107_127_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_127_0_" class="arrow" onclick="toggleFolder('107_127_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimExpr.html" target="_self">tvm::PrimExpr</a></td><td class="desc">Reference to <a class="el" href="classtvm_1_1PrimExprNode.html" title="Base node of all primitive expressions [...]
+<tr id="row_107_127_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_127_0_0_" class="arrow" onclick="toggleFolder('107_127_0_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterMapExpr.html" target="_self">tvm::arith::IterMapExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterMapExprNode.htm [...]
+<tr id="row_107_127_0_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSplitExpr.html" target="_self">tvm::arith::IterSplitExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html" title="Split of an iterator. ">IterSplitExprNode</a> </td></tr>
+<tr id="row_107_127_0_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1arith_1_1IterSumExpr.html" target="_self">tvm::arith::IterSumExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1arith_1_1IterSumExprNode.html" title="Fuse multiple iterators by summing them with scaling. ">IterSumExprNode</a> </td></tr>
+<tr id="row_107_127_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">tvm::FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
+<tr id="row_107_127_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_127_0_2_" class="arrow" onclick="toggleFolder('107_127_0_2_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">tvm::IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literal [...]
+<tr id="row_107_127_0_2_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">tvm::Bool</a></td><td class="desc">Boolean constant </td></tr>
+<tr id="row_107_127_0_2_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">tvm::Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
+<tr id="row_107_127_0_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">tvm::tir::Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b ">AddNode</a> </td></tr>
+<tr id="row_107_127_0_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">tvm::tir::And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a && b ">AndNode</a> </td></tr>
+<tr id="row_107_127_0_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">tvm::tir::Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape. ">AnyNode</a> </td></tr>
+<tr id="row_107_127_0_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">tvm::tir::Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value. ">BroadcastNode</a> </td></tr>
+<tr id="row_107_127_0_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">tvm::tir::BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer. ">BufferLoadNode</a> </td></tr>
+<tr id="row_107_127_0_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">tvm::tir::Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node. ">CallNode</a> </td></tr>
+<tr id="row_107_127_0_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">tvm::tir::Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another. ">CastNode</a> </td></tr>
+<tr id="row_107_127_0_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">tvm::tir::Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics. ">DivNode</a> </td></tr>
+<tr id="row_107_127_0_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">tvm::tir::EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b ">EQNode</a> </td></tr>
+<tr id="row_107_127_0_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">tvm::tir::FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b) ">FloorDivNode</a> </td></tr>
+<tr id="row_107_127_0_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">tvm::tir::FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv. ">FloorModNode</a> </td></tr>
+<tr id="row_107_127_0_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">tvm::tir::GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a >= b ">GENode</a> </td></tr>
+<tr id="row_107_127_0_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">tvm::tir::GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a > b ">GTNode</a> </td></tr>
+<tr id="row_107_127_0_16_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">tvm::tir::LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a <= b ">LENode</a> </td></tr>
+<tr id="row_107_127_0_17_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">tvm::tir::Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body. ">LetNode</a> </td></tr>
+<tr id="row_107_127_0_18_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Load.html" target="_self">tvm::tir::Load</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoadNode.html" title="Load the value from buffer_var. ">LoadNode</a> </td></tr>
+<tr id="row_107_127_0_19_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">tvm::tir::LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a < b ">LTNode</a> </td></tr>
+<tr id="row_107_127_0_20_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">tvm::tir::Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b) ">MaxNode</a> </td></tr>
+<tr id="row_107_127_0_21_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">tvm::tir::Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b) ">MinNode</a> </td></tr>
+<tr id="row_107_127_0_22_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">tvm::tir::Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics. ">ModNode</a> </td></tr>
+<tr id="row_107_127_0_23_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">tvm::tir::Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b ">MulNode</a> </td></tr>
+<tr id="row_107_127_0_24_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">tvm::tir::NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b ">NENode</a> </td></tr>
+<tr id="row_107_127_0_25_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">tvm::tir::Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a ">NotNode</a> </td></tr>
+<tr id="row_107_127_0_26_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">tvm::tir::Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b ">OrNode</a> </td></tr>
+<tr id="row_107_127_0_27_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">tvm::tir::ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer. ">ProducerLoadNode</a> </td></tr>
+<tr id="row_107_127_0_28_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">tvm::tir::Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to const [...]
+<tr id="row_107_127_0_29_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">tvm::tir::Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator. ">ReduceNode</a> </td></tr>
+<tr id="row_107_127_0_30_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">tvm::tir::Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value. ">SelectNode</a> </td></tr>
+<tr id="row_107_127_0_31_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">tvm::tir::Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...">ShuffleNode [...]
+<tr id="row_107_127_0_32_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">tvm::tir::StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts. ">StringImmNode</a> </td></tr>
+<tr id="row_107_127_0_33_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">tvm::tir::Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b ">SubNode</a> </td></tr>
+<tr id="row_107_127_0_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_127_0_34_" class="arrow" onclick="toggleFolder('107_127_0_34_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Var.html" target="_self">tvm::tir::Var</a></td><td class="desc">Named variable in TIR </td></tr>
+<tr id="row_107_127_0_34_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVar.html" target="_self">tvm::tir::SizeVar</a></td><td class="desc">Named variable represents a tensor index size </td></tr>
+<tr id="row_107_127_1_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_127_1_" class="arrow" onclick="toggleFolder('107_127_1_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayExpr.html" target="_self">tvm::RelayExpr</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayExprNode.html" title="Base node of all non-primit [...]
+<tr id="row_107_127_1_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span id="arr_107_127_1_0_" class="arrow" onclick="toggleFolder('107_127_1_0_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseFunc.html" target="_self">tvm::BaseFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseFuncNode.html" title="Base node of all functio [...]
+<tr id="row_107_127_1_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">tvm::relay::Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_107_127_1_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">tvm::tir::PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements. ">PrimFuncNode</a> </td></tr>
+<tr id="row_107_127_1_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">tvm::Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
+<tr id="row_107_127_1_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">tvm::GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
+<tr id="row_107_127_1_3_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">tvm::Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
+<tr id="row_107_127_1_4_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">tvm::relay::Call</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_5_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">tvm::relay::Constant</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_6_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">tvm::relay::If</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_7_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">tvm::relay::Let</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_8_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">tvm::relay::Match</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_9_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">tvm::relay::RefCreate</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_10_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">tvm::relay::RefRead</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_11_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">tvm::relay::RefWrite</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_12_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">tvm::relay::TempExpr</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_13_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">tvm::relay::Tuple</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_14_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">tvm::relay::TupleGetItem</a></td><td class="desc"></td></tr>
+<tr id="row_107_127_1_15_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">tvm::relay::Var</a></td><td class="desc"></td></tr>
+<tr id="row_107_128_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1CompilationConfig.html" target="_self">tvm::CompilationConfig</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1CompilationConfig.html" title="Managed reference class to CompilationConfig. ">CompilationConfig</a></code> </td></tr>
+<tr id="row_107_129_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Diagnostic.html" target="_self">tvm::Diagnostic</a></td><td class="desc"></td></tr>
+<tr id="row_107_130_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticContext.html" target="_self">tvm::DiagnosticContext</a></td><td class="desc"></td></tr>
+<tr id="row_107_131_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DiagnosticRenderer.html" target="_self">tvm::DiagnosticRenderer</a></td><td class="desc"></td></tr>
+<tr id="row_107_132_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">tvm::EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM's global environment. ">EnvFuncNode</a> </td></tr>
+<tr id="row_107_133_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">tvm::GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
+<tr id="row_107_134_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1instrument_1_1PassInstrument.html" target="_self">tvm::instrument::PassInstrument</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1instrument_1_1PassInstrumentNode.html" title="PassInstrumentNode forms an instrument implementation. It [...]
+<tr id="row_107_135_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">tvm::IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
+<tr id="row_107_136_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">tvm::MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
+<tr id="row_107_137_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBest.html" target="_self">tvm::meta_schedule::ApplyHistoryBest</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ApplyHistoryBestNode.html" title="An integration context that allows application o [...]
+<tr id="row_107_138_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_138_" class="arrow" onclick="toggleFolder('107_138_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfo.html" target="_self">tvm::meta_schedule::ArgInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ArgInfoNode.h [...]
