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Posted to commits@tvm.apache.org by lm...@apache.org on 2021/06/10 09:15:04 UTC
[tvm-site] branch asf-site updated: Docs build at Thu Jun 10
02:13:37 PDT 2021
This is an automated email from the ASF dual-hosted git repository.
lmzheng pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 079bb3a Docs build at Thu Jun 10 02:13:37 PDT 2021
079bb3a is described below
commit 079bb3a9f910c6b4365b1cf13f67909996beb83c
Author: Lianmin Zheng <li...@gmail.com>
AuthorDate: Thu Jun 10 02:13:39 2021 -0700
Docs build at Thu Jun 10 02:13:37 PDT 2021
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878 files changed, 132084 insertions(+), 126683 deletions(-)
diff --git a/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py b/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
index fffbfbf..d9174da 100644
--- a/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
+++ b/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
@@ -494,5 +494,5 @@ capabilities, and set the stage for understanding how TVM works.
# --help``.
#
# In the next tutorial, `Compiling and Optimizing a Model with the Python
-# AutoScheduler <auto_tuning_with_pyton>`_, we will cover the same compilation
+# Interface <auto_tuning_with_pyton>`_, we will cover the same compilation
# and optimization steps using the Python interface.
diff --git a/docs/_downloads/5605e45978c6b29c0434162ad6899d08/autotvm_matmul.ipynb b/docs/_downloads/1c56ea64f39bea4084f04ec0959030ed/autotvm_matmul_x86.ipynb
similarity index 91%
rename from docs/_downloads/5605e45978c6b29c0434162ad6899d08/autotvm_matmul.ipynb
rename to docs/_downloads/1c56ea64f39bea4084f04ec0959030ed/autotvm_matmul_x86.ipynb
index 4db0d8e..049b3d7 100644
--- a/docs/_downloads/5605e45978c6b29c0434162ad6899d08/autotvm_matmul.ipynb
+++ b/docs/_downloads/1c56ea64f39bea4084f04ec0959030ed/autotvm_matmul_x86.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nOptimizing Operators with 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 will now show how the TVM Template Extension (TE) language\ncan be used to write scheduling templates that can be searched by AutoTVM to\nfind optimal configurations of scheduling variables. This process is called\nAuto-Tuning, and builds on TE to h [...]
+ "\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 [...]
]
},
{
@@ -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 upcom [...]
+ "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 [...]
]
}
],
diff --git a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
index e8d0fee..f5a049b 100644
--- a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
+++ b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/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/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py b/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
index 4e80a74..ef92156 100644
--- a/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
+++ b/docs/_downloads/2c8ef0390ad4c53ca85671fa36c33b26/tune_conv2d_cuda.py
@@ -77,7 +77,7 @@ from tvm import autotvm
# 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 :doc:`autotvm tutorial <tune_simple_template>`
+# tutorials and :ref:`autotvm tutorial <tutorial-autotvm-matmul-x86>`
#
# It is worth noting that the search space for a conv2d operator
# can be very large (at the level of 10^9 for some input shapes)
diff --git a/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py b/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py
index ee13d9e..8fbdb75 100644
--- a/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py
+++ b/docs/_downloads/37c76200603adf82ebeffc23bdef8d31/tensor_expr_get_started.py
@@ -17,22 +17,19 @@
"""
.. _tutorial-tensor-expr-get-started:
-Working with Operators Using Tensor Expressions
-===============================================
+Working with Operators Using Tensor Expression
+==============================================
**Author**: `Tianqi Chen <https://tqchen.github.io>`_
In this tutorial we will turn our attention to how TVM works with Tensor
-Expressions (TE) to create a space to search for performant configurations. TE
+Expression (TE) to define tensor computations and apply loop optimizations. TE
describes tensor computations in a pure functional language (that is each
expression has no side effects). When viewed in context of the TVM as a whole,
Relay describes a computation as a set of operators, and each of these
operators can be represented as a TE expression where each TE expression takes
-input tensors and produces an output tensor. It's important to note that the
-tensor isn't necessarily a fully materialized array, rather it is a
-representation of a computation. If you want to produce a computation from a
-TE, you will need to use the scheduling features of TVM.
+input tensors and produces an output tensor.
-This is an introductory tutorial to the Tensor expression language in TVM. TVM
+This is an introductory tutorial to the Tensor Expression language in TVM. TVM
uses a domain specific tensor expression for efficient kernel construction. We
will demonstrate the basic workflow with two examples of using the tensor expression
language. The first example introduces TE and scheduling with vector
@@ -47,8 +44,8 @@ features of TVM.
# ---------------------------------------------------------------
#
# Let's look at an example in Python in which we will implement a TE for
-# vector addition, followed by a schedule targeted towards a CPU. We begin by initializing a TVM
-# environment.
+# vector addition, followed by a schedule targeted towards a CPU.
+# We begin by initializing a TVM environment.
import tvm
import tvm.testing
@@ -59,7 +56,8 @@ import numpy as np
# and specify it. If you're using llvm, you can get this information from the
# command ``llc --version`` to get the CPU type, and you can check
# ``/proc/cpuinfo`` for additional extensions that your processor might
-# support. For example, ``tgt = "llvm -mcpu=`skylake`
+# support. For example, you can use "llvm -mcpu=skylake-avx512" for CPUs with
+# AVX-512 instructions.
tgt = tvm.target.Target(target="llvm", host="llvm")
@@ -69,7 +67,7 @@ tgt = tvm.target.Target(target="llvm", host="llvm")
# We describe a vector addition computation. TVM adopts tensor semantics, with
# each intermediate result represented as a multi-dimensional array. The user
# needs to describe the computation rule that generates the tensors. We first
-# define a symbolic variable n to represent the shape. We then define two
+# define a symbolic variable ``n`` to represent the shape. We then define two
# placeholder Tensors, ``A`` and ``B``, with given shape ``(n,)``. We then
# describe the result tensor ``C``, with a ``compute`` operation. The
# ``compute`` defines a computation, with the output conforming to the
@@ -79,7 +77,6 @@ tgt = tvm.target.Target(target="llvm", host="llvm")
# tensors. Remember, no actual computation happens during this phase, as we
# are only declaring how the computation should be done.
-
n = te.var("n")
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
@@ -88,10 +85,10 @@ C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
################################################################################
# .. note:: Lambda Functions
#
-# The second argument to the ``te.compute`` method is the function that
-# performs the computation. In this example, we're using an anonymous function,
-# also known as a ``lambda`` function, to define the computation, in this case
-# addition on the ``i``th element of ``A`` and ``B``.
+# The second argument to the ``te.compute`` method is the function that
+# performs the computation. In this example, we're using an anonymous function,
+# also known as a ``lambda`` function, to define the computation, in this case
+# addition on the ``i``th element of ``A`` and ``B``.
################################################################################
# Create a Default Schedule for the Computation
@@ -322,8 +319,6 @@ if run_cuda:
bx, tx = s[C].split(C.op.axis[0], factor=64)
- xXXXXXXXx
-
################################################################################
# Finally we must bind the iteration axis bx and tx to threads in the GPU
# compute grid. The naive schedule is not valid for GPUs, and these are
diff --git a/docs/_downloads/4c010cd30d80efae3f2dfe5743a098a5/install.ipynb b/docs/_downloads/4c010cd30d80efae3f2dfe5743a098a5/install.ipynb
index c388946..f54e469 100644
--- a/docs/_downloads/4c010cd30d80efae3f2dfe5743a098a5/install.ipynb
+++ b/docs/_downloads/4c010cd30d80efae3f2dfe5743a098a5/install.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nInstalling TVM\n==============\n**Authors**:\n`Jocelyn Shiue <https://github.com/>`_,\n`Chris Hoge <https://github.com/hogepodge>`_\n\nDepending on your needs and your working environment, there are a few different\nmethods for installing TVM. These include:\n * Installing from source \n * Installing from third-party binary package.\n\n"
+ "\nInstalling TVM\n==============\n**Authors**:\n`Jocelyn Shiue <https://github.com/>`_,\n`Chris Hoge <https://github.com/hogepodge>`_\n\nDepending on your needs and your working environment, there are a few different\nmethods for installing TVM. These include:\n* Installing from source\n* Installing from third-party binary package.\n\n"
]
},
{
diff --git a/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb b/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb
index d840260..0c845d6 100644
--- a/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb
+++ b/docs/_downloads/70a0767409e81bb5aaa9ce4e7a151dec/tensor_expr_get_started.ipynb
@@ -15,14 +15,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nWorking with Operators Using Tensor Expressions\n===============================================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nIn this tutorial we will turn our attention to how TVM works with Tensor\nExpressions (TE) to create a space to search for performant configurations. TE\ndescribes tensor computations in a pure functional language (that is each\nexpression has no side effects). When viewed in context of the TVM as a whole,\nRelay describes [...]
+ "\n\nWorking with Operators Using Tensor Expression\n==============================================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nIn this tutorial we will turn our attention to how TVM works with Tensor\nExpression (TE) to define tensor computations and apply loop optimizations. TE\ndescribes tensor computations in a pure functional language (that is each\nexpression has no side effects). When viewed in context of the TVM as a whole,\nRelay describes a [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Example 1: Writing and Scheduling Vector Addition in TE for CPU\n---------------------------------------------------------------\n\nLet's look at an example in Python in which we will implement a TE for\nvector addition, followed by a schedule targeted towards a CPU. We begin by initializing a TVM\nenvironment.\n\n"
+ "Example 1: Writing and Scheduling Vector Addition in TE for CPU\n---------------------------------------------------------------\n\nLet's look at an example in Python in which we will implement a TE for\nvector addition, followed by a schedule targeted towards a CPU.\nWe begin by initializing a TVM environment.\n\n"
]
},
{
@@ -33,14 +33,14 @@
},
"outputs": [],
"source": [
- "import tvm\nimport tvm.testing\nfrom tvm import te\nimport numpy as np\n\n# You will get better performance if you can identify the CPU you are targeting\n# and specify it. If you're using llvm, you can get this information from the\n# command ``llc --version`` to get the CPU type, and you can check\n# ``/proc/cpuinfo`` for additional extensions that your processor might\n# support. For example, ``tgt = \"llvm -mcpu=`skylake`\n\ntgt = tvm.target.Target(target=\"llvm\", host=\"llvm\")"
+ "import tvm\nimport tvm.testing\nfrom tvm import te\nimport numpy as np\n\n# You will get better performance if you can identify the CPU you are targeting\n# and specify it. If you're using llvm, you can get this information from the\n# command ``llc --version`` to get the CPU type, and you can check\n# ``/proc/cpuinfo`` for additional extensions that your processor might\n# support. For example, you can use \"llvm -mcpu=skylake-avx512\" for CPUs with\n# AVX-512 instructions.\n\n [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Describing the Vector Computation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nWe describe a vector addition computation. TVM adopts tensor semantics, with\neach intermediate result represented as a multi-dimensional array. The user\nneeds to describe the computation rule that generates the tensors. We first\ndefine a symbolic variable n to represent the shape. We then define two\nplaceholder Tensors, ``A`` and ``B``, with given shape ``(n,)``. We then\ndescribe the result tensor ``C``, [...]
+ "Describing the Vector Computation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nWe describe a vector addition computation. TVM adopts tensor semantics, with\neach intermediate result represented as a multi-dimensional array. The user\nneeds to describe the computation rule that generates the tensors. We first\ndefine a symbolic variable ``n`` to represent the shape. We then define two\nplaceholder Tensors, ``A`` and ``B``, with given shape ``(n,)``. We then\ndescribe the result tensor ``C [...]
]
},
{
@@ -58,7 +58,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>Lambda Functions</p></div>\n\nThe second argument to the ``te.compute`` method is the function that\nperforms the computation. In this example, we're using an anonymous function,\nalso known as a ``lambda`` function, to define the computation, in this case\naddition on the ``i``th element of ``A`` and ``B``.\n\n"
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Lambda Functions\n\n The second argument to the ``te.compute`` method is the function that\n performs the computation. In this example, we're using an anonymous function,\n also known as a ``lambda`` function, to define the computation, in this case\n addition on the ``i``th element of ``A`` and ``B``.</p></div>\n\n"
]
},
{
@@ -252,7 +252,7 @@
},
"outputs": [],
"source": [
- "# If you want to run this code, change ``run_cuda = True``\n# Note that by default this example is not run in the docs CI.\n\nrun_cuda = False\nif run_cuda:\n # Change this target to the correct backend for you gpu. For example: cuda (NVIDIA GPUs),\n # rocm (Radeon GPUS), OpenCL (opencl).\n tgt_gpu = tvm.target.Target(target=\"cuda\", host=\"llvm\")\n\n # Recreate the schedule\n n = te.var(\"n\")\n A = te.placeholder((n,), name=\"A\")\n B = te.placeholder((n [...]
+ "# If you want to run this code, change ``run_cuda = True``\n# Note that by default this example is not run in the docs CI.\n\nrun_cuda = False\nif run_cuda:\n # Change this target to the correct backend for you gpu. For example: cuda (NVIDIA GPUs),\n # rocm (Radeon GPUS), OpenCL (opencl).\n tgt_gpu = tvm.target.Target(target=\"cuda\", host=\"llvm\")\n\n # Recreate the schedule\n n = te.var(\"n\")\n A = te.placeholder((n,), name=\"A\")\n B = te.placeholder((n [...]
]
},
{
diff --git a/docs/_downloads/ba83c0c088bfea1dc14806773e24bee8/auto_tuning_with_python.ipynb b/docs/_downloads/7ba56b899e0cc8ebdb274901ddb3d351/autotvm_relay_x86.ipynb
similarity index 88%
rename from docs/_downloads/ba83c0c088bfea1dc14806773e24bee8/auto_tuning_with_python.ipynb
rename to docs/_downloads/7ba56b899e0cc8ebdb274901ddb3d351/autotvm_relay_x86.ipynb
index 03f36b2..951ff8d 100644
--- a/docs/_downloads/ba83c0c088bfea1dc14806773e24bee8/auto_tuning_with_python.ipynb
+++ b/docs/_downloads/7ba56b899e0cc8ebdb274901ddb3d351/autotvm_relay_x86.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nCompiling and Optimizing a Model with the Python AutoScheduler\n==============================================================\n**Author**:\n`Chris Hoge <https://github.com/hogepodge>`_\n\nIn the `TVMC Tutorial <tvmc_command_line_driver>`_, we covered how to compile, run, and tune a\npre-trained vision model, ResNet-50-v2 using the command line interface for\nTVM, TVMC. TVM is more that just a command-line tool though, it is an\noptimizing framework with APIs available for a n [...]
+ "\nCompiling and Optimizing a Model with the Python Interface (AutoTVM)\n====================================================================\n**Author**:\n`Chris Hoge <https://github.com/hogepodge>`_\n\nIn the `TVMC Tutorial <tvmc_command_line_driver>`_, we covered how to compile, run, and tune a\npre-trained vision model, ResNet-50-v2 using the command line interface for\nTVM, TVMC. TVM is more that just a command-line tool though, it is an\noptimizing framework with APIs avail [...]
]
},
{
@@ -184,7 +184,7 @@
},
"outputs": [],
"source": [
- "import tvm.auto_scheduler as auto_scheduler\nfrom tvm.autotvm.tuner import XGBTuner\nfrom tvm import autotvm\n\n# Set up some basic parameters for the runner. The runner takes compiled code\n# that is generated with a specific set of parameters and measures the\n# performance of it. ``number`` specifies the number of different\n# configurations that we will test, while ``repeat`` specifies how many\n# measurements we will take of each configuration. ``min_repeat_ms`` is a value\ [...]
+ "import tvm.auto_scheduler as auto_scheduler\nfrom tvm.autotvm.tuner import XGBTuner\nfrom tvm import autotvm\n\n# Set up some basic parameters for the runner. The runner takes compiled code\n# that is generated with a specific set of parameters and measures the\n# performance of it. ``number`` specifies the number of different\n# configurations that we will test, while ``repeat`` specifies how many\n# measurements we will take of each configuration. ``min_repeat_ms`` is a value\ [...]
]
},
{
diff --git a/docs/_downloads/95f64205f29091c5f31071f9072f3236/install.py b/docs/_downloads/95f64205f29091c5f31071f9072f3236/install.py
index 6d1db4d..efc951a 100644
--- a/docs/_downloads/95f64205f29091c5f31071f9072f3236/install.py
+++ b/docs/_downloads/95f64205f29091c5f31071f9072f3236/install.py
@@ -23,8 +23,8 @@ Installing TVM
Depending on your needs and your working environment, there are a few different
methods for installing TVM. These include:
- * Installing from source
- * Installing from third-party binary package.