+<tr id="row_107_138_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfo.html" target="_self">tvm::meta_schedule::TensorInfo</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TensorInfoNode.html" title="The tensor argument information. ">TensorInfoNode</a> </td></tr>
+<tr id="row_107_139_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Builder.html" target="_self">tvm::meta_schedule::Builder</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderNode.html" title="The abstract builder interface. ">BuilderNode</a> </td></tr>
+<tr id="row_107_140_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInput.html" target="_self">tvm::meta_schedule::BuilderInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderInputNode.html" title="The builder's input, containing an IRModule and the targ [...]
+<tr id="row_107_141_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResult.html" target="_self">tvm::meta_schedule::BuilderResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1BuilderResultNode.html" title="The builder's output, containing the artifact path or [...]
+<tr id="row_107_142_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1CostModel.html" target="_self">tvm::meta_schedule::CostModel</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1CostModelNode.html" title="Cost model. ">CostModelNode</a> </td></tr>
+<tr id="row_107_143_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Database.html" target="_self">tvm::meta_schedule::Database</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1DatabaseNode.html">DatabaseNode</a> </td></tr>
+<tr id="row_107_144_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_self">tvm::meta_schedule::ExtractedTask</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ExtractedTaskNode.html" title="A tuning task extracted from the high-level IR. ">Extract [...]
+<tr id="row_107_145_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html" target="_self">tvm::meta_schedule::FeatureExtractor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1FeatureExtractorNode.html" title="Extractor for features from measure candidates f [...]
+<tr id="row_107_146_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallback.html" target="_self">tvm::meta_schedule::MeasureCallback</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCallbackNode.html" title="Rules to apply after measure results is available. [...]
+<tr id="row_107_147_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_self">tvm::meta_schedule::MeasureCandidate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MeasureCandidateNode.html" title="The schedule (with input shapes) to be measured. [...]
+<tr id="row_107_148_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Mutator.html" target="_self">tvm::meta_schedule::Mutator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1MutatorNode.html" title="Mutator is designed to mutate the trace to explore the design space. ">Muta [...]
+<tr id="row_107_149_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Postproc.html" target="_self">tvm::meta_schedule::Postproc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1PostprocNode.html" title="Rules to apply a postprocessor to a schedule. ">PostprocNode</a> </td></tr>
+<tr id="row_107_150_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Runner.html" target="_self">tvm::meta_schedule::Runner</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerNode.html" title="The abstract runner interface. ">RunnerNode</a> </td></tr>
+<tr id="row_107_151_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_self">tvm::meta_schedule::RunnerFuture</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerFutureNode.html" title="A class to asynchronously fetch runner's output. ">Runne [...]
+<tr id="row_107_152_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_self">tvm::meta_schedule::RunnerInput</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerInputNode.html" title="Runner's input containing path of artifact, type of device [...]
+<tr id="row_107_153_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_self">tvm::meta_schedule::RunnerResult</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1RunnerResultNode.html" title="Runner's output containing measurement result of Measure [...]
+<tr id="row_107_154_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRule.html" target="_self">tvm::meta_schedule::ScheduleRule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1ScheduleRuleNode.html" title="Rules to modify a block in a schedule. ">ScheduleRuleNode</a [...]
+<tr id="row_107_155_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_self">tvm::meta_schedule::SearchStrategy</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SearchStrategyNode.html" title="The search strategy for measure candidates generation. [...]
+<tr id="row_107_156_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_self">tvm::meta_schedule::SpaceGenerator</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1SpaceGeneratorNode.html" title="The abstract class for design space generation. ">Spac [...]
+<tr id="row_107_157_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_self">tvm::meta_schedule::TaskScheduler</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TaskSchedulerNode.html" title="The abstract interface of task schedulers. ">TaskSchedule [...]
+<tr id="row_107_158_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_self">tvm::meta_schedule::TuneContext</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1TuneContextNode.html" title="The auto tuning context. ">TuneContextNode</a> </td></tr>
+<tr id="row_107_159_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_self">tvm::meta_schedule::TuningRecord</a></td><td class="desc">The managed reference of <a class="el" href="classtvm_1_1meta__schedule_1_1TuningRecordNode.html" title="The class of tuning records. ">TuningRecordNode</a> </td></tr>
+<tr id="row_107_160_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1meta__schedule_1_1Workload.html" target="_self">tvm::meta_schedule::Workload</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1meta__schedule_1_1WorkloadNode.html" title="A workload, i.e. an IRModule and its structural hash. ">WorkloadNode</a> [...]
+<tr id="row_107_161_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1Source.html" target="_self">tvm::parser::Source</a></td><td class="desc"></td></tr>
+<tr id="row_107_162_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1parser_1_1SourceMap.html" target="_self">tvm::parser::SourceMap</a></td><td class="desc"></td></tr>
+<tr id="row_107_163_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PoolInfo.html" target="_self">tvm::PoolInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_164_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Range.html" target="_self">tvm::Range</a></td><td class="desc"><a class="el" href="classtvm_1_1Range.html" title="Range constainer. ">Range</a> constainer </td></tr>
+<tr id="row_107_165_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">tvm::relay::Clause</a></td><td class="desc"></td></tr>
+<tr id="row_107_166_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">tvm::relay::ConstructorValue</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_167_" class="arrow" onclick="toggleFolder('107_167_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">tvm::relay::DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
+<tr id="row_107_167_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AltPattern.html" target="_self">tvm::relay::AltPattern</a></td><td class="desc">A pattern which matches either of two patterns </td></tr>
+<tr id="row_107_167_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1AttrPattern.html" target="_self">tvm::relay::AttrPattern</a></td><td class="desc">A pattern which matches attributes in another pattern </td></tr>
+<tr id="row_107_167_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">tvm::relay::CallPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">tvm::relay::ConstantPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">tvm::relay::DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_107_167_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">tvm::relay::DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
+<tr id="row_107_167_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">tvm::relay::ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
+<tr id="row_107_167_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionPattern.html" target="_self">tvm::relay::FunctionPattern</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_107_167_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfPattern.html" target="_self">tvm::relay::IfPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetPattern.html" target="_self">tvm::relay::LetPattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Let.html">Let</a> binding that binds a local var </td></tr>
+<tr id="row_107_167_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">tvm::relay::ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_107_167_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">tvm::relay::TupleGetItemPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">tvm::relay::TuplePattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">tvm::relay::TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_107_167_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">tvm::relay::VarPattern</a></td><td class="desc"></td></tr>
+<tr id="row_107_167_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">tvm::relay::WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
+<tr id="row_107_168_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">tvm::relay::DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
+<tr id="row_107_169_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Executor.html" target="_self">tvm::relay::Executor</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1ExecutorNode.html" title="Executor information. ">ExecutorNode</a> </td></tr>
+<tr id="row_107_170_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">tvm::relay::Id</a></td><td class="desc"></td></tr>
+<tr id="row_107_171_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">tvm::relay::OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
+<tr id="row_107_172_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">tvm::relay::OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
+<tr id="row_107_173_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">tvm::relay::OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
+<tr id="row_107_174_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_174_" class="arrow" onclick="toggleFolder('107_174_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">tvm::relay::Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT mat [...]
+<tr id="row_107_174_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">tvm::relay::PatternConstructor</a></td><td class="desc"></td></tr>
+<tr id="row_107_174_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">tvm::relay::PatternTuple</a></td><td class="desc"></td></tr>
+<tr id="row_107_174_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">tvm::relay::PatternVar</a></td><td class="desc"></td></tr>
+<tr id="row_107_174_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">tvm::relay::PatternWildcard</a></td><td class="desc"></td></tr>
+<tr id="row_107_175_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">tvm::relay::RecClosure</a></td><td class="desc"></td></tr>
+<tr id="row_107_176_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">tvm::relay::RefValue</a></td><td class="desc"></td></tr>
+<tr id="row_107_177_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Runtime.html" target="_self">tvm::relay::Runtime</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1relay_1_1RuntimeNode.html" title="Runtime information. ">RuntimeNode</a> </td></tr>
+<tr id="row_107_178_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">tvm::runtime::ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
+<tr id="row_107_179_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">tvm::runtime::Array< T, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contiguous sequence of ObjectRefs. ">Array</a>, container representing a [...]
+<tr id="row_107_180_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_180_" class="arrow" onclick="toggleFolder('107_180_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Closure.html" target="_self">tvm::runtime::Closure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_107_180_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">tvm::relay::InterpreterClosure</a></td><td class="desc"></td></tr>
+<tr id="row_107_180_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMClosure.html" target="_self">tvm::runtime::vm::VMClosure</a></td><td class="desc">Reference to closure </td></tr>
+<tr id="row_107_181_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Map.html" target="_self">tvm::runtime::Map< K, V, typename, typename ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Map.html" title="Map container of NodeRef->NodeRef in DSL graph. Map implements copy on write semantics, which m [...]
+<tr id="row_107_182_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_182_" class="arrow" onclick="toggleFolder('107_182_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataBase.html" target="_self">tvm::runtime::metadata::MetadataBase</a></td><td class="desc">Reference class for the common <a class="el" href="classtvm_1_1ru [...]
+<tr id="row_107_182_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1Metadata.html" target="_self">tvm::runtime::metadata::Metadata</a></td><td class="desc"></td></tr>
+<tr id="row_107_182_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" target="_self">tvm::runtime::metadata::MetadataArray</a></td><td class="desc">Reference class for <a class="el" href="classtvm_1_1runtime_1_1metadata_1_1MetadataArray.html" title="Reference class for MetadataArray. ">MetadataA [...]
+<tr id="row_107_182_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1metadata_1_1TensorInfo.html" target="_self">tvm::runtime::metadata::TensorInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_183_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Module.html" target="_self">tvm::runtime::Module</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Module.html" title="Module container of TVM. ">Module</a> container of TVM </td></tr>
+<tr id="row_107_184_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1NDArray.html" target="_self">tvm::runtime::NDArray</a></td><td class="desc">Managed <a class="el" href="classtvm_1_1runtime_1_1NDArray.html" title="Managed NDArray. The array is backed by reference counted blocks. ">NDArray</a>. The array is backed by referenc [...]
+<tr id="row_107_185_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Optional.html" target="_self">tvm::runtime::Optional< T ></a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Optional.html" title="Optional container that to represent to a Nullable variant of T. ">Optional</a> container that to represen [...]
+<tr id="row_107_186_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" target="_self">tvm::runtime::PackedFunc</a></td><td class="desc">Packed function is a type-erased function. The arguments are passed by packed format </td></tr>
+<tr id="row_107_187_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1DeviceWrapper.html" target="_self">tvm::runtime::profiling::DeviceWrapper</a></td><td class="desc">Wrapper for <code>Device</code> </td></tr>
+<tr id="row_107_188_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollector.html" target="_self">tvm::runtime::profiling::MetricCollector</a></td><td class="desc">Wrapper for <code><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1MetricCollectorNode.html" title="Interface for user defined profiling [...]
+<tr id="row_107_189_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1profiling_1_1Report.html" target="_self">tvm::runtime::profiling::Report</a></td><td class="desc"></td></tr>
+<tr id="row_107_190_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ShapeTuple.html" target="_self">tvm::runtime::ShapeTuple</a></td><td class="desc">Reference to shape tuple objects </td></tr>
+<tr id="row_107_191_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1String.html" target="_self">tvm::runtime::String</a></td><td class="desc">Reference to string objects </td></tr>
+<tr id="row_107_192_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Timer.html" target="_self">tvm::runtime::Timer</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Timer.html" title="Timer for a specific device. ">Timer</a> for a specific device </td></tr>
+<tr id="row_107_193_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1vm_1_1Storage.html" target="_self">tvm::runtime::vm::Storage</a></td><td class="desc">Reference to storage </td></tr>
+<tr id="row_107_194_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SourceName.html" target="_self">tvm::SourceName</a></td><td class="desc">The source name of a file span </td></tr>
+<tr id="row_107_195_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">tvm::Span</a></td><td class="desc"></td></tr>
+<tr id="row_107_196_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">tvm::Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
+<tr id="row_107_197_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetKind.html" target="_self">tvm::TargetKind</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetKindNode.html" title="Target kind, specifies the kind of the target. ">TargetKindNode</a> </td></tr>
+<tr id="row_107_198_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetTag.html" target="_self">tvm::TargetTag</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetTagNode.html" title="A target tag. ">TargetTagNode</a> </td></tr>
+<tr id="row_107_199_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html" target="_self">tvm::te::IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
+<tr id="row_107_200_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_200_" class="arrow" onclick="toggleFolder('107_200_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html" target="_self">tvm::te::IterVarRelation</a></td><td class="desc">The schedule relation between IterVars can be <a class="el" href="classtvm_1_1te_1_1Split.h [...]
+<tr id="row_107_200_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Fuse.html" target="_self">tvm::te::Fuse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1FuseNode.html" title="Fuse two domains into one domain. ">FuseNode</a> </td></tr>
+<tr id="row_107_200_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Rebase.html" target="_self">tvm::te::Rebase</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1RebaseNode.html" title="Rebase the iteration to make min to be 0. This is useful to normalize the Schedule to make every leaf...">Rebas [...]
+<tr id="row_107_200_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Singleton.html" target="_self">tvm::te::Singleton</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SingletonNode.html" title="Singleton iterator [0, 1) ">SingletonNode</a> </td></tr>
+<tr id="row_107_200_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Split.html" target="_self">tvm::te::Split</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SplitNode.html" title="Split the parent domain into product of outer and iter. ">SplitNode</a> </td></tr>
+<tr id="row_107_200_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Transform.html" target="_self">tvm::te::Transform</a></td><td class="desc"></td></tr>
+<tr id="row_107_201_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_201_" class="arrow" onclick="toggleFolder('107_201_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Operation.html" target="_self">tvm::te::Operation</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operati [...]
+<tr id="row_107_201_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOp.html" target="_self">tvm::te::ComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" title="A Compute op that compute a tensor on certain domain. ">ComputeOpNode</a> </td></tr>
+<tr id="row_107_201_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOp.html" target="_self">tvm::te::ExternOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" title="External computation that cannot be splitted. ">ExternOpNode</a> </td></tr>
+<tr id="row_107_201_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOp.html" target="_self">tvm::te::HybridOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" title="A computation operator that generated by hybrid script. ">HybridOpNode</a> </td></tr>
+<tr id="row_107_201_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html" target="_self">tvm::te::PlaceholderOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html" title="A placeholder op represents an input placeholder. ">PlaceholderOpNode</a> </td></tr>
+<tr id="row_107_201_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOp.html" target="_self">tvm::te::ScanOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" title="Symbolic scan. ">ScanOpNode</a> </td></tr>
+<tr id="row_107_201_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOp.html" target="_self">tvm::te::TensorComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" title="A TenorCompute op that compute a tensor with an tensor intrinsic. ">TensorComputeOpN [...]
+<tr id="row_107_202_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Schedule.html" target="_self">tvm::te::Schedule</a></td><td class="desc">Global schedule container For operations and all the operations they depend on. The schedule per <a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. " [...]
+<tr id="row_107_203_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html" target="_self">tvm::te::SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </td></tr>
+<tr id="row_107_204_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Stage.html" target="_self">tvm::te::Stage</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">Stage</a>, contains scheduling for a stage of computation </td></tr>
+<tr id="row_107_205_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html" target="_self">tvm::te::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" title="Node to represent a Tensor intrinsic operator. ">TensorIntrinNode</a> </td></tr>
+<tr id="row_107_206_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html" target="_self">tvm::te::TensorIntrinCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html">TensorIntrinCallNode</a> </td></tr>
+<tr id="row_107_207_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">tvm::tir::BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode. ">Layout</a>, < [...]
+<tr id="row_107_208_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRV.html" target="_self">tvm::tir::BlockRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRVNode.html" title="A random variable that evaluates to a TensorIR block. ">BlockRVNode</a> </td></tr>
+<tr id="row_107_209_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockScope.html" target="_self">tvm::tir::BlockScope</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockScopeNode.html" title="An object with 1-to-1 correspondence with each block reference in the sref tree. This data structu [...]
+<tr id="row_107_210_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">tvm::tir::Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">Buffer</a> is a symbolic n-darray structur [...]
+<tr id="row_107_211_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRegion.html" target="_self">tvm::tir::BufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRegionNode.html" title="Representing the region of multi-dimensional buffer access. ">BufferRegionNode</a> </td></tr>
+<tr id="row_107_212_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">tvm::tir::CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element..." [...]
+<tr id="row_107_213_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_213_" class="arrow" onclick="toggleFolder('107_213_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">tvm::tir::DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base [...]
+<tr id="row_107_213_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Tensor.html" target="_self">tvm::te::Tensor</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> structure representing a possible input [...]
+<tr id="row_107_214_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Dependency.html" target="_self">tvm::tir::Dependency</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DependencyNode.html" title="A tuple (src, dst, kind) representing certain types of dependency. For example, (A, B, kRAW) means [...]