+* Installing from source
+* Installing from third-party binary package.
"""
################################################################################
diff --git a/docs/_downloads/9c47ef19ddae24b555c61973e482829d/introduction.py b/docs/_downloads/9c47ef19ddae24b555c61973e482829d/introduction.py
index 0ee79d3..0746c39 100644
--- a/docs/_downloads/9c47ef19ddae24b555c61973e482829d/introduction.py
+++ b/docs/_downloads/9c47ef19ddae24b555c61973e482829d/introduction.py
@@ -19,7 +19,8 @@ Introduction
============
**Authors**:
`Jocelyn Shiue <https://github.com/>`_,
-`Chris Hoge <https://github.com/hogepodge>`_
+`Chris Hoge <https://github.com/hogepodge>`_,
+`Lianmin Zheng <https://github.com/merrymercy>`_
Apache TVM is an open source machine learning compiler framework for CPUs,
GPUs, and machine learning accelerators. It aims to enable machine learning
@@ -35,11 +36,11 @@ Contents
#. :doc:`Introduction <introduction>`
#. :doc:`Installing TVM <install>`
-#. :doc:`Compiling and Optimizing a Model with TVMC <tvmc_command_line_driver>`
-#. :doc:`Compiling and Optimizing a Model with the Python AutoScheduler <auto_tuning_with_python>`
-#. :doc:`Working with Operators Using Tensor Expressions <tensor_expr_get_started>`
-#. :doc:`Optimizing Operators with Templates and AutoTVM <autotvm_matmul>`
-#. :doc:`Optimizing Operators with AutoScheduling <tune_matmul_x86>`
+#. :doc:`Compiling and Optimizing a Model with the Command Line Interface <tvmc_command_line_driver>`
+#. :doc:`Compiling and Optimizing a Model with the Python Interface <autotvm_relay_x86>`
+#. :doc:`Working with Operators Using Tensor Expression <tensor_expr_get_started>`
+#. :doc:`Optimizing Operators with Templates and AutoTVM <autotvm_matmul_x86>`
+#. :doc:`Optimizing Operators with Template-free AutoScheduler <auto_scheduler_matmul_x86>`
#. :doc:`Cross Compilation and Remote Procedure Calls (RPC) <cross_compilation_and_rpc>`
#. :doc:`Compiling Deep Learning Models for GPUs <relay_quick_start>`
"""
@@ -51,18 +52,18 @@ Contents
# The diagram below illustrates the steps a machine model takes as it is
# transformed with the TVM optimizing compiler framework.
#
-# .. image:: https://raw.githubusercontent.com/hogepodge/web-data/c339ebbbae41f3762873147c1e920a53a08963dd/images/getting_started/overview.png
+# .. image:: https://raw.githubusercontent.com/apache/tvm-site/main/images/tutorial/overview.png
# :width: 100%
# :alt: A High Level View of TVM
#
# 1. Import the model from a framework like *Tensorflow*, *Pytorch*, or *Onnx*.
# The importer layer is where TVM can ingest models from other frameworks, like
-# ONNX, Tensorflow, or PyTorch. The level of support that TVM offers for each
+# Tensorflow, PyTorch, or ONNX. The level of support that TVM offers for each
# frontend varies as we are constantly improving the open source project. If
# you're having issues importing your model into TVM, you may want to try
# converting it to ONNX.
#
-# 2. Translate to *Relay*, TVM's high level model language.
+# 2. Translate to *Relay*, TVM's high-level model language.
# A model that has been imported into TVM is represented in Relay. Relay is a
# functional language and intermediate representation (IR) for neural networks.
# It has support for:
@@ -72,46 +73,47 @@ Contents
# differentiable language
# - Ability to allow the user to mix the two programming styles
#
-# Relay applies several high-level optimization to the model, after which
-# is runs the Relay Fusion Pass. To aid in the process of converting to
-# Relay, TVM includes a Tensor Operator Inventory (TOPI) that has pre-defined
-# templates of common computations.
+# Relay applies graph-level optimization passes to optimize the model.
#
# 3. Lower to *Tensor Expression* (TE) representation. Lowering is when a
# higher-level representation is transformed into a lower-level
-# representation. In Relay Fusion Pass, the model is lowered from the
-# higher-level Relay representation into a smaller set of subgraphs, where
-# each node is a task. A task is a collection of computation templates,
-# expressed in TE, where there parameters of the template can control how
-# the computation is carried out on hardware. The specific ordering of compuation,
-# defined by parameters to the TE template, is called a schedule.
-#
-# 4. Search for optimized schedule using *AutoTVM* or *AutoScheduler* for each
-# task through tuning. Tuning is the process of searching the TE parameter
-# space for a schedule that is optimized for target hardware. There are
-# couple of optimization options available, each requiring varying levels of
-# user interaction. The optimization options include:
-#
-# - **AutoTVM**: The user specifies a search template for the schedule of a TE task,
-# or TE subraph. AutoTVM directs the search of the parameter space defined by the
-# template to produce an optimized configuration. AutoTVM requires users to
-# define manually templates for each operator as part of the TOPI.
-# - **Ansor/AutoSchedule**: Using a TVM Operator Inventory (TOPI) of operations,
-# Ansor can automatically search an optimization space with much less
-# intervention and guidance from the end user. Ansor depends on TE templates to
-# guide the search.
-#
-# 5. Choose the optimal configuration for the model. After tuning, an optimal schedule
-# for each task is chosen. Regardless if it is AutoTVM or AutoSchedule,
-# schedule records in JSON format are produced that are referred to by this step
-# to build an optimized model.
-#
-# 6. Lower to a hardware specific compiler. After selecting an optimized configuration
-# based on the tuning step, the model is then lowered to a representation
-# expected by the target compiler for the hardware platform. This is the
-# final code generation phase with the intention of producing an optimized
-# model that can be deployed into production. TVM supports a number of
-# different compiler backends including:
+# representation. After applying the high-level optimizations, Relay
+# runs FuseOps pass to partition the model into many small subgraphs and lowers
+# the subgraphs to TE representation. Tensor Expression (TE) is a
+# domain-specific language for describing tensor computations.
+# TE also provides several *schedule* primitives to specify low-level loop
+# optimizations, such as tiling, vectorization, parallelization,
+# unrolling, and fusion.
+# To aid in the process of converting Relay representation into TE representation,
+# TVM includes a Tensor Operator Inventory (TOPI) that has pre-defined
+# templates of common tensor operators (e.g., conv2d, transpose).
+#
+# 4. Search for the best schedule using the auto-tuning module *AutoTVM* or *AutoScheduler*.
+# A schedule specifies the low-level loop optimizations for an operator or
+# subgraph defined in TE. Auto-tuning modules search for the best schedule
+# and compare them with cost models and on-device measurements.
+# There are two auto-tuning modules in TVM.
+#
+# - **AutoTVM**: A template-based auto-tuning module. It runs search algorithms
+# to find the best values for the tunable knobs in a user-defined template.
+# For common operators, their templates are already provided in TOPI.
+# - **AutoScheduler (a.k.a. Ansor)**: A template-free auto-tuning module.
+# It does not require pre-defined schedule templates. Instead, it generates
+# the search space automatically by analyzing the computation definition.
+# It then searches for the best schedule in the generated search space.
+#
+# 5. Choose the optimal configurations for model compilation. After tuning, the
+# auto-tuning module generates tuning records in JSON format. This step
+# picks the best schedule for each subgraph.
+#
+# 6. Lower to Tensor Intermediate Representation (TIR), TVM's low-level
+# intermediate representation. After selecting the optimal configurations
+# based on the tuning step, each TE subgraph is lowered to TIR and be
+# optimized by low-level optimization passes. Next, the optimized TIR is
+# lowered to the target compiler of the hardware platform.
+# This is the final code generation phase to produce an optimized model
+# that can be deployed into production. TVM supports several different
+# compiler backends including:
#
# - LLVM, which can target arbitrary microprocessor architecture including
# standard x86 and ARM processors, AMDGPU and NVPTX code generation, and any
diff --git a/docs/_downloads/b78890bb249aab574c50f16eb0be62a9/tune_network_arm.ipynb b/docs/_downloads/b78890bb249aab574c50f16eb0be62a9/tune_network_arm.ipynb
index d0e8355..13f01c9 100644
--- a/docs/_downloads/b78890bb249aab574c50f16eb0be62a9/tune_network_arm.ipynb
+++ b/docs/_downloads/b78890bb249aab574c50f16eb0be62a9/tune_network_arm.ipynb
@@ -133,7 +133,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
+ "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
]
}
],
diff --git a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
index 68d263b..debf8b8 100644
--- a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
+++ b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
@@ -278,6 +278,10 @@ def tune_tasks(
tuner_obj = XGBTuner(tsk, loss_type="rank")
elif tuner == "xgb_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+ elif tuner == "xgb_itervar":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
+ elif tuner == "xgb_curve":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
elif tuner == "ga":
tuner_obj = GATuner(tsk, pop_size=50)
elif tuner == "random":
@@ -291,7 +295,7 @@ def tune_tasks(
if os.path.isfile(tmp_log_file):
tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
- # do tuning
+ # process tuning
tsk_trial = min(n_trial, len(tsk.config_space))
tuner_obj.tune(
n_trial=tsk_trial,
diff --git a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
index 78b0aa6..97471a7 100644
--- a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
+++ b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
@@ -76,7 +76,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Build the inference graph executor\n---------------------------------\nGrab vision model from Gluon model zoo and compile with Relay.\nThe compilation steps are:\n\n1. Front end translation from MxNet into Relay module.\n2. Apply 8-bit quantization: here we skip the first conv layer,\n and dense layer which will both be executed in fp32 on the CPU.\n3. Perform graph packing to alter the data layout for tensorization.\n4. Perform constant folding to reduce number of operators ( [...]
+ "Build the inference graph executor\n----------------------------------\nGrab vision model from Gluon model zoo and compile with Relay.\nThe compilation steps are:\n\n1. Front end translation from MxNet into Relay module.\n2. Apply 8-bit quantization: here we skip the first conv layer,\n and dense layer which will both be executed in fp32 on the CPU.\n3. Perform graph packing to alter the data layout for tensorization.\n4. Perform constant folding to reduce number of operators [...]
]
},
{
diff --git a/docs/_downloads/41a7d69f33d4708ae5b843474adf9c9e/tune_matmul_x86.py b/docs/_downloads/ca1e46b7753fde55e274840c315f6b1a/auto_scheduler_matmul_x86.py
similarity index 99%
rename from docs/_downloads/41a7d69f33d4708ae5b843474adf9c9e/tune_matmul_x86.py
rename to docs/_downloads/ca1e46b7753fde55e274840c315f6b1a/auto_scheduler_matmul_x86.py
index 8156d0e..f9fb361 100644
--- a/docs/_downloads/41a7d69f33d4708ae5b843474adf9c9e/tune_matmul_x86.py
+++ b/docs/_downloads/ca1e46b7753fde55e274840c315f6b1a/auto_scheduler_matmul_x86.py
@@ -23,7 +23,7 @@ Optimizing Operators with Auto-scheduling
In this tutorial, we will show how TVM's Auto Scheduling feature can find
optimal schedules without the need for writing a custom template.
-Different from the template-based :ref:`<autotvm_matmul>` which relies on
+Different from the template-based :doc:`AutoTVM <autotvm_matmul_x86>` which relies on
manual templates to define the search space, the auto-scheduler does not
require any templates. Users only need to write the computation declaration
without any schedule commands or templates. The auto-scheduler can
diff --git a/docs/_downloads/dfa0880631b34bb8814952afdc9031d8/tvmc_command_line_driver.ipynb b/docs/_downloads/dfa0880631b34bb8814952afdc9031d8/tvmc_command_line_driver.ipynb
index e663bf7..e70dd56 100644
--- a/docs/_downloads/dfa0880631b34bb8814952afdc9031d8/tvmc_command_line_driver.ipynb
+++ b/docs/_downloads/dfa0880631b34bb8814952afdc9031d8/tvmc_command_line_driver.ipynb
@@ -106,7 +106,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Final Remarks\n-------------\n\nIn this tutorial, we presented TVMC, a command line driver for TVM. We\ndemonstrated how to compile, run, and tune a model. We also discussed the\nneed for pre and post-processing of inputs and outputs. After the tuning\nprocess, we demonstrated how to compare the performance of the unoptimized\nand optimize models.\n\nHere we presented a simple example using ResNet 50 V2 locally. However, TVMC\nsupports many more features including cross-compilat [...]
+ "Final Remarks\n-------------\n\nIn this tutorial, we presented TVMC, a command line driver for TVM. We\ndemonstrated how to compile, run, and tune a model. We also discussed the\nneed for pre and post-processing of inputs and outputs. After the tuning\nprocess, we demonstrated how to compare the performance of the unoptimized\nand optimize models.\n\nHere we presented a simple example using ResNet 50 V2 locally. However, TVMC\nsupports many more features including cross-compilat [...]
]
}
],
diff --git a/docs/_downloads/ea5735ab0fc6252b8d1a381f756a9290/introduction.ipynb b/docs/_downloads/ea5735ab0fc6252b8d1a381f756a9290/introduction.ipynb
index 1012bfc..cefef7a 100644
--- a/docs/_downloads/ea5735ab0fc6252b8d1a381f756a9290/introduction.ipynb
+++ b/docs/_downloads/ea5735ab0fc6252b8d1a381f756a9290/introduction.ipynb
@@ -15,14 +15,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nIntroduction\n============\n**Authors**:\n`Jocelyn Shiue <https://github.com/>`_,\n`Chris Hoge <https://github.com/hogepodge>`_\n\nApache TVM is an open source machine learning compiler framework for CPUs,\nGPUs, and machine learning accelerators. It aims to enable machine learning\nengineers to optimize and run computations efficiently on any hardware backend.\nThe purpose of this tutorial is to take a guided tour through all of the major\nfeatures of TVM by defining and demo [...]
+ "\nIntroduction\n============\n**Authors**:\n`Jocelyn Shiue <https://github.com/>`_,\n`Chris Hoge <https://github.com/hogepodge>`_,\n`Lianmin Zheng <https://github.com/merrymercy>`_\n\nApache TVM is an open source machine learning compiler framework for CPUs,\nGPUs, and machine learning accelerators. It aims to enable machine learning\nengineers to optimize and run computations efficiently on any hardware backend.\nThe purpose of this tutorial is to take a guided tour through all [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "An Overview of TVM and Model Optimization\n=========================================\n\nThe diagram below illustrates the steps a machine model takes as it is\ntransformed with the TVM optimizing compiler framework.\n\n\n\n :width: 100%\n :alt: A High Level View of TVM\n\n1. Import the model from a framework like *Tensorflow*, *Pytorch*, or *Onn [...]
+ "An Overview of TVM and Model Optimization\n=========================================\n\nThe diagram below illustrates the steps a machine model takes as it is\ntransformed with the TVM optimizing compiler framework.\n\n\n\n :width: 100%\n :alt: A High Level View of TVM\n\n1. Import the model from a framework like *Tensorflow*, *Pytorch*, or *Onnx*.\n The importer layer is where TVM can in [...]
]
}
],
diff --git a/docs/_downloads/1087d6f8ab0d37227d1a26042de7ac88/auto_tuning_with_python.py b/docs/_downloads/eabaa51a209056955e5626fa23b5978e/autotvm_relay_x86.py
similarity index 99%
rename from docs/_downloads/1087d6f8ab0d37227d1a26042de7ac88/auto_tuning_with_python.py
rename to docs/_downloads/eabaa51a209056955e5626fa23b5978e/autotvm_relay_x86.py
index 848511c..67faec4 100644
--- a/docs/_downloads/1087d6f8ab0d37227d1a26042de7ac88/auto_tuning_with_python.py
+++ b/docs/_downloads/eabaa51a209056955e5626fa23b5978e/autotvm_relay_x86.py
@@ -15,8 +15,8 @@
# specific language governing permissions and limitations
# under the License.
"""
-Compiling and Optimizing a Model with the Python AutoScheduler
-==============================================================
+Compiling and Optimizing a Model with the Python Interface (AutoTVM)
+====================================================================
**Author**:
`Chris Hoge <https://github.com/hogepodge>`_
@@ -302,6 +302,7 @@ runner = autotvm.LocalRunner(
repeat=repeat,
timeout=timeout,
min_repeat_ms=min_repeat_ms,
+ enable_cpu_cache_flush=True,
)
# Create a simple structure for holding tuning options. We use an XGBoost
diff --git a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
index 493db87..b2f909b 100644
--- a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
+++ b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
@@ -141,7 +141,7 @@ ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
######################################################################
# Build the inference graph executor
-# ---------------------------------
+# ----------------------------------
# Grab vision model from Gluon model zoo and compile with Relay.
# The compilation steps are:
#
diff --git a/docs/_downloads/9a24ccf4f11859cf70918413ff2aea58/tune_matmul_x86.ipynb b/docs/_downloads/f5648c7917eb5013237a54625ed7a6c2/auto_scheduler_matmul_x86.ipynb
similarity index 93%
rename from docs/_downloads/9a24ccf4f11859cf70918413ff2aea58/tune_matmul_x86.ipynb
rename to docs/_downloads/f5648c7917eb5013237a54625ed7a6c2/auto_scheduler_matmul_x86.ipynb
index 4e25366..0ee7d5b 100644
--- a/docs/_downloads/9a24ccf4f11859cf70918413ff2aea58/tune_matmul_x86.ipynb
+++ b/docs/_downloads/f5648c7917eb5013237a54625ed7a6c2/auto_scheduler_matmul_x86.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\nOptimizing Operators with Auto-scheduling\n=========================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Chengfan Jia <https://github.com/jcf94/>`_\n\nIn this tutorial, we will show how TVM's Auto Scheduling feature can find\noptimal schedules without the need for writing a custom template.\n\nDifferent from the template-based `<autotvm_matmul>` which relies on\nmanual templates to define the search space, the auto-schedu [...]
+ "\nOptimizing Operators with Auto-scheduling\n=========================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Chengfan Jia <https://github.com/jcf94/>`_\n\nIn this tutorial, we will show how TVM's Auto Scheduling feature can find\noptimal schedules without the need for writing a custom template.\n\nDifferent from the template-based :doc:`AutoTVM <autotvm_matmul_x86>` which relies on\nmanual templates to define the search space [...]
]
},
{
diff --git a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
index 339edcd..437f771 100644
--- a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
+++ b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
@@ -108,7 +108,7 @@
},
"outputs": [],
"source": [
- "# You can skip the implementation of this function for this tutorial.\ndef tune_tasks(\n tasks,\n measure_option,\n tuner=\"xgb\",\n n_trial=1000,\n early_stopping=None,\n log_filename=\"tuning.log\",\n use_transfer_learning=True,\n):\n # create tmp log file\n tmp_log_file = log_filename + \".tmp\"\n if os.path.exists(tmp_log_file):\n os.remove(tmp_log_file)\n\n for i, tsk in enumerate(reversed(tasks)):\n prefix = \"[Task %2d/%2d] \ [...]
+ "# You can skip the implementation of this function for this tutorial.\ndef tune_tasks(\n tasks,\n measure_option,\n tuner=\"xgb\",\n n_trial=1000,\n early_stopping=None,\n log_filename=\"tuning.log\",\n use_transfer_learning=True,\n):\n # create tmp log file\n tmp_log_file = log_filename + \".tmp\"\n if os.path.exists(tmp_log_file):\n os.remove(tmp_log_file)\n\n for i, tsk in enumerate(reversed(tasks)):\n prefix = \"[Task %2d/%2d] \ [...]
]
},
{
diff --git a/docs/_downloads/2d6bc109462266f309063f204dc15e8e/autotvm_matmul.py b/docs/_downloads/f96f5aca0e2b941b788322b5509ba2b6/autotvm_matmul_x86.py
similarity index 96%
rename from docs/_downloads/2d6bc109462266f309063f204dc15e8e/autotvm_matmul.py
rename to docs/_downloads/f96f5aca0e2b941b788322b5509ba2b6/autotvm_matmul_x86.py
index 234315b..97e1b0b 100644
--- a/docs/_downloads/2d6bc109462266f309063f204dc15e8e/autotvm_matmul.py
+++ b/docs/_downloads/f96f5aca0e2b941b788322b5509ba2b6/autotvm_matmul_x86.py
@@ -15,17 +15,18 @@
# specific language governing permissions and limitations
# under the License.
"""
-Optimizing Operators with Templates and AutoTVM
-===============================================
+.. _tutorial-autotvm-matmul-x86:
+
+Optimizing Operators with Schedule Templates and AutoTVM
+========================================================
**Authors**:
`Lianmin Zheng <https://github.com/merrymercy>`_,
`Chris Hoge <https://github.com/hogepodge>`_
-In this tutorial, we will now show how the TVM Template Extension (TE) language
-can be used to write scheduling templates that can be searched by AutoTVM to
-find optimal configurations of scheduling variables. This process is called
-Auto-Tuning, and builds on TE to help automate the process of optimizing
-operations.
+In this tutorial, we show how the TVM Tensor Expression (TE) language
+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
multiplication using TE <tensor_expr_get_started>`.
@@ -371,6 +372,6 @@ tvm.testing.assert_allclose(c_np, c_tvm.numpy(), rtol=1e-4)
# 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
-# Expressions <tensor_expr_get_started>_` tutorial In the upcoming sections, we
+# Expressions <tensor_expr_get_started>_` tutorial. In the upcoming sections, we
# will demonstate 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/fb88afbf9be39a834109b9b842f12fd0/tune_network_arm.py b/docs/_downloads/fb88afbf9be39a834109b9b842f12fd0/tune_network_arm.py
index a7a844b..5b09314 100644
--- a/docs/_downloads/fb88afbf9be39a834109b9b842f12fd0/tune_network_arm.py
+++ b/docs/_downloads/fb88afbf9be39a834109b9b842f12fd0/tune_network_arm.py
@@ -437,7 +437,7 @@ def tune_and_evaluate():
# in function :code:`run_tuning`. Say,
# :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
# 4. If you have multiple target CPUs, you can use all of them for measurements to
-# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
# to learn how to use the RPC Tracker and RPC Server.
# To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
# with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_images/sphx_glr_tune_matmul_x86_thumb.png b/docs/_images/sphx_glr_auto_scheduler_matmul_x86_thumb.png
similarity index 100%
rename from docs/_images/sphx_glr_tune_matmul_x86_thumb.png
rename to docs/_images/sphx_glr_auto_scheduler_matmul_x86_thumb.png
diff --git a/docs/_images/sphx_glr_autotvm_matmul_thumb.png b/docs/_images/sphx_glr_autotvm_matmul_x86_thumb.png
similarity index 100%
rename from docs/_images/sphx_glr_autotvm_matmul_thumb.png
rename to docs/_images/sphx_glr_autotvm_matmul_x86_thumb.png
diff --git a/docs/_images/sphx_glr_auto_tuning_with_python_thumb.png b/docs/_images/sphx_glr_autotvm_relay_x86_thumb.png
similarity index 100%
rename from docs/_images/sphx_glr_auto_tuning_with_python_thumb.png
rename to docs/_images/sphx_glr_autotvm_relay_x86_thumb.png
diff --git a/docs/_sources/api/python/graph_executor.rst.txt b/docs/_sources/api/python/graph_executor.rst.txt
index 3f88115..1af93e8 100644
--- a/docs/_sources/api/python/graph_executor.rst.txt
+++ b/docs/_sources/api/python/graph_executor.rst.txt
@@ -16,6 +16,6 @@
under the License.
tvm.contrib.graph_executor
--------------------------
+--------------------------
.. automodule:: tvm.contrib.graph_executor
:members:
diff --git a/docs/_sources/deploy/bnns.rst.txt b/docs/_sources/deploy/bnns.rst.txt
index 7b62fb1..43c7e7b 100644
--- a/docs/_sources/deploy/bnns.rst.txt
+++ b/docs/_sources/deploy/bnns.rst.txt
@@ -175,7 +175,8 @@ Operator support
| nn.bias_add | Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense |
| | fusion |
+------------------------+------------------------------------------------------------------------------+
-| add | Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense fusion |
+| add | Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense |
+| | fusion |
+------------------------+------------------------------------------------------------------------------+
| nn.relu | Supported by BNNS integration only as a part of nn.conv2d or nn.dense fusion |
+------------------------+------------------------------------------------------------------------------+
diff --git a/docs/_sources/dev/device_target_interactions.rst.txt b/docs/_sources/dev/device_target_interactions.rst.txt
index 373b8fe..e5fa708 100644
--- a/docs/_sources/dev/device_target_interactions.rst.txt
+++ b/docs/_sources/dev/device_target_interactions.rst.txt
@@ -18,7 +18,7 @@
.. _tvm-target-specific-overview:
Device/Target Interactions
---------------------------
+==========================
This documented is intended for developers interested in understanding
how the TVM framework interacts with specific device APIs, or who
diff --git a/docs/_sources/dev/index.rst.txt b/docs/_sources/dev/index.rst.txt
index d03f0fb..873af9c 100644
--- a/docs/_sources/dev/index.rst.txt
+++ b/docs/_sources/dev/index.rst.txt
@@ -29,10 +29,6 @@ This page is organized as follows:
The sections after are specific guides focused on each logical component, organized
by the component's name.
-- The `Device/Target Interactions`_ section describes how TVM
- interacts with each supported physical device and code-generation
- target.
-
- Feel free to also check out the :ref:`dev-how-to` for useful development tips.
This guide provides a few complementary views of the architecture.
@@ -245,12 +241,13 @@ for learning-based optimizations.
.. toctree::
- :maxdepth: 2
+ :maxdepth: 1
runtime
debugger
virtual_machine
introduction_to_module_serialization
+ device_target_interactions
tvm/node
--------
@@ -318,6 +315,11 @@ It also provides a common `Target` class that describes the target.
The compilation pipeline can be customized according to the target by querying the attribute information
in the target and builtin information registered to each target id(cuda, opencl).
+.. toctree::
+ :maxdepth: 1
+
+ device_target_interactions
+
tvm/tir
-------
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index 2a1078e..e3cf466 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -44,8 +44,8 @@ For Developers
contribute/index
deploy/index
dev/how_to
- microtvm/index
errors
+ faq
.. toctree::
:maxdepth: 1
@@ -76,8 +76,8 @@ For Developers
:hidden:
:caption: MISC
+ microtvm/index
vta/index
- faq
Index
diff --git a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
index d05f35c..53b9c07 100644
--- a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
Computation times
=================
-**02:11.816** total execution time for **tutorials_auto_scheduler** files:
-
-- **00:49.990**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:48.961**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **00:15.992**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:13.475**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:02.244**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_arm.py` (``tune_network_arm.py``)
-- **00:01.153**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_mali.py` (``tune_network_mali.py``)
+**02:09.226** total execution time for **tutorials_auto_scheduler** files:
+
+- **01:03.026**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **00:33.480**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:15.965**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:13.303**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:02.297**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_arm.py` (``tune_network_arm.py``)
+- **00:01.156**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_mali.py` (``tune_network_mali.py``)
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
index 131456a..dc341d1 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
@@ -216,18 +216,18 @@ cooperative fetching, unrolling and operator fusion.
primfn(data_1: handle, kernel_1: handle, bias_1: handle, compute_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {compute: Buffer(compute_2: Pointer(float32), float32, [1, 512, 7, 7], []),
- bias: Buffer(bias_2: Pointer(float32), float32, [1, 512, 1, 1], []),
data: Buffer(data_2: Pointer(float32), float32, [1, 512, 7, 7], []),
+ bias: Buffer(bias_2: Pointer(float32), float32, [1, 512, 1, 1], []),
kernel: Buffer(kernel_2: Pointer(float32), float32, [512, 512, 3, 3], [])}
buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
- attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 128;
+ attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 64;
attr [compute_3: Pointer(float32)] "storage_scope" = "local";
allocate(compute_3, float32, [7]);
attr [pad_temp.shared: Pointer(float32)] "storage_scope" = "shared";
- allocate(pad_temp.shared, float32, [162]);
+ allocate(pad_temp.shared, float32, [1008]);
attr [kernel.shared: Pointer(float32)] "storage_scope" = "shared";
- allocate(kernel.shared, float32, [72]);
- attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28 {
+ allocate(kernel.shared, float32, [384]);
+ attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56 {
compute_3[0] = 0f32
compute_3[1] = 0f32
compute_3[2] = 0f32
@@ -235,50 +235,405 @@ cooperative fetching, unrolling and operator fusion.
compute_3[4] = 0f32
compute_3[5] = 0f32
compute_3[6] = 0f32
- for (rc.outer.outer: int32, 0, 256) {
- attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- pad_temp.shared[threadIdx.x_1] = @tir.if_then_else((((9 <= threadIdx.x_1) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[((((rc.outer.outer*98) + (floordiv(threadIdx.x_1, 9)*7)) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- pad_temp.shared[(threadIdx.x_1 + 28)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 1), 9)) && (floormod((threadIdx.x_1 + 1), 9) < 8)), (float32*)data_2[((((rc.outer.outer*98) + (floordiv((threadIdx.x_1 + 28), 9)*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- pad_temp.shared[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 56), 81)) && (floormod((threadIdx.x_1 + 56), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*98) + (floordiv((threadIdx.x_1 + 56), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 56), 81), 9)*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- pad_temp.shared[(threadIdx.x_1 + 84)] = @tir.if_then_else((((9 <= floormod((threadIdx.x_1 + 3), 81)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*98) + (floordiv((threadIdx.x_1 + 84), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 3), 81), 9)*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- pad_temp.shared[(threadIdx.x_1 + 112)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 4), 9)) && (floormod((threadIdx.x_1 + 4), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*98) + (floordiv((threadIdx.x_1 + 112), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 31), 81), 9)*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
- attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- if @tir.likely((threadIdx.x_1 < 22), dtype=bool) {
- pad_temp.shared[(threadIdx.x_1 + 140)] = @tir.if_then_else((((floormod((threadIdx.x_1 + 59), 81) < 72) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*98) + (floordiv((threadIdx.x_1 + 140), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 59), 81), 9)*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
- }
- attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- kernel.shared[threadIdx.x_2] = (float32*)kernel_2[((((blockIdx.x*18432) + (floordiv(threadIdx.x_2, 18)*4608)) + (rc.outer.outer*18)) + floormod(threadIdx.x_2, 18))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- kernel.shared[(threadIdx.x_2 + 28)] = (float32*)kernel_2[((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 28), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 10), 18))]
- attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
- if @tir.likely((threadIdx.x_2 < 16), dtype=bool) {
- kernel.shared[(threadIdx.x_2 + 56)] = (float32*)kernel_2[((((blockIdx.x*18432) + (floordiv((threadIdx.x_2 + 56), 18)*4608)) + (rc.outer.outer*18)) + floormod((threadIdx.x_2 + 2), 18))]
- }
- for (rx.outer.inner: int32, 0, 3) {
- for (rc.inner: int32, 0, 2) {
- for (ry.inner: int32, 0, 3) {
- compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7))]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 9)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 18)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 27)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 36)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 45)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(((((rc.inner*81) + (ry.inner*9)) + rx.outer.inner) + floormod(threadIdx.x, 7)) + 54)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (rc.inner*9)) + (ry.inner*3)) + rx.outer.inner)]))
- }
+ for (rc.outer.outer: int32, 0, 32) {
+ for (rx.outer.outer: int32, 0, 3) {
+ attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[threadIdx.x_1] = @tir.if_then_else((((7 <= threadIdx.x_1) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((rc.outer.outer*784) + threadIdx.x_1) + rx.outer.outer) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 8), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 8), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 8), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 8), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7) [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 7), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 7), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 16), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 7), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 168)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 6), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 6), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 24), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 6), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 5), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 5), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 32), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 5), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 280)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 4), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 4), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 40), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 4), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 336)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 3), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 3), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 48), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 3), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 392)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 2), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 2), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 56), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 448)] = @tir.if_then_else((((floormod((floordiv(threadIdx.x_1, 7) + 1), 9) < 8) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 64), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 1), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 504)] = @tir.if_then_else((((7 <= threadIdx.x_1) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[(((((rc.outer.outer*784) + (floordiv(threadIdx.x_1, 7)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) + 384)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 560)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 8), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 8), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 80), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 8), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 616)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 7), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 7), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 88), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 7), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 672)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 6), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 6), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 96), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 6), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 728)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 5), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 5), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 104), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 5), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 784)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 4), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 4), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 112), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 4), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 840)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 3), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 3), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 120), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 3), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 896)] = @tir.if_then_else(((((1 <= floormod((floordiv(threadIdx.x_1, 7) + 2), 9)) && (floormod((floordiv(threadIdx.x_1, 7) + 2), 9) < 8)) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 128), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 2), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, [...]
+ attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ pad_temp.shared[(threadIdx.x_1 + 952)] = @tir.if_then_else((((floormod((floordiv(threadIdx.x_1, 7) + 1), 9) < 8) && (1 <= (rx.outer.outer + floormod(threadIdx.x_1, 7)))) && ((rx.outer.outer + floormod(threadIdx.x_1, 7)) < 8)), (float32*)data_2[((((((rc.outer.outer*784) + (floordiv((floordiv(threadIdx.x_1, 7) + 136), 9)*49)) + (floormod((floordiv(threadIdx.x_1, 7) + 1), 9)*7)) + rx.outer.outer) + floormod(threadIdx.x_1, 7)) - 8)], 0f32, dtype=float32)
+ attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[threadIdx.x_2] = (float32*)kernel_2[(((((blockIdx.x*36864) + (floordiv(threadIdx.x_2, 48)*4608)) + (rc.outer.outer*144)) + (floormod(threadIdx.x_2, 48)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 56)] = (float32*)kernel_2[(((((blockIdx.x*36864) + (floordiv((threadIdx.x_2 + 56), 48)*4608)) + (rc.outer.outer*144)) + (floormod((threadIdx.x_2 + 8), 48)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 112)] = (float32*)kernel_2[(((((blockIdx.x*36864) + (floordiv((threadIdx.x_2 + 112), 48)*4608)) + (rc.outer.outer*144)) + (floormod((threadIdx.x_2 + 16), 48)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 168)] = (float32*)kernel_2[(((((blockIdx.x*36864) + (floordiv((threadIdx.x_2 + 168), 48)*4608)) + (rc.outer.outer*144)) + (floormod((threadIdx.x_2 + 24), 48)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 224)] = (float32*)kernel_2[(((((blockIdx.x*36864) + (floordiv((threadIdx.x_2 + 224), 48)*4608)) + (rc.outer.outer*144)) + (floormod((threadIdx.x_2 + 32), 48)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ kernel.shared[(threadIdx.x_2 + 280)] = (float32*)kernel_2[(((((blockIdx.x*36864) + (floordiv((threadIdx.x_2 + 280), 48)*4608)) + (rc.outer.outer*144)) + (floormod((threadIdx.x_2 + 40), 48)*3)) + rx.outer.outer)]
+ attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 56;
+ if @tir.likely((threadIdx.x_2 < 48), dtype=bool) {
+ kernel.shared[(threadIdx.x_2 + 336)] = (float32*)kernel_2[(((((blockIdx.x*36864) + (rc.outer.outer*144)) + (threadIdx.x_2*3)) + rx.outer.outer) + 32256)]
}
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7)*7)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 1)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 2)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 3)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 4)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 5)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 6)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*48)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 7)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 8)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 9)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 10)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 11)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 12)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 13)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 1)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 14)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 15)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 16)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 17)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 18)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 19)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 20)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 2)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 63)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 64)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 65)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 66)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 67)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 68)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 69)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 3)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 70)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 71)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 72)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 73)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 74)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 75)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 76)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 4)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 77)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 78)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 79)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 80)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 81)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 82)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 83)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 5)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 126)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 127)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 128)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 129)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 130)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 131)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 132)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 6)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 133)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 134)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 135)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 136)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 137)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 138)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 139)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 7)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 140)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 141)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 142)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 143)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 144)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 145)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 146)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 8)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 189)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 190)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 191)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 192)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 193)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 194)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 195)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 9)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 196)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 197)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 198)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 199)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 200)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 201)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 202)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 10)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 203)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 204)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 205)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 206)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 207)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 208)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 209)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 11)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 252)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 253)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 254)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 255)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 256)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 257)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 258)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 12)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 259)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 260)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 261)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 262)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 263)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 264)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 265)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 13)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 266)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 267)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 268)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 269)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 270)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 271)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 272)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 14)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 315)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 316)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 317)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 318)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 319)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 320)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 321)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 15)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 322)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 323)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 324)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 325)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 326)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 327)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 328)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 16)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 329)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 330)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 331)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 332)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 333)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 334)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 335)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 17)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 378)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 379)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 380)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 381)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 382)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 383)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 384)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 18)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 385)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 386)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 387)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 388)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 389)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 390)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 391)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 19)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 392)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 393)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 394)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 395)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 396)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 397)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 398)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 20)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 441)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 442)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 443)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 444)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 445)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 446)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 447)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 21)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 448)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 449)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 450)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 451)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 452)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 453)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 454)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 22)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 455)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 456)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 457)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 458)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 459)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 460)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 461)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 23)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 504)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 505)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 506)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 507)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 508)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 509)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 510)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 24)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 511)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 512)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 513)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 514)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 515)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 516)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 517)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 25)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 518)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 519)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 520)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 521)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 522)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 523)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 524)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 26)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 567)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 568)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 569)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 570)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 571)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 572)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 573)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 27)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 574)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 575)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 576)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 577)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 578)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 579)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 580)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 28)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 581)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 582)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 583)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 584)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 585)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 586)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 587)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 29)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 630)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 631)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 632)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 633)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 634)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 635)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 636)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 30)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 637)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 638)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 639)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 640)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 641)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 642)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 643)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 31)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 644)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 645)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 646)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 647)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 648)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 649)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 650)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 32)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 693)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 694)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 695)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 696)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 697)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 698)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 699)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 33)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 700)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 701)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 702)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 703)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 704)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 705)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 706)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 34)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 707)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 708)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 709)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 710)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 711)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 712)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 713)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 35)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 756)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 757)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 758)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 759)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 760)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 761)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 762)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 36)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 763)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 764)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 765)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 766)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 767)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 768)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 769)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 37)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 770)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 771)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 772)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 773)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 774)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 775)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 776)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 38)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 819)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 820)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 821)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 822)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 823)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 824)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 825)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 39)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 826)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 827)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 828)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 829)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 830)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 831)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 832)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 40)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 833)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 834)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 835)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 836)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 837)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 838)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 839)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 41)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 882)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 883)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 884)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 885)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 886)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 887)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 888)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 42)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 889)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 890)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 891)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 892)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 893)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 894)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 895)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 43)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 896)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 897)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 898)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 899)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 900)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 901)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 902)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 44)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 945)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 946)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 947)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 948)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 949)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 950)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 951)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 45)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 952)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 953)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 954)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 955)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 956)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 957)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 958)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 46)]))
+ compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 959)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
+ compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 960)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
+ compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 961)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
+ compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 962)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
+ compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 963)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
+ compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 964)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
+ compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[((floormod(threadIdx.x, 7)*7) + 965)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*48) + 47)]))
}
}
- compute_2[(((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7))] = max(((float32*)compute_3[0] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
- compute_2[((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7)) + 7)] = max(((float32*)compute_3[1] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
- compute_2[((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7)) + 14)] = max(((float32*)compute_3[2] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
- compute_2[((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7)) + 21)] = max(((float32*)compute_3[3] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
- compute_2[((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7)) + 28)] = max(((float32*)compute_3[4] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
- compute_2[((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7)) + 35)] = max(((float32*)compute_3[5] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
- compute_2[((((blockIdx.x*196) + (floordiv(threadIdx.x, 7)*49)) + floormod(threadIdx.x, 7)) + 42)] = max(((float32*)compute_3[6] + (float32*)bias_2[((blockIdx.x*4) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[((blockIdx.x*392) + (threadIdx.x*7))] = max(((float32*)compute_3[0] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[(((blockIdx.x*392) + (threadIdx.x*7)) + 1)] = max(((float32*)compute_3[1] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[(((blockIdx.x*392) + (threadIdx.x*7)) + 2)] = max(((float32*)compute_3[2] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[(((blockIdx.x*392) + (threadIdx.x*7)) + 3)] = max(((float32*)compute_3[3] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[(((blockIdx.x*392) + (threadIdx.x*7)) + 4)] = max(((float32*)compute_3[4] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[(((blockIdx.x*392) + (threadIdx.x*7)) + 5)] = max(((float32*)compute_3[5] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
+ compute_2[(((blockIdx.x*392) + (threadIdx.x*7)) + 6)] = max(((float32*)compute_3[6] + (float32*)bias_2[((blockIdx.x*8) + floordiv(threadIdx.x, 7))]), 0f32)
}
}
@@ -330,7 +685,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.266 ms
+ Execution time of this operator: 0.353 ms
@@ -376,35 +731,35 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
compute_nn_o_o_o_o, compute_nn_o_o_o_i = s[compute].split(compute_nn_o_o_o_i, factor=1)
compute_ff_o_i, compute_ff_i = s[compute].split(compute_ff, factor=1)
compute_ff_o_o_i, compute_ff_o_i = s[compute].split(compute_ff_o_i, factor=1)
- compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=4)
+ compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=8)
compute_ff_o_o_o_o, compute_ff_o_o_o_i = s[compute].split(compute_ff_o_o_o_i, factor=1)
compute_yy_o_i, compute_yy_i = s[compute].split(compute_yy, factor=1)
compute_yy_o_o_i, compute_yy_o_i = s[compute].split(compute_yy_o_i, factor=1)
- compute_yy_o_o_o_i, compute_yy_o_o_i = s[compute].split(compute_yy_o_o_i, factor=1)
- compute_yy_o_o_o_o, compute_yy_o_o_o_i = s[compute].split(compute_yy_o_o_o_i, factor=7)
+ compute_yy_o_o_o_i, compute_yy_o_o_i = s[compute].split(compute_yy_o_o_i, factor=7)
+ compute_yy_o_o_o_o, compute_yy_o_o_o_i = s[compute].split(compute_yy_o_o_o_i, factor=1)
compute_xx_o_i, compute_xx_i = s[compute].split(compute_xx, factor=1)
compute_xx_o_o_i, compute_xx_o_i = s[compute].split(compute_xx_o_i, factor=1)
- compute_xx_o_o_o_i, compute_xx_o_o_i = s[compute].split(compute_xx_o_o_i, factor=7)
- compute_xx_o_o_o_o, compute_xx_o_o_o_i = s[compute].split(compute_xx_o_o_o_i, factor=1)
- compute_rc_o_i, compute_rc_i = s[compute].split(compute_rc, factor=2)
+ compute_xx_o_o_o_i, compute_xx_o_o_i = s[compute].split(compute_xx_o_o_i, factor=1)
+ compute_xx_o_o_o_o, compute_xx_o_o_o_i = s[compute].split(compute_xx_o_o_o_i, factor=7)
+ compute_rc_o_i, compute_rc_i = s[compute].split(compute_rc, factor=16)
compute_rc_o_o, compute_rc_o_i = s[compute].split(compute_rc_o_i, factor=1)
compute_ry_o_i, compute_ry_i = s[compute].split(compute_ry, factor=3)
compute_ry_o_o, compute_ry_o_i = s[compute].split(compute_ry_o_i, factor=1)
compute_rx_o_i, compute_rx_i = s[compute].split(compute_rx, factor=1)
- compute_rx_o_o, compute_rx_o_i = s[compute].split(compute_rx_o_i, factor=3)
+ compute_rx_o_o, compute_rx_o_i = s[compute].split(compute_rx_o_i, factor=1)
s[compute].reorder(compute_nn_o_o_o_o, compute_ff_o_o_o_o, compute_yy_o_o_o_o, compute_xx_o_o_o_o, compute_nn_o_o_o_i, compute_ff_o_o_o_i, compute_yy_o_o_o_i, compute_xx_o_o_o_i, compute_nn_o_o_i, compute_ff_o_o_i, compute_yy_o_o_i, compute_xx_o_o_i, compute_rc_o_o, compute_ry_o_o, compute_rx_o_o, compute_rc_o_i, compute_ry_o_i, compute_rx_o_i, compute_nn_o_i, compute_ff_o_i, compute_yy_o_i, compute_xx_o_i, compute_rc_i, compute_ry_i, compute_rx_i, compute_nn_i, compute_ff_i, compute [...]
compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
- compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=4)
+ compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=8)
compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
- compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
- compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=7)
+ compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
+ compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
- compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
- compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+ compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+ compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
s[compute].compute_at(s[compute], compute_i3_o_i)
kernel_shared = s.cache_read(kernel, "shared", [compute])
@@ -423,14 +778,14 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=28)
+ kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=56)
s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
- pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=28)
+ pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=56)
s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
- s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 16)
+ s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 1024)
s[compute].pragma(compute_nn_o_o_o_o, "unroll_explicit", True)
CUDA source code:
@@ -450,8 +805,8 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
#endif
extern "C" __global__ void default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
float compute1[7];
- __shared__ float pad_temp_shared[162];
- __shared__ float kernel_shared[72];
+ __shared__ float pad_temp_shared[1008];
+ __shared__ float kernel_shared[384];
compute1[(0)] = 0.000000e+00f;
compute1[(1)] = 0.000000e+00f;
compute1[(2)] = 0.000000e+00f;
@@ -459,43 +814,382 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
compute1[(4)] = 0.000000e+00f;
compute1[(5)] = 0.000000e+00f;
compute1[(6)] = 0.000000e+00f;
- for (int rc_outer_outer = 0; rc_outer_outer < 256; ++rc_outer_outer) {
- __syncthreads();
- pad_temp_shared[(((int)threadIdx.x))] = ((((9 <= ((int)threadIdx.x)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 98) + ((((int)threadIdx.x) / 9) * 7)) + (((int)threadIdx.x) % 9)) - 8))] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) + 28))] = (((1 <= ((((int)threadIdx.x) + 1) % 9)) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 98) + (((((int)threadIdx.x) + 28) / 9) * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8))] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) + 56))] = (((((9 <= ((((int)threadIdx.x) + 56) % 81)) && (((((int)threadIdx.x) + 56) % 81) < 72)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[((((((rc_outer_outer * 98) + (((((int)threadIdx.x) + 56) / 81) * 49)) + ((((((int)threadIdx.x) + 56) % 81) / 9) * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8))] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) + 84))] = ((((6 <= ((int)threadIdx.x)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[((((((rc_outer_outer * 98) + (((((int)threadIdx.x) + 84) / 81) * 49)) + (((((int)threadIdx.x) + 3) / 9) * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8))] : 0.000000e+00f);
- pad_temp_shared[((((int)threadIdx.x) + 112))] = (((1 <= ((((int)threadIdx.x) + 4) % 9)) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[((((((rc_outer_outer * 98) + (((((int)threadIdx.x) + 112) / 81) * 49)) + (((((int)threadIdx.x) + 31) / 9) * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8))] : 0.000000e+00f);
- if (((int)threadIdx.x) < 22) {
- pad_temp_shared[((((int)threadIdx.x) + 140))] = ((((((int)threadIdx.x) < 13) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[((((((rc_outer_outer * 98) + (((((int)threadIdx.x) + 140) / 81) * 49)) + (((((int)threadIdx.x) + 59) / 9) * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8))] : 0.000000e+00f);
- }
- kernel_shared[(((int)threadIdx.x))] = kernel[(((((((int)blockIdx.x) * 18432) + ((((int)threadIdx.x) / 18) * 4608)) + (rc_outer_outer * 18)) + (((int)threadIdx.x) % 18)))];
- kernel_shared[((((int)threadIdx.x) + 28))] = kernel[(((((((int)blockIdx.x) * 18432) + (((((int)threadIdx.x) + 28) / 18) * 4608)) + (rc_outer_outer * 18)) + ((((int)threadIdx.x) + 10) % 18)))];
- if (((int)threadIdx.x) < 16) {
- kernel_shared[((((int)threadIdx.x) + 56))] = kernel[(((((((int)blockIdx.x) * 18432) + (((((int)threadIdx.x) + 56) / 18) * 4608)) + (rc_outer_outer * 18)) + (((int)threadIdx.x) + 2)))];
- }
- __syncthreads();
- for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
- for (int rc_inner = 0; rc_inner < 2; ++rc_inner) {
- for (int ry_inner = 0; ry_inner < 3; ++ry_inner) {
- compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 9))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 18))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 27))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 36))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 45))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((rc_inner * 81) + (ry_inner * 9)) + rx_outer_inner) + (((int)threadIdx.x) % 7)) + 54))] * kernel_shared[((((((((int)threadIdx.x) / 7) * 18) + (rc_inner * 9)) + (ry_inner * 3)) + rx_outer_inner))]));
- }
+ for (int rc_outer_outer = 0; rc_outer_outer < 32; ++rc_outer_outer) {
+ for (int rx_outer_outer = 0; rx_outer_outer < 3; ++rx_outer_outer) {
+ __syncthreads();
+ pad_temp_shared[(((int)threadIdx.x))] = ((((7 <= ((int)threadIdx.x)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((rc_outer_outer * 784) + ((int)threadIdx.x)) + rx_outer_outer) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 56))] = (((((1 <= (((((int)threadIdx.x) / 7) + 8) % 9)) && ((((((int)threadIdx.x) / 7) + 8) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 56) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 8) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 112))] = (((((1 <= (((((int)threadIdx.x) / 7) + 7) % 9)) && ((((((int)threadIdx.x) / 7) + 7) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 112) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 7) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 168))] = (((((1 <= (((((int)threadIdx.x) / 7) + 6) % 9)) && ((((((int)threadIdx.x) / 7) + 6) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 168) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 6) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 224))] = (((((1 <= (((((int)threadIdx.x) / 7) + 5) % 9)) && ((((((int)threadIdx.x) / 7) + 5) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 224) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 5) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 280))] = (((((1 <= (((((int)threadIdx.x) / 7) + 4) % 9)) && ((((((int)threadIdx.x) / 7) + 4) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 280) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 4) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 336))] = (((((1 <= (((((int)threadIdx.x) / 7) + 3) % 9)) && ((((((int)threadIdx.x) / 7) + 3) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 336) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 3) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 392))] = (((((1 <= (((((int)threadIdx.x) / 7) + 2) % 9)) && ((((((int)threadIdx.x) / 7) + 2) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 392) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 2) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 448))] = ((((((int)threadIdx.x) < 49) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 448) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 1) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 504))] = ((((7 <= ((int)threadIdx.x)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((rc_outer_outer * 784) + ((int)threadIdx.x)) + rx_outer_outer) + 384))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 560))] = (((((1 <= (((((int)threadIdx.x) / 7) + 8) % 9)) && ((((((int)threadIdx.x) / 7) + 8) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 560) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 8) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 616))] = (((((1 <= (((((int)threadIdx.x) / 7) + 7) % 9)) && ((((((int)threadIdx.x) / 7) + 7) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 616) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 7) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 672))] = (((((1 <= (((((int)threadIdx.x) / 7) + 6) % 9)) && ((((((int)threadIdx.x) / 7) + 6) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 672) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 6) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 728))] = (((((1 <= (((((int)threadIdx.x) / 7) + 5) % 9)) && ((((((int)threadIdx.x) / 7) + 5) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 728) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 5) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 784))] = (((((1 <= (((((int)threadIdx.x) / 7) + 4) % 9)) && ((((((int)threadIdx.x) / 7) + 4) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 784) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 4) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 840))] = (((((1 <= (((((int)threadIdx.x) / 7) + 3) % 9)) && ((((((int)threadIdx.x) / 7) + 3) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 840) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 3) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 896))] = (((((1 <= (((((int)threadIdx.x) / 7) + 2) % 9)) && ((((((int)threadIdx.x) / 7) + 2) % 9) < 8)) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 896) / 63) * 49)) + ((((((int)threadIdx.x) / 7) + 2) % 9) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ pad_temp_shared[((((int)threadIdx.x) + 952))] = ((((((int)threadIdx.x) < 49) && (1 <= (rx_outer_outer + (((int)threadIdx.x) % 7)))) && ((rx_outer_outer + (((int)threadIdx.x) % 7)) < 8)) ? data[(((((((rc_outer_outer * 784) + (((((int)threadIdx.x) + 952) / 63) * 49)) + (((((int)threadIdx.x) / 7) + 1) * 7)) + rx_outer_outer) + (((int)threadIdx.x) % 7)) - 8))] : 0.000000e+00f);
+ kernel_shared[(((int)threadIdx.x))] = kernel[((((((((int)blockIdx.x) * 36864) + ((((int)threadIdx.x) / 48) * 4608)) + (rc_outer_outer * 144)) + ((((int)threadIdx.x) % 48) * 3)) + rx_outer_outer))];
+ kernel_shared[((((int)threadIdx.x) + 56))] = kernel[((((((((int)blockIdx.x) * 36864) + (((((int)threadIdx.x) + 56) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) + 8) % 48) * 3)) + rx_outer_outer))];
+ kernel_shared[((((int)threadIdx.x) + 112))] = kernel[((((((((int)blockIdx.x) * 36864) + (((((int)threadIdx.x) + 112) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) + 16) % 48) * 3)) + rx_outer_outer))];
+ kernel_shared[((((int)threadIdx.x) + 168))] = kernel[((((((((int)blockIdx.x) * 36864) + (((((int)threadIdx.x) + 168) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) + 24) % 48) * 3)) + rx_outer_outer))];
+ kernel_shared[((((int)threadIdx.x) + 224))] = kernel[((((((((int)blockIdx.x) * 36864) + (((((int)threadIdx.x) + 224) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) + 32) % 48) * 3)) + rx_outer_outer))];
+ kernel_shared[((((int)threadIdx.x) + 280))] = kernel[((((((((int)blockIdx.x) * 36864) + (((((int)threadIdx.x) + 280) / 48) * 4608)) + (rc_outer_outer * 144)) + (((((int)threadIdx.x) + 40) % 48) * 3)) + rx_outer_outer))];
+ if (((int)threadIdx.x) < 48) {
+ kernel_shared[((((int)threadIdx.x) + 336))] = kernel[((((((((int)blockIdx.x) * 36864) + (rc_outer_outer * 144)) + (((int)threadIdx.x) * 3)) + rx_outer_outer) + 32256))];
}
+ __syncthreads();
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[(((((int)threadIdx.x) % 7) * 7))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 1))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 2))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 3))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 4))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 5))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 6))] * kernel_shared[(((((int)threadIdx.x) / 7) * 48))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 8))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 9))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 10))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 11))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 12))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 13))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 1))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 14))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 15))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 16))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 17))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 18))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 19))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 20))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 2))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 63))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 64))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 65))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 66))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 67))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 68))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 69))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 3))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 70))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 71))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 72))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 73))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 74))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 75))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 76))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 4))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 77))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 78))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 79))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 80))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 81))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 82))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 83))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 5))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 126))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 127))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 128))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 129))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 130))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 131))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 132))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 6))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 133))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 134))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 135))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 136))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 137))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 138))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 139))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 7))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 140))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 141))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 142))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 143))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 144))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 145))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 146))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 8))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 189))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 190))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 191))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 192))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 193))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 194))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 195))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 9))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 196))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 197))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 198))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 199))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 200))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 201))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 202))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 10))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 203))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 204))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 205))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 206))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 207))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 208))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 209))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 11))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 252))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 253))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 254))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 255))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 256))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 257))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 258))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 12))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 259))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 260))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 261))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 262))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 263))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 264))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 265))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 13))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 266))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 267))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 268))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 269))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 270))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 271))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 272))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 14))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 315))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 316))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 317))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 318))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 319))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 320))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 321))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 15))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 322))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 323))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 324))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 325))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 326))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 327))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 328))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 16))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 329))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 330))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 331))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 332))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 333))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 334))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 335))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 17))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 378))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 379))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 380))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 381))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 382))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 383))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 384))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 18))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 385))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 386))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 387))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 388))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 389))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 390))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 391))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 19))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 392))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 393))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 394))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 395))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 396))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 397))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 398))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 20))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 441))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 442))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 443))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 444))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 445))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 446))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 447))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 21))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 448))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 449))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 450))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 451))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 452))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 453))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 454))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 22))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 455))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 456))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 457))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 458))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 459))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 460))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 461))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 23))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 504))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 505))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 506))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 507))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 508))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 509))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 510))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 24))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 511))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 512))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 513))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 514))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 515))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 516))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 517))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 25))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 518))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 519))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 520))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 521))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 522))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 523))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 524))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 26))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 567))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 568))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 569))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 570))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 571))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 572))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 573))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 27))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 574))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 575))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 576))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 577))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 578))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 579))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 580))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 28))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 581))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 582))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 583))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 584))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 585))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 586))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 587))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 29))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 630))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 631))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 632))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 633))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 634))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 635))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 636))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 30))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 637))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 638))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 639))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 640))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 641))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 642))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 643))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 31))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 644))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 645))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 646))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 647))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 648))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 649))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 650))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 32))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 693))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 694))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 695))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 696))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 697))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 698))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 699))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 33))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 700))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 701))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 702))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 703))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 704))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 705))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 706))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 34))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 707))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 708))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 709))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 710))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 711))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 712))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 713))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 35))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 756))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 757))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 758))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 759))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 760))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 761))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 762))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 36))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 763))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 764))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 765))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 766))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 767))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 768))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 769))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 37))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 770))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 771))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 772))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 773))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 774))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 775))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 776))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 38))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 819))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 820))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 821))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 822))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 823))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 824))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 825))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 39))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 826))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 827))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 828))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 829))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 830))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 831))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 832))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 40))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 833))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 834))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 835))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 836))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 837))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 838))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 839))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 41))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 882))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 883))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 884))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 885))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 886))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 887))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 888))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 42))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 889))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 890))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 891))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 892))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 893))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 894))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 895))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 43))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 896))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 897))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 898))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 899))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 900))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 901))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 902))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 44))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 945))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 946))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 947))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 948))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 949))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 950))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 951))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 45))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 952))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 953))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 954))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 955))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 956))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 957))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 958))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 46))]));
+ compute1[(0)] = (compute1[(0)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 959))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
+ compute1[(1)] = (compute1[(1)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 960))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
+ compute1[(2)] = (compute1[(2)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 961))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
+ compute1[(3)] = (compute1[(3)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 962))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
+ compute1[(4)] = (compute1[(4)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 963))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
+ compute1[(5)] = (compute1[(5)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 964))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
+ compute1[(6)] = (compute1[(6)] + (pad_temp_shared[((((((int)threadIdx.x) % 7) * 7) + 965))] * kernel_shared[((((((int)threadIdx.x) / 7) * 48) + 47))]));
}
}
- compute[((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)))] = max((compute1[(0)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
- compute[(((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)) + 7))] = max((compute1[(1)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
- compute[(((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)) + 14))] = max((compute1[(2)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
- compute[(((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)) + 21))] = max((compute1[(3)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
- compute[(((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)) + 28))] = max((compute1[(4)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
- compute[(((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)) + 35))] = max((compute1[(5)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
- compute[(((((((int)blockIdx.x) * 196) + ((((int)threadIdx.x) / 7) * 49)) + (((int)threadIdx.x) % 7)) + 42))] = max((compute1[(6)] + bias[(((((int)blockIdx.x) * 4) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[(((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)))] = max((compute1[(0)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[((((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)) + 1))] = max((compute1[(1)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[((((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)) + 2))] = max((compute1[(2)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[((((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)) + 3))] = max((compute1[(3)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[((((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)) + 4))] = max((compute1[(4)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[((((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)) + 5))] = max((compute1[(5)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
+ compute[((((((int)blockIdx.x) * 392) + (((int)threadIdx.x) * 7)) + 6))] = max((compute1[(6)] + bias[(((((int)blockIdx.x) * 8) + (((int)threadIdx.x) / 7)))]), 0.000000e+00f);
}
@@ -551,6 +1245,11 @@ In the example below we resume the status and do more 5 trials.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 3.026 seconds)
+
+
.. _sphx_glr_download_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py:
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_arm.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_arm.rst.txt
index 199fd09..301c726 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_arm.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_arm.rst.txt
@@ -668,7 +668,7 @@ Other Tips
in function :code:`run_tuning`. Say,
:code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
4. If you have multiple target CPUs, you can use all of them for measurements to
- parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+ parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
to learn how to use the RPC Tracker and RPC Server.
To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
index 541f182..3ae90b5 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
@@ -614,7 +614,7 @@ so we can read the log file and load the best schedules.
Compile...
Evaluate inference time cost...
- Mean inference time (std dev): 2.43 ms (0.00 ms)
+ Mean inference time (std dev): 2.44 ms (0.00 ms)
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt
index f25ac86..96e6e42 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_mali.rst.txt
@@ -207,7 +207,7 @@ The task scheduler will just optimize this objective.
.. code-block:: none
Extract tasks...
-
...2%, 0.01 MB, 52 KB/s, 0 seconds passed
...5%, 0.02 MB, 104 KB/s, 0 seconds passed
...7%, 0.02 MB, 157 KB/s, 0 seconds passed
...10%, 0.03 MB, 209 KB/s, 0 seconds passed
...12%, 0.04 MB, 261 KB/s, 0 seconds passed
...15%, 0.05 MB, 313 KB/s, 0 seconds passed
...18%, 0.05 MB, 364 KB/s, 0 seconds passed
...20%, 0.06 MB, 416 KB/s, 0 seconds passed
...23%, 0.07 MB, 468 KB/s, 0 seconds passed
...25%, 0.08 MB, 519 KB/s, 0 seconds passed
...28%, 0.09 MB, 571 KB/s, 0 seconds passed
...30%, 0.09 MB, 622 KB/s, 0 seconds passed
...33%, 0.10 MB, 672 KB/s, 0 seconds passed
...36%, 0.11 MB, 723 KB/s, 0 seconds passed
...38%, 0.12 MB, 774 KB/s, 0 seconds passed
...41%, 0.12 MB, 824 KB/s, 0 seconds passed
...43%, 0.13 MB, 873 KB/s, 0 seconds passed
...46%, 0.14 MB, 923 KB/s, 0 seconds passed
...48%, 0.15 MB, 974 KB/s, 0 seconds passed
...51%, 0.16 MB, 1024 KB/s, 0 seconds passed
...54%, 0.16 MB, 1072 KB/s, 0 secon
ds passed
...56%, 0.17 MB, 1121 KB/s, 0 seconds passed
...59%, 0.18 MB, 1171 KB/s, 0 seconds passed
...61%, 0.19 MB, 1221 KB/s, 0 seconds passed
...64%, 0.20 MB, 1271 KB/s, 0 seconds passed
...66%, 0.20 MB, 1320 KB/s, 0 seconds passed
...69%, 0.21 MB, 1368 KB/s, 0 seconds passed
...72%, 0.22 MB, 1417 KB/s, 0 seconds passed
...74%, 0.23 MB, 1466 KB/s, 0 seconds passed
...77%, 0.23 MB, 1515 KB/s, 0 seconds passed
...79%, 0.24 MB, 1563 KB/s, 0 seconds passed
...82%, 0.25 MB, 1612 KB/s, 0 seconds passed
...84%, 0.26 MB, 1661 KB/s, 0 seconds passed
...87%, 0.27 MB, 1710 KB/s, 0 seconds passed
...90%, 0.27 MB, 1758 KB/s, 0 seconds passed
...92%, 0.28 MB, 1806 KB/s, 0 seconds passed
...95%, 0.29 MB, 1855 KB/s, 0 seconds passed
...97%, 0.30 MB, 1904 KB/s, 0 seconds passed
...100%, 0.30 MB, 1950 KB/s, 0 seconds passed
+
...2%, 0.01 MB, 49 KB/s, 0 seconds passed
...5%, 0.02 MB, 98 KB/s, 0 seconds passed
...7%, 0.02 MB, 147 KB/s, 0 seconds passed
...10%, 0.03 MB, 196 KB/s, 0 seconds passed
...12%, 0.04 MB, 245 KB/s, 0 seconds passed
...15%, 0.05 MB, 294 KB/s, 0 seconds passed
...18%, 0.05 MB, 342 KB/s, 0 seconds passed
...20%, 0.06 MB, 391 KB/s, 0 seconds passed
...23%, 0.07 MB, 438 KB/s, 0 seconds passed
...25%, 0.08 MB, 487 KB/s, 0 seconds passed
...28%, 0.09 MB, 535 KB/s, 0 seconds passed
...30%, 0.09 MB, 583 KB/s, 0 seconds passed
...33%, 0.10 MB, 631 KB/s, 0 seconds passed
...36%, 0.11 MB, 678 KB/s, 0 seconds passed
...38%, 0.12 MB, 724 KB/s, 0 seconds passed
...41%, 0.12 MB, 772 KB/s, 0 seconds passed
...43%, 0.13 MB, 819 KB/s, 0 seconds passed
...46%, 0.14 MB, 867 KB/s, 0 seconds passed
...48%, 0.15 MB, 914 KB/s, 0 seconds passed
...51%, 0.16 MB, 961 KB/s, 0 seconds passed
...54%, 0.16 MB, 1006 KB/s, 0 seconds
passed
...56%, 0.17 MB, 1053 KB/s, 0 seconds passed
...59%, 0.18 MB, 1100 KB/s, 0 seconds passed
...61%, 0.19 MB, 1147 KB/s, 0 seconds passed
...64%, 0.20 MB, 1193 KB/s, 0 seconds passed
...66%, 0.20 MB, 1240 KB/s, 0 seconds passed
...69%, 0.21 MB, 1283 KB/s, 0 seconds passed
...72%, 0.22 MB, 1329 KB/s, 0 seconds passed
...74%, 0.23 MB, 1376 KB/s, 0 seconds passed
...77%, 0.23 MB, 1422 KB/s, 0 seconds passed
...79%, 0.24 MB, 1468 KB/s, 0 seconds passed
...82%, 0.25 MB, 1514 KB/s, 0 seconds passed
...84%, 0.26 MB, 1556 KB/s, 0 seconds passed
...87%, 0.27 MB, 1602 KB/s, 0 seconds passed
...90%, 0.27 MB, 1648 KB/s, 0 seconds passed
...92%, 0.28 MB, 1693 KB/s, 0 seconds passed
...95%, 0.29 MB, 1739 KB/s, 0 seconds passed
...97%, 0.30 MB, 1784 KB/s, 0 seconds passed
...100%, 0.30 MB, 1827 KB/s, 0 seconds passed
========== Task 0 (workload key: ["d7b65649a4dd54becea0a52aabbc5af5", 1, 1000, 1, 1000]) ==========
placeholder = PLACEHOLDER [1, 1000]
T_softmax_maxelem(i0) max= placeholder[i0, k]
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
index 4e3a944..84ececf 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_x86.rst.txt
@@ -633,7 +633,7 @@ so we can read the log file and load the best schedules.