+<tr id="row_107_215_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IndexMap.html" target="_self">tvm::tir::IndexMap</a></td><td class="desc"></td></tr>
+<tr id="row_107_216_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Instruction.html" target="_self">tvm::tir::Instruction</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionNode.html" title="Schedule instructions each corresponds to a schedule primitive. ">InstructionNode</a> </td></tr>
+<tr id="row_107_217_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1InstructionKind.html" target="_self">tvm::tir::InstructionKind</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1InstructionKindNode.html" title="Kind of an instruction, e.g. Split, Reorder, etc. Besides the name, every kind of i [...]
+<tr id="row_107_218_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVar.html" target="_self">tvm::tir::IterVar</a></td><td class="desc">Iteration Variable, represents an iteration over an integer interval </td></tr>
+<tr id="row_107_219_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">tvm::tir::Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">Layo [...]
+<tr id="row_107_220_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LoopRV.html" target="_self">tvm::tir::LoopRV</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoopRVNode.html" title="A random variable that evaluates to a TensorIR for loop. ">LoopRVNode</a> </td></tr>
+<tr id="row_107_221_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MatchBufferRegion.html" target="_self">tvm::tir::MatchBufferRegion</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MatchBufferRegionNode.html" title="Match introduces a constraint that the source buffer region can be remapped t [...]
+<tr id="row_107_222_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Schedule.html" target="_self">tvm::tir::Schedule</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleNode.html" title="The user-facing schedule class. ">ScheduleNode</a> </td></tr>
+<tr id="row_107_223_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ScheduleState.html" target="_self">tvm::tir::ScheduleState</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ScheduleStateNode.html" title="The state of scheduling, which exposes a Replace method as the primary interface for all [...]
+<tr id="row_107_224_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_224_" class="arrow" onclick="toggleFolder('107_224_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Stmt.html" target="_self">tvm::tir::Stmt</a></td><td class="desc">Container of all statements </td></tr>
+<tr id="row_107_224_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Allocate.html" target="_self">tvm::tir::Allocate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" title="Allocate a buffer that can be used in body. ">AllocateNode</a> </td></tr>
+<tr id="row_107_224_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AllocateConst.html" target="_self">tvm::tir::AllocateConst</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateConstNode.html" title="Allocate a buffer that can be used in body. ">AllocateConstNode</a> </td></tr>
+<tr id="row_107_224_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html" target="_self">tvm::tir::AssertStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" title="Assert condition, if an error occurs, return the error message. ">AssertStmtNode</a> </td></tr>
+<tr id="row_107_224_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html" target="_self">tvm::tir::AttrStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" title="Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary inform. [...]
+<tr id="row_107_224_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Block.html" target="_self">tvm::tir::Block</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockNode.html" title="A block is a basic schedule unit in TIR. ">BlockNode</a> </td></tr>
+<tr id="row_107_224_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BlockRealize.html" target="_self">tvm::tir::BlockRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BlockRealizeNode.html" title="A block realization node represents execution of the block at the binding values. ...">Blo [...]
+<tr id="row_107_224_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html" target="_self">tvm::tir::BufferRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" title="Annotate the region where the buffer need to be read and write in the body. We only nee [...]
+<tr id="row_107_224_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStore.html" target="_self">tvm::tir::BufferStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" title="Store value to the high dimension buffer. ">BufferStoreNode</a> </td></tr>
+<tr id="row_107_224_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Evaluate.html" target="_self">tvm::tir::Evaluate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" title="Evaluates an expression. This is mostly used for putting a Call node into Stmt. ">EvaluateNode</a> </td></tr>
+<tr id="row_107_224_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1For.html" target="_self">tvm::tir::For</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ForNode.html" title="A for loop, with poissible type annotations. ">ForNode</a> </td></tr>
+<tr id="row_107_224_10_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html" target="_self">tvm::tir::IfThenElse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" title="IfThenElse statment. ">IfThenElseNode</a> </td></tr>
+<tr id="row_107_224_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmt.html" target="_self">tvm::tir::LetStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" title="Let binding, bind var to value, then run body. ">LetStmtNode</a> </td></tr>
+<tr id="row_107_224_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Prefetch.html" target="_self">tvm::tir::Prefetch</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" title="A prefetch hint for a buffer. ">PrefetchNode</a> </td></tr>
+<tr id="row_107_224_13_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerRealize.html" target="_self">tvm::tir::ProducerRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html" title="Annotate the bounds where the data produced by the producer need to be written a [...]
+<tr id="row_107_224_14_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerStore.html" target="_self">tvm::tir::ProducerStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerStoreNode.html" title="Store value into mult-dimensional array that will be read by the consumer of the produ [...]
+<tr id="row_107_224_15_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html" target="_self">tvm::tir::SeqStmt</a></td><td class="desc">Sequence statement </td></tr>
+<tr id="row_107_224_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Store.html" target="_self">tvm::tir::Store</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StoreNode.html" title="Store value to the buffer. ">StoreNode</a> </td></tr>
+<tr id="row_107_224_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1While.html" target="_self">tvm::tir::While</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1WhileNode.html" title="A While loop. ">WhileNode</a> </td></tr>
+<tr id="row_107_225_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtSRef.html" target="_self">tvm::tir::StmtSRef</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StmtSRefNode.html" title="An object that refers to schedulable elements (block/for-loop) in TensorIR, aka "sref". ">Stmt [...]
+<tr id="row_107_226_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1TensorIntrin.html" target="_self">tvm::tir::TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TensorIntrinNode.html" title="Tensor intrinsics for tensorization. ">TensorIntrinNode</a> </td></tr>
+<tr id="row_107_227_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Trace.html" target="_self">tvm::tir::Trace</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1TraceNode.html" title="An execution trace of a scheduling program. ">TraceNode</a> </td></tr>
+<tr id="row_107_228_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1AllocatedPoolInfo.html" target="_self">tvm::tir::usmp::AllocatedPoolInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_229_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfo.html" target="_self">tvm::tir::usmp::BufferInfo</a></td><td class="desc"></td></tr>
+<tr id="row_107_230_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1BufferInfoAnalysis.html" target="_self">tvm::tir::usmp::BufferInfoAnalysis</a></td><td class="desc"></td></tr>
+<tr id="row_107_231_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1usmp_1_1PoolAllocation.html" target="_self">tvm::tir::usmp::PoolAllocation</a></td><td class="desc"></td></tr>
+<tr id="row_107_232_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_232_" class="arrow" onclick="toggleFolder('107_232_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Pass.html" target="_self">tvm::transform::Pass</a></td><td class="desc"></td></tr>
+<tr id="row_107_232_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Sequential.html" target="_self">tvm::transform::Sequential</a></td><td class="desc"></td></tr>
+<tr id="row_107_233_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassContext.html" target="_self">tvm::transform::PassContext</a></td><td class="desc"><a class="el" href="classtvm_1_1transform_1_1PassContext.html" title="PassContext that is used to configure the pass behavior. ">PassContext</a> that is used to configure t [...]
+<tr id="row_107_234_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassInfo.html" target="_self">tvm::transform::PassInfo</a></td><td class="desc">Managed reference class for <a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html" title="Meta data that will be used to help optimization and analysis. ">PassInfoNode</ [...]
+<tr id="row_107_235_" class="even" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_107_235_" class="arrow" onclick="toggleFolder('107_235_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">tvm::Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</ [...]
+<tr id="row_107_235_0_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseTensorType.html" target="_self">tvm::BaseTensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1BaseTensorTypeNode.html" title="Base of all Tensor types This container can hold TensorType or GenericTensorType. ...">BaseTensorTypeNo [...]
+<tr id="row_107_235_1_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">tvm::FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
+<tr id="row_107_235_2_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">tvm::GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
+<tr id="row_107_235_3_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">tvm::IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNod [...]
+<tr id="row_107_235_4_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PointerType.html" target="_self">tvm::PointerType</a></td><td class="desc"></td></tr>
+<tr id="row_107_235_5_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1PrimType.html" target="_self">tvm::PrimType</a></td><td class="desc"></td></tr>
+<tr id="row_107_235_6_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1RelayRefType.html" target="_self">tvm::RelayRefType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1RelayRefTypeNode.html" title="Reference Type High-level Relay IR. ">RelayRefTypeNode</a> </td></tr>
+<tr id="row_107_235_7_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">tvm::TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> < [...]
+<tr id="row_107_235_8_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">tvm::TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
+<tr id="row_107_235_9_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">tvm::TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
+<tr id="row_107_235_10_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_107_235_10_" class="arrow" onclick="toggleFolder('107_235_10_')">►</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">tvm::TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential [...]
+<tr id="row_107_235_10_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">tvm::TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
+<tr id="row_107_235_11_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">tvm::TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
+<tr id="row_107_235_12_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">tvm::TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
+<tr id="row_107_236_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">tvm::TypedEnvFunc< R(Args...)></a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNo [...]
+<tr id="row_107_237_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">tvm::TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
+<tr id="row_107_238_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1VirtualDevice.html" target="_self">tvm::VirtualDevice</a></td><td class="desc">Managed reference class to <code><a class="el" href="classtvm_1_1VirtualDeviceNode.html" title="Describes at compile time the constraints on where data is to be stored at runtime down to the ( [...]
+<tr id="row_107_239_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1WorkspaceMemoryPools.html" target="_self">tvm::WorkspaceMemoryPools</a></td><td class="desc"></td></tr>
<tr id="row_108_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html" target="_self">tvm::runtime::ObjectTypeChecker< T ></a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> traits for runtime type check during FFI conversion </td></tr>
<tr id="row_109_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html" target="_self">tvm::runtime::ObjectTypeChecker< Array< T > ></a></td><td class="desc"></td></tr>
<tr id="row_110_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Map_3_01K_00_01V_01_4_01_4.html" target="_self">tvm::runtime::ObjectTypeChecker< Map< K, V > ></a></td><td class="desc"></td></tr>
@@ -1268,8 +1269,8 @@ This inheritance list is sorted roughly, but not completely, alphabetically:</di
<tr id="row_153_0_" class="even" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1detail_1_1is__specialized_3_01Container_3_01Args_8_8_8_01_4_00_01Container_01_4.html" target="_self">tvm::detail::is_specialized< Container< Args... >, Container ></a></td><td class="desc"></td></tr>
<tr id="row_154_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm__workspace__t.html" target="_self">tvm_workspace_t</a></td><td class="desc"></td></tr>
<tr id="row_155_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMAotExecutor.html" target="_self">TVMAotExecutor</a></td><td class="desc"></td></tr>
-<tr id="row_156_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">tvm::runtime::TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
-<tr id="row_157_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
+<tr id="row_156_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMArgs.html" target="_self">TVMArgs</a></td><td class="desc"></td></tr>
+<tr id="row_157_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgs.html" target="_self">tvm::runtime::TVMArgs</a></td><td class="desc">Arguments into TVM functions </td></tr>
<tr id="row_158_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TVMArgsSetter.html" target="_self">tvm::runtime::TVMArgsSetter</a></td><td class="desc"></td></tr>
<tr id="row_159_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
<tr id="row_160_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMFuncRegistry.html" target="_self">TVMFuncRegistry</a></td><td class="desc">A data structure that facilitates function lookup by C-string name </td></tr>
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index 6c2153917..bae2058ce 100644
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<g id="a_node1"><a xlink:href="classtvm_1_1runtime_1_1NDArray_1_1ContainerBase.html" target="_top" xlink:title="The container base structure contains all the fields except for the Object header. ">
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@@ -29,15 +29,15 @@
</a>
</g>
</g>
-<!-- Node1227->Node504 -->
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<g id="edge1" class="edge">
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<g id="a_node3"><a xlink:href="classtvm_1_1runtime_1_1InplaceArrayBase.html" target="_top" xlink:title="Base template for classes with array like memory layout. ">
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<text text-anchor="start" x="230.5" y="-2560" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::runtime::InplaceArray</text>
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<g id="a_node5"><a xlink:href="classtvm_1_1runtime_1_1InplaceArrayBase.html" target="_top" xlink:title="tvm::runtime::InplaceArray\lBase\< ADTObj, ObjectRef \>">
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<g id="a_node7"><a xlink:href="classtvm_1_1runtime_1_1InplaceArrayBase.html" target="_top" xlink:title="tvm::runtime::InplaceArray\lBase\< ArrayNode, ObjectRef \>">
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+<text text-anchor="middle" x="293.5" y="-4635.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::EnvFunc</text>
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<g id="node220" class="node">
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-<text text-anchor="middle" x="293.5" y="-4559.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::instrument::PassInstrument</text>
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+<text text-anchor="middle" x="293.5" y="-4597.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::GenericFunc</text>
</a>
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<g id="node221" class="node">
<title>Node220</title>
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-<text text-anchor="middle" x="293.5" y="-4521.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::IRModule</text>
+<g id="a_node221"><a xlink:href="classtvm_1_1instrument_1_1PassInstrument.html" target="_top" xlink:title="Managed reference class for PassInstrumentNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="205.5,-4552.5 205.5,-4571.5 381.5,-4571.5 381.5,-4552.5 205.5,-4552.5"/>
+<text text-anchor="middle" x="293.5" y="-4559.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::instrument::PassInstrument</text>
</a>
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<g id="edge220" class="edge">
<title>Node0->Node220</title>
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<g id="node222" class="node">
<title>Node221</title>
-<g id="a_node222"><a xlink:href="classtvm_1_1MemoryInfo.html" target="_top" xlink:title="Defines memory info. ">
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-<text text-anchor="middle" x="293.5" y="-4483.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::MemoryInfo</text>
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+<polygon fill="#ffffff" stroke="#000000" points="250,-4514.5 250,-4533.5 337,-4533.5 337,-4514.5 250,-4514.5"/>
+<text text-anchor="middle" x="293.5" y="-4521.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::IRModule</text>
</a>
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<g id="edge221" class="edge">
<title>Node0->Node221</title>
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<g id="node223" class="node">
<title>Node222</title>
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-<text text-anchor="middle" x="293.5" y="-4434" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ApplyHistoryBest</text>
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+<polygon fill="#ffffff" stroke="#000000" points="243,-4476.5 243,-4495.5 344,-4495.5 344,-4476.5 243,-4476.5"/>
+<text text-anchor="middle" x="293.5" y="-4483.5" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::MemoryInfo</text>
</a>
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<g id="edge222" class="edge">
<title>Node0->Node222</title>
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<g id="node224" class="node">
<title>Node223</title>
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-<text text-anchor="middle" x="293.5" y="-4385" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ArgInfo</text>
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+<polygon fill="#ffffff" stroke="#000000" points="235.5,-4427 235.5,-4457 351.5,-4457 351.5,-4427 235.5,-4427"/>
+<text text-anchor="start" x="243.5" y="-4445" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4434" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ApplyHistoryBest</text>
</a>
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<g id="edge223" class="edge">
<title>Node0->Node223</title>
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-<g id="node226" class="node">
-<title>Node225</title>
-<g id="a_node226"><a xlink:href="classtvm_1_1meta__schedule_1_1Builder.html" target="_top" xlink:title="Managed reference to BuilderNode. ">