Compile...
Evaluate inference time cost...
- Mean inference time (std dev): 1132.91 ms (25.38 ms)
+ Mean inference time (std dev): 601.80 ms (1.87 ms)
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_sparse_x86.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_sparse_x86.rst.txt
index 66f2e91..73bc15c 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_sparse_x86.rst.txt
@@ -354,32 +354,60 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
Lowered TIR:
primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {placeholder_4: Buffer(placeholder_10: Pointer(float32), float32, [128, 256], []),
- placeholder_3: Buffer(placeholder_11: Pointer(int32), int32, [4916], []),
- placeholder_1: Buffer(placeholder_12: Pointer(float32), float32, [4916, 16, 1], []),
+ buffers = {compute: Buffer(compute_2: Pointer(float32), float32, [128, 512], []),
+ placeholder_4: Buffer(placeholder_10: Pointer(float32), float32, [128, 512], []),
+ placeholder_3: Buffer(placeholder_11: Pointer(float32), float32, [128, 256], []),
+ placeholder_2: Buffer(placeholder_12: Pointer(float32), float32, [4916, 16, 1], []),
placeholder: Buffer(placeholder_13: Pointer(int32), int32, [33], []),
- placeholder_2: Buffer(placeholder_14: Pointer(float32), float32, [128, 512], []),
- compute: Buffer(compute_2: Pointer(float32), float32, [128, 512], [])}
- buffer_map = {placeholder_8: placeholder, compute_1: compute, placeholder_6: placeholder_1, placeholder_9: placeholder_2, placeholder_7: placeholder_3, placeholder_5: placeholder_4} {
- for (i0.outer.i1.outer.fused: int32, 0, 128) "parallel" {
+ placeholder_1: Buffer(placeholder_14: Pointer(int32), int32, [4916], [])}
+ buffer_map = {placeholder_8: placeholder, placeholder_7: placeholder_1, placeholder_6: placeholder_2, placeholder_5: placeholder_3, placeholder_9: placeholder_4, compute_1: compute} {
+ for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
attr [compute_3: Pointer(float32)] "storage_scope" = "global";
- allocate(compute_3, float32, [512]) {
+ allocate(compute_3, float32, [2048]) {
for (i.outer.inner: int32, 0, 8) {
- for (i.inner.init: int32, 0, 4) {
- for (j.init: int32, 0, 16) {
- compute_3[(((i.outer.inner*64) + (i.inner.init*16)) + j.init)] = 0f32
- }
+ for (i.inner.init: int32, 0, 16) {
+ compute_3[((i.outer.inner*256) + (i.inner.init*16))] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 1)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 2)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 3)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 4)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 5)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 6)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 7)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 8)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 9)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 10)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 11)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 12)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 13)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 14)] = 0f32
+ compute_3[(((i.outer.inner*256) + (i.inner.init*16)) + 15)] = 0f32
}
- for (elem_idx: int32, 0, ((int32*)placeholder_13[(floormod(i0.outer.i1.outer.fused, 32) + 1)] - (int32*)placeholder_13[floormod(i0.outer.i1.outer.fused, 32)])) {
- for (i.inner: int32, 0, 4) {
- for (j: int32, 0, 16) {
- compute_3[(((i.outer.inner*64) + (i.inner*16)) + j)] = ((float32*)compute_3[(((i.outer.inner*64) + (i.inner*16)) + j)] + ((float32*)placeholder_12[((((int32*)placeholder_13[floormod(i0.outer.i1.outer.fused, 32)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_10[((((floordiv(i0.outer.i1.outer.fused, 32)*8192) + (i.outer.inner*1024)) + (i.inner*256)) + (int32*)placeholder_11[((int32*)placeholder_13[floormod(i0.outer.i1.outer.fused, 32)] + elem_idx)])], 0f32)))
- }
+ for (elem_idx: int32, 0, ((int32*)placeholder_13[(i0.outer.i1.outer.fused + 1)] - (int32*)placeholder_13[i0.outer.i1.outer.fused])) {
+ for (i.inner: int32, 0, 16) {
+ compute_3[((i.outer.inner*256) + (i.inner*16))] = ((float32*)compute_3[((i.outer.inner*256) + (i.inner*16))] + ((float32*)placeholder_12[(((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16))]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 1)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 1)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 1)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 2)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 2)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 2)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 3)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 3)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 3)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 4)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 4)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 4)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 5)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 5)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 5)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 6)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 6)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 6)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 7)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 7)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 7)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 8)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 8)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 8)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 9)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 9)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 9)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 10)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 10)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 10)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 11)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 11)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 11)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 12)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 12)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 12)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 13)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 13)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 13)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 14)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 14)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 14)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
+ compute_3[(((i.outer.inner*256) + (i.inner*16)) + 15)] = ((float32*)compute_3[(((i.outer.inner*256) + (i.inner*16)) + 15)] + ((float32*)placeholder_12[((((int32*)placeholder_13[i0.outer.i1.outer.fused]*16) + (elem_idx*16)) + 15)]*max((float32*)placeholder_11[(((i.outer.inner*4096) + (i.inner*256)) + (int32*)placeholder_14[((int32*)placeholder_13[i0.outer.i1.outer.fused] + elem_idx)])], 0f32)))
}
}
}
- for (i0.inner: int32, 0, 32) {
- compute_2[ramp((((floordiv(i0.outer.i1.outer.fused, 32)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16)), 1, 16)] = max(((float32x16*)compute_3[ramp((i0.inner*16), 1, 16)] + (float32x16*)placeholder_14[ramp((((floordiv(i0.outer.i1.outer.fused, 32)*16384) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 32)*16)), 1, 16)]), broadcast(0f32, 16))
+ for (i0.inner: int32, 0, 128) {
+ for (i1.inner: int32, 0, 16) {
+ compute_2[(((i0.inner*512) + (i0.outer.i1.outer.fused*16)) + i1.inner)] = max(((float32*)compute_3[((i0.inner*16) + i1.inner)] + (float32*)placeholder_10[(((i0.inner*512) + (i0.outer.i1.outer.fused*16)) + i1.inner)]), 0f32)
+ }
}
}
}
@@ -433,7 +461,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 0.876 ms
+ Execution time of this operator: 0.906 ms
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index 3169246..af17563 100644
--- a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
Computation times
=================
-**00:40.720** total execution time for **tutorials_autotvm** files:
+**00:41.697** total execution time for **tutorials_autotvm** files:
-- **00:40.047**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.173**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.170**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.167**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.163**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:41.028**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.176**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.171**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.162**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.161**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index a677631..213b0c0 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -76,7 +76,7 @@ template is very general. Actually this template can be easily modified
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 :doc:`autotvm tutorial <tune_simple_template>`
+tutorials and :ref:`autotvm tutorial <tutorial-autotvm-matmul-x86>`
It is worth noting that the search space for a conv2d operator
can be very large (at the level of 10^9 for some input shapes)
@@ -242,26 +242,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 6 GFLOPS: 90.13/90.13 result: MeasureResult(costs=(0.0025684365384615387,), error_no=0, all_cost=1.6944539546966553, timestamp=1623018908.8711293) [('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: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 8 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 9 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 10 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 99: 0x00000000005099ff\n 98: 0x0000000000507cd3\n 97: _PyEval_EvalFrameDefault\n 96: 0x000000000050a3fc\n 95: 0x00000000005096c7\n 94: _PyEval_EvalFrameDefault\n 93: 0x000000000050a3fc\n 92: 0x00000000005096c7\n 91: _PyEval_EvalFrameDefault\n 90: 0x000000000050a3fc\n 89: 0x00000000005096c7\n 88: _PyEval_EvalFrameDefault\n 87: 0x000000000050a532\n 86: _PyObject [...]
- No: 11 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 12 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 13 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 14 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 15 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 16 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 17 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 18 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1150\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
- No: 19 GFLOPS: 0.00/90.13 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 99: 0x00000000005099ff\n 98: 0x0000000000507cd3\n 97: _PyEval_EvalFrameDefault\n 96: 0x000000000050a3fc\n 95: 0x00000000005096c7\n 94: _PyEval_EvalFrameDefault\n 93: 0x000000000050a3fc\n 92: 0x00000000005096c7\n 91: _PyEval_EvalFrameDefault\n 90: 0x000000000050a3fc\n 89: 0x00000000005096c7\n 88: _PyEval_EvalFrameDefault\n 87: 0x000000000050a532\n 86: _PyObject [...]
- No: 20 GFLOPS: 180.87/180.87 result: MeasureResult(costs=(0.0012799667721518986,), error_no=0, all_cost=1.325697422027588, timestamp=1623018925.593863) [('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: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 6 GFLOPS: 108.76/108.76 result: MeasureResult(costs=(0.0021284664444444447,), error_no=0, all_cost=1.626126766204834, timestamp=1623307307.2898288) [('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: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 8 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 9 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 10 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 99: 0x00000000005099ff\n 98: 0x0000000000507cd3\n 97: _PyEval_EvalFrameDefault\n 96: 0x000000000050a3fc\n 95: 0x00000000005096c7\n 94: _PyEval_EvalFrameDefault\n 93: 0x000000000050a3fc\n 92: 0x00000000005096c7\n 91: _PyEval_EvalFrameDefault\n 90: 0x000000000050a3fc\n 89: 0x00000000005096c7\n 88: _PyEval_EvalFrameDefault\n 87: 0x000000000050a532\n 86: _PyObject [...]
+ No: 11 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 12 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 13 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 14 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 15 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 16 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 17 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 18 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n 20: TVMFuncCall\n at /workspace/src/runtime/c_runtime_api.cc:474\n 19: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const\n at /workspace/include/tvm/runtime/packed_func.h:1151\n 18: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRet [...]
+ No: 19 GFLOPS: 0.00/108.76 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n 99: 0x00000000005099ff\n 98: 0x0000000000507cd3\n 97: _PyEval_EvalFrameDefault\n 96: 0x000000000050a3fc\n 95: 0x00000000005096c7\n 94: _PyEval_EvalFrameDefault\n 93: 0x000000000050a3fc\n 92: 0x00000000005096c7\n 91: _PyEval_EvalFrameDefault\n 90: 0x000000000050a3fc\n 89: 0x00000000005096c7\n 88: _PyEval_EvalFrameDefault\n 87: 0x000000000050a532\n 86: _PyObject [...]
+ No: 20 GFLOPS: 179.84/179.84 result: MeasureResult(costs=(0.0012872528412698413,), error_no=0, all_cost=1.5482125282287598, timestamp=1623307324.5833743) [('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
@@ -314,7 +314,7 @@ 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: 0.001361
+ Time cost of this operator: 0.001358
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
index 56b3a82..a603d94 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
@@ -295,6 +295,10 @@ We will introduce a more sophisticated tuning scheduler in the future.
tuner_obj = XGBTuner(tsk, loss_type="rank")
elif tuner == "xgb_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+ elif tuner == "xgb_itervar":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
+ elif tuner == "xgb_curve":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
elif tuner == "ga":
tuner_obj = GATuner(tsk, pop_size=50)
elif tuner == "random":
@@ -308,7 +312,7 @@ We will introduce a more sophisticated tuning scheduler in the future.
if os.path.isfile(tmp_log_file):
tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
- # do tuning
+ # process tuning
tsk_trial = min(n_trial, len(tsk.config_space))
tuner_obj.tune(
n_trial=tsk_trial,
diff --git a/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt b/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
index 1666130..06e7912 100644
--- a/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
@@ -521,7 +521,7 @@ 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
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index 5c6e797..cfcb0f9 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:22.050** total execution time for **tutorials_dev** files:
+**00:22.263** total execution time for **tutorials_dev** files:
-- **00:21.493**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:00.365**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.192**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:21.735**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:00.353**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.175**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
index 818ca3b..5bc7dc6 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -417,7 +417,7 @@ Execute on TVM
TVM prediction top-1: tiger cat
Evaluate inference time cost...
- Mean inference time (std dev): 4.78 ms (0.46 ms)
+ Mean inference time (std dev): 7.40 ms (1.26 ms)
diff --git a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
index 6b742ae..dc512d8 100644
--- a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
@@ -246,7 +246,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 54.011 seconds)
+ **Total running time of the script:** ( 4 minutes 50.492 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
index 8285ebc..656ad9c 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -350,7 +350,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 14.218015849999999
+ Elapsed average ms: 12.74692235
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index 6609fdb..8b005de 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
@@ -368,7 +368,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 33.370725379999996
+ Elapsed average ms: 33.41605432000001
@@ -401,7 +401,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 40.141 seconds)
+ **Total running time of the script:** ( 1 minutes 40.653 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index 95c499e..d42fc8a 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -199,7 +199,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 11.732 seconds)
+ **Total running time of the script:** ( 1 minutes 11.247 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index bb9c1d8..04bad50 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -142,7 +142,7 @@ provides a static definition of the input size.
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/onnx.py:3585: UserWarning: Mismatched attribute type in ' : kernel_shape'
+ /workspace/docs/../python/tvm/relay/frontend/onnx.py:3591: UserWarning: Mismatched attribute type in ' : kernel_shape'
==> Context: Bad node spec: input: "1" input: "2" output: "11" op_type: "Conv" attribute { name: "kernel_shape" ints: 5 ints: 5 } attribute { name: "strides" ints: 1 ints: 1 } attribute { name: "pads" ints: 2 ints: 2 ints: 2 ints: 2 } attribute { name: "dilations" ints: 1 ints: 1 } attribute { name: "group" i: 1 }
warnings.warn(str(e))
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index bee37e2..ede0246 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -193,9 +193,9 @@ Results:
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:3482: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
+ /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:520: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
"will be used for operator %s." % node.name
- /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:1002: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
+ /workspace/docs/../python/tvm/relay/frontend/tensorflow_ops.py:994: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
Tensorflow protobuf imported to relay frontend.
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index 7d1a086..b7759ed 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
Computation times
=================
-**10:41.206** total execution time for **tutorials_frontend** files:
+**10:37.232** total execution time for **tutorials_frontend** files:
-- **04:54.011**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **01:40.141**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:11.732**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:26.443**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:23.040**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:19.237**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:18.113**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:18.061**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:12.910**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:11.651**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:10.039**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:08.734**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:07.398**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:07.251**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:07.140**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:02.920**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.359**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
-- **00:00.857**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:00.168**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
+- **04:50.492**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **01:40.653**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:11.247**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:26.145**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:22.573**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:19.146**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:18.671**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:17.904**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:13.167**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:11.902**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:10.133**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:08.540**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:07.426**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:07.188**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:07.103**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:02.577**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.362**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:00.839**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:00.166**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/tutorials/get_started/tune_matmul_x86.rst.txt b/docs/_sources/tutorials/get_started/auto_scheduler_matmul_x86.rst.txt
similarity index 95%
rename from docs/_sources/tutorials/get_started/tune_matmul_x86.rst.txt
rename to docs/_sources/tutorials/get_started/auto_scheduler_matmul_x86.rst.txt
index 6199d7d..8a32653 100644
--- a/docs/_sources/tutorials/get_started/tune_matmul_x86.rst.txt
+++ b/docs/_sources/tutorials/get_started/auto_scheduler_matmul_x86.rst.txt
@@ -1,10 +1,10 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_tutorials_get_started_tune_matmul_x86.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorials_get_started_auto_scheduler_matmul_x86.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_tutorials_get_started_tune_matmul_x86.py:
+.. _sphx_glr_tutorials_get_started_auto_scheduler_matmul_x86.py:
Optimizing Operators with Auto-scheduling
@@ -14,7 +14,7 @@ Optimizing Operators with Auto-scheduling
In this tutorial, we will show how TVM's Auto Scheduling feature can find
optimal schedules without the need for writing a custom template.