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-<text text-anchor="middle" x="293.5" y="-4336" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Builder</text>
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+<title>Node224</title>
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+<text text-anchor="middle" x="293.5" y="-4385" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ArgInfo</text>
</a>
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-<title>Node0->Node225</title>
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+<g id="edge224" class="edge">
+<title>Node0->Node224</title>
+<path fill="none" stroke="#191970" d="M68.2702,-5170.3618C75.8669,-5055.6093 116.764,-4474.9332 170,-4418 186.5846,-4400.2637 212.0056,-4393.208 235.4823,-4390.9053"/>
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<title>Node226</title>
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+<polygon fill="#ffffff" stroke="#000000" points="235.5,-4329 235.5,-4359 351.5,-4359 351.5,-4329 235.5,-4329"/>
+<text text-anchor="start" x="243.5" y="-4347" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4336" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Builder</text>
</a>
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<title>Node0->Node226</title>
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<g id="node228" class="node">
<title>Node227</title>
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+<text text-anchor="middle" x="293.5" y="-4287" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::BuilderInput</text>
</a>
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<title>Node0->Node227</title>
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<g id="node229" class="node">
<title>Node228</title>
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+<text text-anchor="start" x="243.5" y="-4249" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4238" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::BuilderResult</text>
</a>
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<title>Node0->Node228</title>
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<!-- Node229 -->
<g id="node230" class="node">
<title>Node229</title>
-<g id="a_node230"><a xlink:href="classtvm_1_1meta__schedule_1_1Database.html" target="_top" xlink:title="Managed reference to DatabaseNode. ">
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-<text text-anchor="middle" x="293.5" y="-4140" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Database</text>
+<g id="a_node230"><a xlink:href="classtvm_1_1meta__schedule_1_1CostModel.html" target="_top" xlink:title="Managed reference to CostModelNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-4182 235.5,-4212 351.5,-4212 351.5,-4182 235.5,-4182"/>
+<text text-anchor="start" x="243.5" y="-4200" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4189" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::CostModel</text>
</a>
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<g id="edge229" class="edge">
<title>Node0->Node229</title>
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<!-- Node230 -->
<g id="node231" class="node">
<title>Node230</title>
-<g id="a_node231"><a xlink:href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_top" xlink:title="Managed reference to ExtractedTaskNode. ">
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-<text text-anchor="start" x="243.5" y="-4102" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-4091" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ExtractedTask</text>
+<g id="a_node231"><a xlink:href="classtvm_1_1meta__schedule_1_1Database.html" target="_top" xlink:title="Managed reference to DatabaseNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-4133 235.5,-4163 351.5,-4163 351.5,-4133 235.5,-4133"/>
+<text text-anchor="start" x="243.5" y="-4151" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4140" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Database</text>
</a>
</g>
</g>
<!-- Node0->Node230 -->
<g id="edge230" class="edge">
<title>Node0->Node230</title>
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+<path fill="none" stroke="#191970" d="M67.611,-5170.3456C71.98,-5035.4814 100.3247,-4249.9229 170,-4173 186.3013,-4155.003 211.6848,-4147.9127 235.2098,-4145.6544"/>
+<polygon fill="#191970" stroke="#191970" points="64.1095,-5170.3396 67.2896,-5180.4459 71.1059,-5170.5623 64.1095,-5170.3396"/>
</g>
<!-- Node231 -->
<g id="node232" class="node">
<title>Node231</title>
-<g id="a_node232"><a xlink:href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html" target="_top" xlink:title="Managed reference to FeatureExtractorNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-4035 235.5,-4065 351.5,-4065 351.5,-4035 235.5,-4035"/>
-<text text-anchor="start" x="243.5" y="-4053" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-4042" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::FeatureExtractor</text>
+<g id="a_node232"><a xlink:href="classtvm_1_1meta__schedule_1_1ExtractedTask.html" target="_top" xlink:title="Managed reference to ExtractedTaskNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-4084 235.5,-4114 351.5,-4114 351.5,-4084 235.5,-4084"/>
+<text text-anchor="start" x="243.5" y="-4102" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4091" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ExtractedTask</text>
</a>
</g>
</g>
<!-- Node0->Node231 -->
<g id="edge231" class="edge">
<title>Node0->Node231</title>
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-<polygon fill="#191970" stroke="#191970" points="63.9691,-5146.3184 67.2221,-5156.4014 70.967,-5146.4905 63.9691,-5146.3184"/>
+<path fill="none" stroke="#191970" d="M67.5174,-5170.26C71.334,-5031.4287 97.038,-4204.9255 170,-4124 186.3331,-4105.8842 211.8666,-4098.8065 235.4877,-4096.5882"/>
+<polygon fill="#191970" stroke="#191970" points="64.0174,-5170.2157 67.2468,-5180.3064 71.0148,-5170.4042 64.0174,-5170.2157"/>
</g>
<!-- Node232 -->
<g id="node233" class="node">
<title>Node232</title>
-<g id="a_node233"><a xlink:href="classtvm_1_1meta__schedule_1_1MeasureCallback.html" target="_top" xlink:title="Managed reference to MeasureCallbackNode. ">
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-<text text-anchor="start" x="243.5" y="-4004" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3993" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::MeasureCallback</text>
+<g id="a_node233"><a xlink:href="classtvm_1_1meta__schedule_1_1FeatureExtractor.html" target="_top" xlink:title="Managed reference to FeatureExtractorNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-4035 235.5,-4065 351.5,-4065 351.5,-4035 235.5,-4035"/>
+<text text-anchor="start" x="243.5" y="-4053" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-4042" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::FeatureExtractor</text>
</a>
</g>
</g>
<!-- Node0->Node232 -->
<g id="edge232" class="edge">
<title>Node0->Node232</title>
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-<polygon fill="#191970" stroke="#191970" points="63.8873,-5146.2333 67.1833,-5156.3024 70.8859,-5146.3756 63.8873,-5146.2333"/>
+<path fill="none" stroke="#191970" d="M67.4399,-5169.8048C70.7556,-5025.9553 93.8139,-4159.86 170,-4075 186.2949,-4056.8499 211.8235,-4049.7677 235.4512,-4047.5554"/>
+<polygon fill="#191970" stroke="#191970" points="63.9321,-5170.1147 67.2063,-5180.1909 70.9304,-5170.2722 63.9321,-5170.1147"/>
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<!-- Node233 -->
<g id="node234" class="node">
<title>Node233</title>
-<g id="a_node234"><a xlink:href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_top" xlink:title="Managed reference to MeasureCandidateNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3937 235.5,-3967 351.5,-3967 351.5,-3937 235.5,-3937"/>
-<text text-anchor="start" x="243.5" y="-3955" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3944" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::MeasureCandidate</text>
+<g id="a_node234"><a xlink:href="classtvm_1_1meta__schedule_1_1MeasureCallback.html" target="_top" xlink:title="Managed reference to MeasureCallbackNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3986 235.5,-4016 351.5,-4016 351.5,-3986 235.5,-3986"/>
+<text text-anchor="start" x="243.5" y="-4004" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3993" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::MeasureCallback</text>
</a>
</g>
</g>
<!-- Node0->Node233 -->
<g id="edge233" class="edge">
<title>Node0->Node233</title>
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-<polygon fill="#191970" stroke="#191970" points="63.8115,-5146.1701 67.1467,-5156.2262 70.8106,-5146.2851 63.8115,-5146.1701"/>
+<path fill="none" stroke="#191970" d="M67.3483,-5170.2852C70.1051,-5024.3408 90.4202,-4114.9846 170,-4026 186.2599,-4007.8185 211.7839,-4000.7323 235.4177,-3998.5253"/>
+<polygon fill="#191970" stroke="#191970" points="63.8463,-5170.3686 67.1621,-5180.4312 70.8451,-5170.4971 63.8463,-5170.3686"/>
</g>
<!-- Node234 -->
<g id="node235" class="node">
<title>Node234</title>
-<g id="a_node235"><a xlink:href="classtvm_1_1meta__schedule_1_1Mutator.html" target="_top" xlink:title="Managed reference to MutatorNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3888 235.5,-3918 351.5,-3918 351.5,-3888 235.5,-3888"/>
-<text text-anchor="start" x="243.5" y="-3906" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3895" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Mutator</text>
-</a>
-</g>
+<g id="a_node235"><a xlink:href="classtvm_1_1meta__schedule_1_1MeasureCandidate.html" target="_top" xlink:title="Managed reference to MeasureCandidateNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3937 235.5,-3967 351.5,-3967 351.5,-3937 235.5,-3937"/>
+<text text-anchor="start" x="243.5" y="-3955" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3944" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::MeasureCandidate</text>
+</a>
+</g>
</g>
<!-- Node0->Node234 -->
<g id="edge234" class="edge">
<title>Node0->Node234</title>
-<path fill="none" stroke="#191970" d="M67.2428,-5145.9742C69.2699,-4993.7836 85.4759,-4023.0141 170,-3928 186.2121,-3909.7759 211.73,-3902.6842 235.3721,-3900.4846"/>
-<polygon fill="#191970" stroke="#191970" points="63.7407,-5146.1281 67.1123,-5156.1721 70.7401,-5146.2178 63.7407,-5146.1281"/>
+<path fill="none" stroke="#191970" d="M67.2797,-5169.9268C69.5595,-5019.3469 87.1747,-4069.945 170,-3977 186.2276,-3958.7897 211.7475,-3951.6998 235.3869,-3949.4978"/>
+<polygon fill="#191970" stroke="#191970" points="63.7736,-5170.32 67.1272,-5180.37 70.7729,-5170.4222 63.7736,-5170.32"/>
</g>
<!-- Node235 -->
<g id="node236" class="node">
<title>Node235</title>
-<g id="a_node236"><a xlink:href="classtvm_1_1meta__schedule_1_1Postproc.html" target="_top" xlink:title="Managed reference to PostprocNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3839 235.5,-3869 351.5,-3869 351.5,-3839 235.5,-3839"/>
-<text text-anchor="start" x="243.5" y="-3857" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3846" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Postproc</text>
+<g id="a_node236"><a xlink:href="classtvm_1_1meta__schedule_1_1Mutator.html" target="_top" xlink:title="Managed reference to MutatorNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3888 235.5,-3918 351.5,-3918 351.5,-3888 235.5,-3888"/>
+<text text-anchor="start" x="243.5" y="-3906" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3895" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Mutator</text>
</a>
</g>
</g>
<!-- Node0->Node235 -->
<g id="edge235" class="edge">
<title>Node0->Node235</title>
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-<polygon fill="#191970" stroke="#191970" points="63.6714,-5146.456 67.0769,-5156.4886 70.6711,-5146.5221 63.6714,-5146.456"/>
+<path fill="none" stroke="#191970" d="M67.2084,-5170.0651C68.9951,-5016.4991 83.8356,-4025.0113 170,-3928 186.1978,-3909.7632 211.7138,-3902.6698 235.3584,-3900.4724"/>
+<polygon fill="#191970" stroke="#191970" points="63.7058,-5170.2921 67.0944,-5180.3305 70.7054,-5170.37 63.7058,-5170.2921"/>
</g>
<!-- Node236 -->
<g id="node237" class="node">
<title>Node236</title>
-<g id="a_node237"><a xlink:href="classtvm_1_1meta__schedule_1_1Runner.html" target="_top" xlink:title="Managed reference to RunnerNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3790 235.5,-3820 351.5,-3820 351.5,-3790 235.5,-3790"/>
-<text text-anchor="start" x="243.5" y="-3808" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3797" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Runner</text>
+<g id="a_node237"><a xlink:href="classtvm_1_1meta__schedule_1_1Postproc.html" target="_top" xlink:title="Managed reference to PostprocNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3839 235.5,-3869 351.5,-3869 351.5,-3839 235.5,-3839"/>
+<text text-anchor="start" x="243.5" y="-3857" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3846" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Postproc</text>
</a>
</g>
</g>
<!-- Node0->Node236 -->
<g id="edge236" class="edge">
<title>Node0->Node236</title>
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-<polygon fill="#191970" stroke="#191970" points="63.611,-5146.4616 67.0477,-5156.4836 70.6108,-5146.5059 63.611,-5146.4616"/>
+<path fill="none" stroke="#191970" d="M67.1428,-5170.2561C68.4529,-5013.9754 80.4828,-3980.0944 170,-3879 186.1701,-3860.7386 211.6826,-3853.6421 235.3319,-3851.449"/>
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</g>
<!-- Node237 -->
<g id="node238" class="node">
<title>Node237</title>
-<g id="a_node238"><a xlink:href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_top" xlink:title="Managed reference to RunnerFutureNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3741 235.5,-3771 351.5,-3771 351.5,-3741 235.5,-3741"/>
-<text text-anchor="start" x="243.5" y="-3759" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3748" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::RunnerFuture</text>
+<g id="a_node238"><a xlink:href="classtvm_1_1meta__schedule_1_1Runner.html" target="_top" xlink:title="Managed reference to RunnerNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3790 235.5,-3820 351.5,-3820 351.5,-3790 235.5,-3790"/>
+<text text-anchor="start" x="243.5" y="-3808" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3797" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Runner</text>
</a>
</g>
</g>
<!-- Node0->Node237 -->
<g id="edge237" class="edge">
<title>Node0->Node237</title>
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-<polygon fill="#191970" stroke="#191970" points="63.5549,-5146.486 67.0204,-5156.498 70.5549,-5146.5102 63.5549,-5146.486"/>
+<path fill="none" stroke="#191970" d="M67.0853,-5170.0184C67.9516,-5009.6249 77.2092,-3935.0889 170,-3830 186.1444,-3811.7159 211.6536,-3804.6164 235.3074,-3802.4273"/>
+<polygon fill="#191970" stroke="#191970" points="63.584,-5170.296 67.0348,-5180.313 70.5839,-5170.3303 63.584,-5170.296"/>
</g>
<!-- Node238 -->
<g id="node239" class="node">
<title>Node238</title>
-<g id="a_node239"><a xlink:href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_top" xlink:title="Managed reference to RunnerInputNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3692 235.5,-3722 351.5,-3722 351.5,-3692 235.5,-3692"/>
-<text text-anchor="start" x="243.5" y="-3710" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3699" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::RunnerInput</text>
+<g id="a_node239"><a xlink:href="classtvm_1_1meta__schedule_1_1RunnerFuture.html" target="_top" xlink:title="Managed reference to RunnerFutureNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3741 235.5,-3771 351.5,-3771 351.5,-3741 235.5,-3741"/>
+<text text-anchor="start" x="243.5" y="-3759" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3748" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::RunnerFuture</text>
</a>
</g>
</g>
<!-- Node0->Node238 -->
<g id="edge238" class="edge">
<title>Node0->Node238</title>
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+<path fill="none" stroke="#191970" d="M67.7779,-5169.9485C74.374,-5002.0912 121.8667,-3835.6567 170,-3781 186.1204,-3762.6948 211.6265,-3755.5926 235.2845,-3753.4071"/>
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<g id="node240" class="node">
<title>Node239</title>
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-<text text-anchor="start" x="243.5" y="-3661" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3650" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::RunnerResult</text>
+<g id="a_node240"><a xlink:href="classtvm_1_1meta__schedule_1_1RunnerInput.html" target="_top" xlink:title="Managed reference to RunnerInputNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3692 235.5,-3722 351.5,-3722 351.5,-3692 235.5,-3692"/>
+<text text-anchor="start" x="243.5" y="-3710" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3699" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::RunnerInput</text>
</a>
</g>
</g>
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<g id="edge239" class="edge">
<title>Node0->Node239</title>
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+<path fill="none" stroke="#191970" d="M67.7147,-5170.2963C73.9568,-5000.4657 120.1698,-3788.7232 170,-3732 186.098,-3713.6751 211.6013,-3706.5704 235.2631,-3704.3883"/>
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<g id="node241" class="node">
<title>Node240</title>
-<g id="a_node241"><a xlink:href="classtvm_1_1meta__schedule_1_1ScheduleRule.html" target="_top" xlink:title="Managed reference to ScheduleRuleNode. ">
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-<text text-anchor="start" x="243.5" y="-3612" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3601" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ScheduleRule</text>
+<g id="a_node241"><a xlink:href="classtvm_1_1meta__schedule_1_1RunnerResult.html" target="_top" xlink:title="Managed reference to RunnerResultNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3643 235.5,-3673 351.5,-3673 351.5,-3643 235.5,-3643"/>
+<text text-anchor="start" x="243.5" y="-3661" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3650" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::RunnerResult</text>
</a>
</g>
</g>
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<g id="edge240" class="edge">
<title>Node0->Node240</title>
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+<path fill="none" stroke="#191970" d="M67.6725,-5170.1721C73.6402,-4996.6758 118.5176,-3741.7393 170,-3683 186.0771,-3664.6567 211.5776,-3657.5496 235.243,-3655.3707"/>