-Different from the template-based :ref:`<autotvm_matmul>` which relies on
+Different from the template-based :doc:`AutoTVM <autotvm_matmul_x86>` which relies on
manual templates to define the search space, the auto-scheduler does not
require any templates. Users only need to write the computation declaration
without any schedule commands or templates. The auto-scheduler can
@@ -213,9 +213,9 @@ operator fusion.
Lowered TIR:
primfn(A_1: handle, B_1: handle, C_1: handle, out_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {out: Buffer(out_2: Pointer(float32), float32, [1024, 1024], []),
A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
- out: Buffer(out_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C, out_1: out} {
attr [auto_scheduler_layout_transform: Pointer(float32)] "storage_scope" = "global";
@@ -299,7 +299,7 @@ We build the binary and check its correctness and performance.
.. code-block:: none
- Execution time of this operator: 19.541 ms
+ Execution time of this operator: 17.525 ms
@@ -408,7 +408,7 @@ Expression (TE) language that demonstrates how TVM can optimize computational
operations.
-.. _sphx_glr_download_tutorials_get_started_tune_matmul_x86.py:
+.. _sphx_glr_download_tutorials_get_started_auto_scheduler_matmul_x86.py:
.. only :: html
@@ -420,13 +420,13 @@ operations.
.. container:: sphx-glr-download
- :download:`Download Python source code: tune_matmul_x86.py <tune_matmul_x86.py>`
+ :download:`Download Python source code: auto_scheduler_matmul_x86.py <auto_scheduler_matmul_x86.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: tune_matmul_x86.ipynb <tune_matmul_x86.ipynb>`
+ :download:`Download Jupyter notebook: auto_scheduler_matmul_x86.ipynb <auto_scheduler_matmul_x86.ipynb>`
.. only:: html
diff --git a/docs/_sources/tutorials/get_started/autotvm_matmul.rst.txt b/docs/_sources/tutorials/get_started/autotvm_matmul_x86.rst.txt
similarity index 94%
rename from docs/_sources/tutorials/get_started/autotvm_matmul.rst.txt
rename to docs/_sources/tutorials/get_started/autotvm_matmul_x86.rst.txt
index 7bb9e01..05e2d37 100644
--- a/docs/_sources/tutorials/get_started/autotvm_matmul.rst.txt
+++ b/docs/_sources/tutorials/get_started/autotvm_matmul_x86.rst.txt
@@ -1,23 +1,24 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_tutorials_get_started_autotvm_matmul.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorials_get_started_autotvm_matmul_x86.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_tutorials_get_started_autotvm_matmul.py:
+.. _sphx_glr_tutorials_get_started_autotvm_matmul_x86.py:
-Optimizing Operators with Templates and AutoTVM
-===============================================
+.. _tutorial-autotvm-matmul-x86:
+
+Optimizing Operators with Schedule Templates and AutoTVM
+========================================================
**Authors**:
`Lianmin Zheng <https://github.com/merrymercy>`_,
`Chris Hoge <https://github.com/hogepodge>`_
-In this tutorial, we will now show how the TVM Template Extension (TE) language
-can be used to write scheduling templates that can be searched by AutoTVM to
-find optimal configurations of scheduling variables. This process is called
-Auto-Tuning, and builds on TE to help automate the process of optimizing
-operations.
+In this tutorial, we show how the TVM Tensor Expression (TE) language
+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
multiplication using TE <tensor_expr_get_started>`.
@@ -440,12 +441,12 @@ 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
-Expressions <tensor_expr_get_started>_` tutorial In the upcoming sections, we
+Expressions <tensor_expr_get_started>_` tutorial. In the upcoming sections, we
will demonstate the AutoScheduler, a method for TVM to optimize common
operators without the need for the user to provide a user-defined template.
-.. _sphx_glr_download_tutorials_get_started_autotvm_matmul.py:
+.. _sphx_glr_download_tutorials_get_started_autotvm_matmul_x86.py:
.. only :: html
@@ -457,13 +458,13 @@ operators without the need for the user to provide a user-defined template.
.. container:: sphx-glr-download
- :download:`Download Python source code: autotvm_matmul.py <autotvm_matmul.py>`
+ :download:`Download Python source code: autotvm_matmul_x86.py <autotvm_matmul_x86.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: autotvm_matmul.ipynb <autotvm_matmul.ipynb>`
+ :download:`Download Jupyter notebook: autotvm_matmul_x86.ipynb <autotvm_matmul_x86.ipynb>`
.. only:: html
diff --git a/docs/_sources/tutorials/get_started/auto_tuning_with_python.rst.txt b/docs/_sources/tutorials/get_started/autotvm_relay_x86.rst.txt
similarity index 86%
rename from docs/_sources/tutorials/get_started/auto_tuning_with_python.rst.txt
rename to docs/_sources/tutorials/get_started/autotvm_relay_x86.rst.txt
index a04639d..20f4075 100644
--- a/docs/_sources/tutorials/get_started/auto_tuning_with_python.rst.txt
+++ b/docs/_sources/tutorials/get_started/autotvm_relay_x86.rst.txt
@@ -1,14 +1,14 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_tutorials_get_started_auto_tuning_with_python.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorials_get_started_autotvm_relay_x86.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_tutorials_get_started_auto_tuning_with_python.py:
+.. _sphx_glr_tutorials_get_started_autotvm_relay_x86.py:
-Compiling and Optimizing a Model with the Python AutoScheduler
-==============================================================
+Compiling and Optimizing a Model with the Python Interface (AutoTVM)
+====================================================================
**Author**:
`Chris Hoge <https://github.com/hogepodge>`_
@@ -229,7 +229,7 @@ runtime module from the library.
.. code-block:: none
-
...47%, 0.01 MB, 483 KB/s, 0 seconds passed
...94%, 0.02 MB, 947 KB/s, 0 seconds passed
...100%, 0.02 MB, 1404 KB/s, 0 seconds passed
+
...47%, 0.01 MB, 640 KB/s, 0 seconds passed
...94%, 0.02 MB, 1253 KB/s, 0 seconds passed
...100%, 0.02 MB, 1857 KB/s, 0 seconds passed
@@ -296,7 +296,7 @@ standard deviation.
.. code-block:: none
- {'mean': 84.46302120108157, 'median': 84.24859350197949, 'std': 0.5331460822777307}
+ {'mean': 87.26180178113282, 'median': 86.47694665123709, 'std': 1.555106753601749}
@@ -412,6 +412,7 @@ In the simplest form, tuning requires you to provide three things:
repeat=repeat,
timeout=timeout,
min_repeat_ms=min_repeat_ms,
+ enable_cpu_cache_flush=True,
)
# Create a simple structure for holding tuning options. We use an XGBoost
@@ -490,31 +491,31 @@ In the simplest form, tuning requires you to provide three things:
.. code-block:: none
-
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 30.75/ 68.58 GFLOPS | Progress: (10/10) | 12.72 s Done.
-
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 5.63/ 48.86 GFLOPS | Progress: (10/10) | 12.70 s Done.
-
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 56.34/ 69.89 GFLOPS | Progress: (10/10) | 4.08 s Done.
-
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 35.63/ 99.00 GFLOPS | Progress: (10/10) | 4.33 s Done.
-
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 73.77/ 73.77 GFLOPS | Progress: (10/10) | 4.04 s Done.
-
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 56.33/ 101.07 GFLOPS | Progress: (10/10) | 4.78 s Done.
-
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 77.71/ 91.40 GFLOPS | Progress: (10/10) | 6.37 s Done.
-
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 41.30/ 71.79 GFLOPS | Progress: (10/10) | 5.31 s Done.
-
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 56.41/ 98.42 GFLOPS | Progress: (10/10) | 4.44 s Done.
-
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 50.77/ 78.14 GFLOPS | Progress: (10/10) | 3.86 s Done.
-
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 71.29/ 104.68 GFLOPS | Progress: (10/10) | 4.66 s Done.
-
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 47.92/ 86.42 GFLOPS | Progress: (10/10) | 3.63 s Done.
-
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 44.70/ 58.88 GFLOPS | Progress: (10/10) | 12.93 s Done.
-
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 76.40/ 109.70 GFLOPS | Progress: (10/10) | 3.68 s Done.
-
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 73.47/ 73.47 GFLOPS | Progress: (10/10) | 4.28 s Done.
-
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 63.63/ 90.05 GFLOPS | Progress: (10/10) | 4.25 s Done.
-
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 77.19/ 81.97 GFLOPS | Progress: (10/10) | 5.67 s Done.
-
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 56.11/ 69.52 GFLOPS | Progress: (10/10) | 12.79 s Done.
-
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 38.56/ 106.38 GFLOPS | Progress: (10/10) | 4.07 s Done.
-
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 57.57/ 58.49 GFLOPS | Progress: (10/10) | 3.58 s Done.
-
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 48.15/ 49.04 GFLOPS | Progress: (10/10) | 3.72 s Done.
-
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 55.31/ 89.96 GFLOPS | Progress: (10/10) | 4.68 s Done.
-
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 27.84/ 106.65 GFLOPS | Progress: (10/10) | 3.86 s Done.
-
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 84.77/ 84.77 GFLOPS | Progress: (10/10) | 8.54 s Done.
-
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 25/25] Current/Best: 71.96/ 107.68 GFLOPS | Progress: (10/10) | 6.63 s Done.
+
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 42.20/ 46.65 GFLOPS | Progress: (10/10) | 13.01 s Done.
+
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 41.51/ 41.51 GFLOPS | Progress: (10/10) | 11.66 s Done.
+
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 34.45/ 62.36 GFLOPS | Progress: (10/10) | 6.26 s Done.
+
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 10.98/ 111.38 GFLOPS | Progress: (10/10) | 7.29 s Done.
+
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 57.91/ 68.41 GFLOPS | Progress: (10/10) | 4.28 s Done.
+
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 89.34/ 92.15 GFLOPS | Progress: (10/10) | 5.34 s Done.
+
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 78.30/ 91.91 GFLOPS | Progress: (10/10) | 5.96 s Done.
+
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 68.40/ 76.25 GFLOPS | Progress: (10/10) | 3.78 s Done.
+
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 21.48/ 95.98 GFLOPS | Progress: (10/10) | 4.55 s Done.
+
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 71.68/ 71.68 GFLOPS | Progress: (10/10) | 5.04 s Done.
+
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 96.64/ 96.64 GFLOPS | Progress: (10/10) | 4.77 s Done.
+
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 86.06/ 87.73 GFLOPS | Progress: (10/10) | 4.75 s Done.
+
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 55.04/ 58.46 GFLOPS | Progress: (10/10) | 8.75 s Done.
+
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 36.51/ 109.11 GFLOPS | Progress: (10/10) | 4.86 s Done.
+
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 49.03/ 72.56 GFLOPS | Progress: (10/10) | 4.59 s Done.
+
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 99.69/ 101.22 GFLOPS | Progress: (10/10) | 3.90 s Done.
+
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 22.12/ 94.84 GFLOPS | Progress: (10/10) | 5.57 s Done.
+
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 68.71/ 68.71 GFLOPS | Progress: (10/10) | 6.31 s Done.
+
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 90.57/ 101.14 GFLOPS | Progress: (10/10) | 4.84 s Done.
+
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 57.69/ 73.52 GFLOPS | Progress: (10/10) | 5.36 s Done.
+
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 55.24/ 55.24 GFLOPS | Progress: (10/10) | 3.70 s Done.
+
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 65.76/ 104.18 GFLOPS | Progress: (10/10) | 4.48 s Done.
+
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 51.16/ 98.47 GFLOPS | Progress: (10/10) | 8.39 s Done.
+
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 24/25] Current/Best: 67.09/ 92.15 GFLOPS | Progress: (10/10) | 5.94 s Done.
+
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 25/25] Current/Best: 74.86/ 88.69 GFLOPS | Progress: (10/10) | 5.09 s Done.
@@ -657,8 +658,8 @@ improvement in comparing the optimized model to the unoptimized model.
.. code-block:: none
- optimized: {'mean': 82.90143675054424, 'median': 82.53361214883626, 'std': 2.258990517555402}
- unoptimized: {'mean': 84.46302120108157, 'median': 84.24859350197949, 'std': 0.5331460822777307}
+ optimized: {'mean': 76.02714203065261, 'median': 75.40562159847468, 'std': 1.3964067266195654}
+ unoptimized: {'mean': 87.26180178113282, 'median': 86.47694665123709, 'std': 1.555106753601749}
@@ -678,10 +679,10 @@ profiling/benchmarking.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 26.284 seconds)
+ **Total running time of the script:** ( 3 minutes 29.557 seconds)
-.. _sphx_glr_download_tutorials_get_started_auto_tuning_with_python.py:
+.. _sphx_glr_download_tutorials_get_started_autotvm_relay_x86.py:
.. only :: html
@@ -693,13 +694,13 @@ profiling/benchmarking.
.. container:: sphx-glr-download
- :download:`Download Python source code: auto_tuning_with_python.py <auto_tuning_with_python.py>`
+ :download:`Download Python source code: autotvm_relay_x86.py <autotvm_relay_x86.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: auto_tuning_with_python.ipynb <auto_tuning_with_python.ipynb>`
+ :download:`Download Jupyter notebook: autotvm_relay_x86.ipynb <autotvm_relay_x86.ipynb>`
.. only:: html
diff --git a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
index 29c429b..63618c3 100644
--- a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.813e-07 secs/op
+ 1.743e-07 secs/op
diff --git a/docs/_sources/tutorials/get_started/install.rst.txt b/docs/_sources/tutorials/get_started/install.rst.txt
index 9887cdb..62f9206 100644
--- a/docs/_sources/tutorials/get_started/install.rst.txt
+++ b/docs/_sources/tutorials/get_started/install.rst.txt
@@ -15,8 +15,8 @@ Installing TVM
Depending on your needs and your working environment, there are a few different
methods for installing TVM. These include:
- * Installing from source
- * Installing from third-party binary package.
+* Installing from source
+* Installing from third-party binary package.
Installing from Source
----------------------
diff --git a/docs/_sources/tutorials/get_started/introduction.rst.txt b/docs/_sources/tutorials/get_started/introduction.rst.txt
index c7d8d3c..cef6079 100644
--- a/docs/_sources/tutorials/get_started/introduction.rst.txt
+++ b/docs/_sources/tutorials/get_started/introduction.rst.txt
@@ -11,7 +11,8 @@ Introduction
============
**Authors**:
`Jocelyn Shiue <https://github.com/>`_,
-`Chris Hoge <https://github.com/hogepodge>`_
+`Chris Hoge <https://github.com/hogepodge>`_,
+`Lianmin Zheng <https://github.com/merrymercy>`_
Apache TVM is an open source machine learning compiler framework for CPUs,
GPUs, and machine learning accelerators. It aims to enable machine learning
@@ -27,11 +28,11 @@ Contents
#. :doc:`Introduction <introduction>`
#. :doc:`Installing TVM <install>`
-#. :doc:`Compiling and Optimizing a Model with TVMC <tvmc_command_line_driver>`
-#. :doc:`Compiling and Optimizing a Model with the Python AutoScheduler <auto_tuning_with_python>`
-#. :doc:`Working with Operators Using Tensor Expressions <tensor_expr_get_started>`
-#. :doc:`Optimizing Operators with Templates and AutoTVM <autotvm_matmul>`
-#. :doc:`Optimizing Operators with AutoScheduling <tune_matmul_x86>`
+#. :doc:`Compiling and Optimizing a Model with the Command Line Interface <tvmc_command_line_driver>`
+#. :doc:`Compiling and Optimizing a Model with the Python Interface <autotvm_relay_x86>`
+#. :doc:`Working with Operators Using Tensor Expression <tensor_expr_get_started>`
+#. :doc:`Optimizing Operators with Templates and AutoTVM <autotvm_matmul_x86>`
+#. :doc:`Optimizing Operators with Template-free AutoScheduler <auto_scheduler_matmul_x86>`
#. :doc:`Cross Compilation and Remote Procedure Calls (RPC) <cross_compilation_and_rpc>`
#. :doc:`Compiling Deep Learning Models for GPUs <relay_quick_start>`
@@ -41,18 +42,18 @@ An Overview of TVM and Model Optimization
The diagram below illustrates the steps a machine model takes as it is
transformed with the TVM optimizing compiler framework.