+<polygon fill="#191970" stroke="#191970" points="64.167,-5170.2772 67.3232,-5180.391 71.1629,-5170.5164 64.167,-5170.2772"/>
</g>
<!-- Node241 -->
<g id="node242" class="node">
<title>Node241</title>
-<g id="a_node242"><a xlink:href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_top" xlink:title="Managed reference to SearchStrategyNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3545 235.5,-3575 351.5,-3575 351.5,-3545 235.5,-3545"/>
-<text text-anchor="start" x="243.5" y="-3563" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3552" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::SearchStrategy</text>
+<g id="a_node242"><a xlink:href="classtvm_1_1meta__schedule_1_1ScheduleRule.html" target="_top" xlink:title="Managed reference to ScheduleRuleNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3594 235.5,-3624 351.5,-3624 351.5,-3594 235.5,-3594"/>
+<text text-anchor="start" x="243.5" y="-3612" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3601" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::ScheduleRule</text>
</a>
</g>
</g>
<!-- Node0->Node241 -->
<g id="edge241" class="edge">
<title>Node0->Node241</title>
-<path fill="none" stroke="#191970" d="M67.6038,-5146.2916C73.1319,-4968.5316 115.9901,-3646.8215 170,-3585 186.0479,-3566.6311 211.5446,-3559.5207 235.2151,-3557.3462"/>
-<polygon fill="#191970" stroke="#191970" points="64.1045,-5146.2172 67.2939,-5156.3205 71.1012,-5146.4334 64.1045,-5146.2172"/>
+<path fill="none" stroke="#191970" d="M67.6234,-5170.3402C73.2857,-4994.353 116.8353,-3694.79 170,-3634 186.0574,-3615.6395 211.5554,-3608.5302 235.2242,-3606.3542"/>
+<polygon fill="#191970" stroke="#191970" points="64.1208,-5170.3666 67.2994,-5180.4734 71.1172,-5170.5904 64.1208,-5170.3666"/>
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<!-- Node242 -->
<g id="node243" class="node">
<title>Node242</title>
-<g id="a_node243"><a xlink:href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_top" xlink:title="Managed reference to SpaceGeneratorNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3496 235.5,-3526 351.5,-3526 351.5,-3496 235.5,-3496"/>
-<text text-anchor="start" x="243.5" y="-3514" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3503" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::SpaceGenerator</text>
+<g id="a_node243"><a xlink:href="classtvm_1_1meta__schedule_1_1SearchStrategy.html" target="_top" xlink:title="Managed reference to SearchStrategyNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3545 235.5,-3575 351.5,-3575 351.5,-3545 235.5,-3545"/>
+<text text-anchor="start" x="243.5" y="-3563" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3552" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::SearchStrategy</text>
</a>
</g>
</g>
<!-- Node0->Node242 -->
<g id="edge242" class="edge">
<title>Node0->Node242</title>
-<path fill="none" stroke="#191970" d="M67.5656,-5146.241C72.8308,-4965.2114 114.3283,-3599.8491 170,-3536 186.0299,-3517.6155 211.5244,-3510.5031 235.198,-3508.3313"/>
-<polygon fill="#191970" stroke="#191970" points="64.0617,-5146.3291 67.2715,-5156.426 71.0588,-5146.5313 64.0617,-5146.3291"/>
+<path fill="none" stroke="#191970" d="M67.5931,-5169.9927C73.0261,-4989.537 115.2041,-3647.7826 170,-3585 186.0389,-3566.6233 211.5345,-3559.5119 235.2066,-3557.3388"/>
+<polygon fill="#191970" stroke="#191970" points="64.0892,-5170.0735 67.2886,-5180.1737 71.0861,-5170.2828 64.0892,-5170.0735"/>
</g>
<!-- Node243 -->
<g id="node244" class="node">
<title>Node243</title>
-<g id="a_node244"><a xlink:href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_top" xlink:title="Managed reference to TaskSchedulerNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3447 235.5,-3477 351.5,-3477 351.5,-3447 235.5,-3447"/>
-<text text-anchor="start" x="243.5" y="-3465" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3454" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TaskScheduler</text>
+<g id="a_node244"><a xlink:href="classtvm_1_1meta__schedule_1_1SpaceGenerator.html" target="_top" xlink:title="Managed reference to SpaceGeneratorNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3496 235.5,-3526 351.5,-3526 351.5,-3496 235.5,-3496"/>
+<text text-anchor="start" x="243.5" y="-3514" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3503" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::SpaceGenerator</text>
</a>
</g>
</g>
<!-- Node0->Node243 -->
<g id="edge243" class="edge">
<title>Node0->Node243</title>
-<path fill="none" stroke="#191970" d="M67.5368,-5145.9258C72.5774,-4960.5853 112.6931,-3552.8465 170,-3487 186.013,-3468.6008 211.5053,-3461.4865 235.1818,-3459.3172"/>
-<polygon fill="#191970" stroke="#191970" points="64.0324,-5146.0476 67.2611,-5156.1385 71.0298,-5146.2366 64.0324,-5146.0476"/>
+<path fill="none" stroke="#191970" d="M67.5475,-5170.2245C72.6856,-4987.6409 113.5132,-3600.8437 170,-3536 186.0215,-3517.6082 211.5149,-3510.4948 235.1899,-3508.3243"/>
+<polygon fill="#191970" stroke="#191970" points="64.0472,-5170.1913 67.2664,-5180.2852 71.0444,-5170.3869 64.0472,-5170.1913"/>
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<!-- Node244 -->
<g id="node245" class="node">
<title>Node244</title>
-<g id="a_node245"><a xlink:href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_top" xlink:title="Managed reference to TuneContextNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3398 235.5,-3428 351.5,-3428 351.5,-3398 235.5,-3398"/>
-<text text-anchor="start" x="243.5" y="-3416" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3405" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TuneContext</text>
+<g id="a_node245"><a xlink:href="classtvm_1_1meta__schedule_1_1TaskScheduler.html" target="_top" xlink:title="Managed reference to TaskSchedulerNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3447 235.5,-3477 351.5,-3477 351.5,-3447 235.5,-3447"/>
+<text text-anchor="start" x="243.5" y="-3465" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3454" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TaskScheduler</text>
</a>
</g>
</g>
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<g id="edge244" class="edge">
<title>Node0->Node244</title>
-<path fill="none" stroke="#191970" d="M67.4939,-5146.2032C72.2476,-4959.0119 110.9959,-3505.9154 170,-3438 186.0688,-3419.5043 211.7161,-3412.4072 235.485,-3410.2751"/>
-<polygon fill="#191970" stroke="#191970" points="63.9933,-5146.1868 67.2402,-5156.2719 70.9911,-5146.3633 63.9933,-5146.1868"/>
+<path fill="none" stroke="#191970" d="M67.5115,-5170.2125C72.3929,-4984.5762 111.8463,-3553.8776 170,-3487 186.0769,-3468.5113 211.7252,-3461.4151 235.4927,-3459.2818"/>
+<polygon fill="#191970" stroke="#191970" points="64.0075,-5170.3245 67.2453,-5180.4125 71.0051,-5170.5072 64.0075,-5170.3245"/>
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<!-- Node245 -->
<g id="node246" class="node">
<title>Node245</title>
-<g id="a_node246"><a xlink:href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_top" xlink:title="The managed reference of TuningRecordNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3349 235.5,-3379 351.5,-3379 351.5,-3349 235.5,-3349"/>
-<text text-anchor="start" x="243.5" y="-3367" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3356" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TuningRecord</text>
+<g id="a_node246"><a xlink:href="classtvm_1_1meta__schedule_1_1TuneContext.html" target="_top" xlink:title="Managed reference to TuneContextNode. ">
+<polygon fill="#ffffff" stroke="#000000" points="235.5,-3398 235.5,-3428 351.5,-3428 351.5,-3398 235.5,-3398"/>
+<text text-anchor="start" x="243.5" y="-3416" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
+<text text-anchor="middle" x="293.5" y="-3405" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::TuneContext</text>
</a>
</g>
</g>
<!-- Node0->Node245 -->
<g id="edge245" class="edge">
<title>Node0->Node245</title>
-<path fill="none" stroke="#191970" d="M67.4599,-5146.2288C71.9635,-4956.2052 109.3238,-3458.9556 170,-3389 186.0537,-3370.4912 211.6991,-3363.3924 235.4706,-3361.2626"/>
-<polygon fill="#191970" stroke="#191970" points="63.9564,-5146.3407 67.2204,-5156.4202 70.9545,-5146.5053 63.9564,-5146.3407"/>
+<path fill="none" stroke="#191970" d="M67.4841,-5169.9285C72.1459,-4980.1397 110.2073,-3506.8797 170,-3438 186.0613,-3419.4978 211.7076,-3412.3999 235.4779,-3410.2689"/>
+<polygon fill="#191970" stroke="#191970" points="63.9801,-5170.0546 67.2353,-5180.1369 70.978,-5170.2252 63.9801,-5170.0546"/>
</g>
<!-- Node246 -->
<g id="node247" class="node">
<title>Node246</title>
-<g id="a_node247"><a xlink:href="classtvm_1_1meta__schedule_1_1Workload.html" target="_top" xlink:title="Managed reference to WorkloadNode. ">
-<polygon fill="#ffffff" stroke="#000000" points="235.5,-3300 235.5,-3330 351.5,-3330 351.5,-3300 235.5,-3300"/>
-<text text-anchor="start" x="243.5" y="-3318" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">tvm::meta_schedule</text>
-<text text-anchor="middle" x="293.5" y="-3307" font-family="Helvetica,sans-Serif" font-size="10.00" fill="#000000">::Workload</text>
+<g id="a_node247"><a xlink:href="classtvm_1_1meta__schedule_1_1TuningRecord.html" target="_top" xlink:title="The managed reference of TuningRecordNode. ">
... 8627 lines suppressed ...