-.. image:: https://raw.githubusercontent.com/hogepodge/web-data/c339ebbbae41f3762873147c1e920a53a08963dd/images/getting_started/overview.png
+.. image:: https://raw.githubusercontent.com/apache/tvm-site/main/images/tutorial/overview.png
:width: 100%
:alt: A High Level View of TVM
1. Import the model from a framework like *Tensorflow*, *Pytorch*, or *Onnx*.
The importer layer is where TVM can ingest models from other frameworks, like
- ONNX, Tensorflow, or PyTorch. The level of support that TVM offers for each
+ Tensorflow, PyTorch, or ONNX. The level of support that TVM offers for each
frontend varies as we are constantly improving the open source project. If
you're having issues importing your model into TVM, you may want to try
converting it to ONNX.
-2. Translate to *Relay*, TVM's high level model language.
+2. Translate to *Relay*, TVM's high-level model language.
A model that has been imported into TVM is represented in Relay. Relay is a
functional language and intermediate representation (IR) for neural networks.
It has support for:
@@ -62,46 +63,47 @@ transformed with the TVM optimizing compiler framework.
differentiable language
- Ability to allow the user to mix the two programming styles
- Relay applies several high-level optimization to the model, after which
- is runs the Relay Fusion Pass. To aid in the process of converting to
- Relay, TVM includes a Tensor Operator Inventory (TOPI) that has pre-defined
- templates of common computations.
+ Relay applies graph-level optimization passes to optimize the model.
3. Lower to *Tensor Expression* (TE) representation. Lowering is when a
higher-level representation is transformed into a lower-level
- representation. In Relay Fusion Pass, the model is lowered from the
- higher-level Relay representation into a smaller set of subgraphs, where
- each node is a task. A task is a collection of computation templates,
- expressed in TE, where there parameters of the template can control how
- the computation is carried out on hardware. The specific ordering of compuation,
- defined by parameters to the TE template, is called a schedule.
-
-4. Search for optimized schedule using *AutoTVM* or *AutoScheduler* for each
- task through tuning. Tuning is the process of searching the TE parameter
- space for a schedule that is optimized for target hardware. There are
- couple of optimization options available, each requiring varying levels of
- user interaction. The optimization options include:
-
- - **AutoTVM**: The user specifies a search template for the schedule of a TE task,
- or TE subraph. AutoTVM directs the search of the parameter space defined by the
- template to produce an optimized configuration. AutoTVM requires users to
- define manually templates for each operator as part of the TOPI.
- - **Ansor/AutoSchedule**: Using a TVM Operator Inventory (TOPI) of operations,
- Ansor can automatically search an optimization space with much less
- intervention and guidance from the end user. Ansor depends on TE templates to
- guide the search.
-
-5. Choose the optimal configuration for the model. After tuning, an optimal schedule
- for each task is chosen. Regardless if it is AutoTVM or AutoSchedule,
- schedule records in JSON format are produced that are referred to by this step
- to build an optimized model.
-
-6. Lower to a hardware specific compiler. After selecting an optimized configuration
- based on the tuning step, the model is then lowered to a representation
- expected by the target compiler for the hardware platform. This is the
- final code generation phase with the intention of producing an optimized
- model that can be deployed into production. TVM supports a number of
- different compiler backends including:
+ representation. After applying the high-level optimizations, Relay
+ runs FuseOps pass to partition the model into many small subgraphs and lowers
+ the subgraphs to TE representation. Tensor Expression (TE) is a
+ domain-specific language for describing tensor computations.
+ TE also provides several *schedule* primitives to specify low-level loop
+ optimizations, such as tiling, vectorization, parallelization,
+ unrolling, and fusion.
+ To aid in the process of converting Relay representation into TE representation,
+ TVM includes a Tensor Operator Inventory (TOPI) that has pre-defined
+ templates of common tensor operators (e.g., conv2d, transpose).
+
+4. Search for the best schedule using the auto-tuning module *AutoTVM* or *AutoScheduler*.
+ A schedule specifies the low-level loop optimizations for an operator or
+ subgraph defined in TE. Auto-tuning modules search for the best schedule
+ and compare them with cost models and on-device measurements.
+ There are two auto-tuning modules in TVM.
+
+ - **AutoTVM**: A template-based auto-tuning module. It runs search algorithms
+ to find the best values for the tunable knobs in a user-defined template.
+ For common operators, their templates are already provided in TOPI.
+ - **AutoScheduler (a.k.a. Ansor)**: A template-free auto-tuning module.
+ It does not require pre-defined schedule templates. Instead, it generates
+ the search space automatically by analyzing the computation definition.
+ It then searches for the best schedule in the generated search space.
+
+5. Choose the optimal configurations for model compilation. After tuning, the
+ auto-tuning module generates tuning records in JSON format. This step
+ picks the best schedule for each subgraph.
+
+6. Lower to Tensor Intermediate Representation (TIR), TVM's low-level
+ intermediate representation. After selecting the optimal configurations
+ based on the tuning step, each TE subgraph is lowered to TIR and be
+ optimized by low-level optimization passes. Next, the optimized TIR is
+ lowered to the target compiler of the hardware platform.
+ This is the final code generation phase to produce an optimized model
+ that can be deployed into production. TVM supports several different
+ compiler backends including:
- LLVM, which can target arbitrary microprocessor architecture including
standard x86 and ARM processors, AMDGPU and NVPTX code generation, and any
diff --git a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
index 5b70742..13ca881 100644
--- a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
@@ -225,7 +225,7 @@ in this example. Then the machine code will be generated as the module library.
.. code-block:: none
-
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diff --git a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
index 6dbe827..b9464c3 100644
--- a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**05:31.252** total execution time for **tutorials_get_started** files:
+**06:03.708** total execution time for **tutorials_get_started** files:
-- **03:26.284**: :ref:`sphx_glr_tutorials_get_started_auto_tuning_with_python.py` (``auto_tuning_with_python.py``)
-- **01:23.738**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:19.709**: :ref:`sphx_glr_tutorials_get_started_tune_matmul_x86.py` (``tune_matmul_x86.py``)
-- **00:13.013**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:08.193**: :ref:`sphx_glr_tutorials_get_started_autotvm_matmul.py` (``autotvm_matmul.py``)
-- **00:00.205**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **03:29.557**: :ref:`sphx_glr_tutorials_get_started_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:23.171**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:31.377**: :ref:`sphx_glr_tutorials_get_started_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:26.615**: :ref:`sphx_glr_tutorials_get_started_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **00:12.620**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.255**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
- **00:00.039**: :ref:`sphx_glr_tutorials_get_started_introduction.py` (``introduction.py``)
-- **00:00.036**: :ref:`sphx_glr_tutorials_get_started_install.py` (``install.py``)
-- **00:00.035**: :ref:`sphx_glr_tutorials_get_started_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.038**: :ref:`sphx_glr_tutorials_get_started_install.py` (``install.py``)
+- **00:00.036**: :ref:`sphx_glr_tutorials_get_started_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
diff --git a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
index 25d8344..8894969 100644
--- a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
@@ -9,22 +9,19 @@
.. _tutorial-tensor-expr-get-started:
-Working with Operators Using Tensor Expressions
-===============================================
+Working with Operators Using Tensor Expression
+==============================================
**Author**: `Tianqi Chen <https://tqchen.github.io>`_
In this tutorial we will turn our attention to how TVM works with Tensor
-Expressions (TE) to create a space to search for performant configurations. TE
+Expression (TE) to define tensor computations and apply loop optimizations. TE
describes tensor computations in a pure functional language (that is each
expression has no side effects). When viewed in context of the TVM as a whole,
Relay describes a computation as a set of operators, and each of these
operators can be represented as a TE expression where each TE expression takes
-input tensors and produces an output tensor. It's important to note that the
-tensor isn't necessarily a fully materialized array, rather it is a
-representation of a computation. If you want to produce a computation from a
-TE, you will need to use the scheduling features of TVM.
+input tensors and produces an output tensor.
-This is an introductory tutorial to the Tensor expression language in TVM. TVM
+This is an introductory tutorial to the Tensor Expression language in TVM. TVM
uses a domain specific tensor expression for efficient kernel construction. We
will demonstrate the basic workflow with two examples of using the tensor expression
language. The first example introduces TE and scheduling with vector
@@ -37,8 +34,8 @@ Example 1: Writing and Scheduling Vector Addition in TE for CPU
---------------------------------------------------------------
Let's look at an example in Python in which we will implement a TE for
-vector addition, followed by a schedule targeted towards a CPU. We begin by initializing a TVM
-environment.
+vector addition, followed by a schedule targeted towards a CPU.
+We begin by initializing a TVM environment.
.. code-block:: default
@@ -53,7 +50,8 @@ environment.
# and specify it. If you're using llvm, you can get this information from the
# command ``llc --version`` to get the CPU type, and you can check
# ``/proc/cpuinfo`` for additional extensions that your processor might
- # support. For example, ``tgt = "llvm -mcpu=`skylake`
+ # support. For example, you can use "llvm -mcpu=skylake-avx512" for CPUs with
+ # AVX-512 instructions.
tgt = tvm.target.Target(target="llvm", host="llvm")
@@ -68,7 +66,7 @@ Describing the Vector Computation
We describe a vector addition computation. TVM adopts tensor semantics, with
each intermediate result represented as a multi-dimensional array. The user
needs to describe the computation rule that generates the tensors. We first
-define a symbolic variable n to represent the shape. We then define two
+define a symbolic variable ``n`` to represent the shape. We then define two
placeholder Tensors, ``A`` and ``B``, with given shape ``(n,)``. We then
describe the result tensor ``C``, with a ``compute`` operation. The
``compute`` defines a computation, with the output conforming to the
@@ -82,7 +80,6 @@ are only declaring how the computation should be done.
.. code-block:: default
-
n = te.var("n")
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
@@ -96,10 +93,10 @@ are only declaring how the computation should be done.
.. note:: Lambda Functions
-The second argument to the ``te.compute`` method is the function that
-performs the computation. In this example, we're using an anonymous function,
-also known as a ``lambda`` function, to define the computation, in this case
-addition on the ``i``th element of ``A`` and ``B``.
+ The second argument to the ``te.compute`` method is the function that
+ performs the computation. In this example, we're using an anonymous function,
+ also known as a ``lambda`` function, to define the computation, in this case
+ addition on the ``i``th element of ``A`` and ``B``.
Create a Default Schedule for the Computation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -327,7 +324,7 @@ compile and run this new schedule with the parallel operation applied:
.. code-block:: none
- parallel: 0.000004
+ parallel: 0.000002
@@ -380,7 +377,7 @@ factor to be the number of threads on your CPU.
.. code-block:: none
- vector: 0.000015
+ vector: 0.000014
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
@@ -428,10 +425,10 @@ We can now compare the different schedules
.. code-block:: none
Operator Timing Performance
- numpy 4.8916798550635575e-06 1.0
- naive 3.6328999999999996e-06 0.7426692072334724
- parallel 3.5216999999999997e-06 0.7199367301918908
- vector 1.5266999999999998e-05 3.1210137319588824
+ numpy 4.978140350431204e-06 1.0
+ naive 3.5808e-06 0.7193047499534305
+ parallel 2.4637e-06 0.49490368422147746
+ vector 1.41518e-05 2.8427884719590475
@@ -484,8 +481,6 @@ will target compilation of the vector addition to GPUs.
bx, tx = s[C].split(C.op.axis[0], factor=64)
- xXXXXXXXx
-
################################################################################
# Finally we must bind the iteration axis bx and tx to threads in the GPU
# compute grid. The naive schedule is not valid for GPUs, and these are
@@ -816,7 +811,7 @@ matrix multiplication.
.. code-block:: none
- Numpy running time: 0.007216
+ Numpy running time: 0.006941
@@ -872,7 +867,7 @@ optimizations.
.. code-block:: none
- none: 5.919446
+ none: 5.871126
@@ -968,7 +963,7 @@ schedule.
.. code-block:: none
- blocking: 0.113877
+ blocking: 0.116482
@@ -1056,7 +1051,7 @@ already cache friendly from our previous optimizations.
.. code-block:: none
- vectorization: 0.125302
+ vectorization: 0.129899
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1124,7 +1119,7 @@ more cache friendly.
.. code-block:: none
- loop permutation: 0.061251
+ loop permutation: 0.059711
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1217,7 +1212,7 @@ optimized schedule.
.. code-block:: none
- array packing: 0.062319
+ array packing: 0.060550
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1305,7 +1300,7 @@ to `C` when all the block results are ready.
.. code-block:: none
- block caching: 0.059286
+ block caching: 0.058847
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
@@ -1384,11 +1379,11 @@ of thread-level parallelization.
.. code-block:: none
- parallelization: 0.017225
+ parallelization: 0.015944
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [packedB: Pointer(float32x32)] "storage_scope" = "global";
@@ -1458,13 +1453,13 @@ working, we can compare the results.
.. code-block:: none
Operator Timing Performance
- none 5.919445745100001 1.0
- blocking 0.1138770327 0.019237786374554602
- vectorization 0.1253015306 0.02116778090308913
- loop permutation 0.061251044899999996 0.010347429056293385
- array packing 0.06231947880000001 0.010527924654362586
- block caching 0.0592864929 0.010015547984213924
- parallelization 0.017225419 0.002909971598989459
+ none 5.8711263175 1.0
+ blocking 0.11648151089999999 0.019839721477769072
+ vectorization 0.1298985455 0.022124978832905172
+ loop permutation 0.059711437 0.01017035467658374
+ array packing 0.060549881800000004 0.010313162845691065
+ block caching 0.0588466685 0.010023062921435772
+ parallelization 0.0159438366 0.0027156350822288367
@@ -1501,7 +1496,7 @@ the computation for specific platforms.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 23.738 seconds)
+ **Total running time of the script:** ( 1 minutes 23.171 seconds)
.. _sphx_glr_download_tutorials_get_started_tensor_expr_get_started.py:
diff --git a/docs/_sources/tutorials/get_started/tvmc_command_line_driver.rst.txt b/docs/_sources/tutorials/get_started/tvmc_command_line_driver.rst.txt
index 25f0435..0684807 100644
--- a/docs/_sources/tutorials/get_started/tvmc_command_line_driver.rst.txt
+++ b/docs/_sources/tutorials/get_started/tvmc_command_line_driver.rst.txt
@@ -466,7 +466,7 @@ To see what other options are available, please have a look at ``tvmc
--help``.
In the next tutorial, `Compiling and Optimizing a Model with the Python
-AutoScheduler <auto_tuning_with_pyton>`_, we will cover the same compilation
+Interface <auto_tuning_with_pyton>`_, we will cover the same compilation
and optimization steps using the Python interface.
diff --git a/docs/_sources/tutorials/index.rst.txt b/docs/_sources/tutorials/index.rst.txt
index d06ea7a..9a511ef 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -86,9 +86,9 @@ Getting Started With TVM
.. only:: html
- .. figure:: /tutorials/get_started/images/thumb/sphx_glr_auto_tuning_with_python_thumb.png
+ .. figure:: /tutorials/get_started/images/thumb/sphx_glr_autotvm_relay_x86_thumb.png
- :ref:`sphx_glr_tutorials_get_started_auto_tuning_with_python.py`
+ :ref:`sphx_glr_tutorials_get_started_autotvm_relay_x86.py`
.. raw:: html
@@ -98,11 +98,11 @@ Getting Started With TVM
.. toctree::
:hidden:
- /tutorials/get_started/auto_tuning_with_python
+ /tutorials/get_started/autotvm_relay_x86
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial we will turn our attention to how TVM works with Tensor Expressions (TE) to cr...">
+ <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial we will turn our attention to how TVM works with Tensor Expression (TE) to def...">
.. only:: html
@@ -122,13 +122,13 @@ Getting Started With TVM
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will now show how the TVM Template Extension (TE) language can be used to ...">
+ <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we show how the TVM Tensor Expression (TE) language can be used to write sche...">
.. only:: html
- .. figure:: /tutorials/get_started/images/thumb/sphx_glr_autotvm_matmul_thumb.png
+ .. figure:: /tutorials/get_started/images/thumb/sphx_glr_autotvm_matmul_x86_thumb.png
- :ref:`sphx_glr_tutorials_get_started_autotvm_matmul.py`
+ :ref:`sphx_glr_tutorials_get_started_autotvm_matmul_x86.py`
.. raw:: html
@@ -138,17 +138,17 @@ Getting Started With TVM
.. toctree::
:hidden:
- /tutorials/get_started/autotvm_matmul
+ /tutorials/get_started/autotvm_matmul_x86
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This tutorial introduces cross compilation and remote device execution with RPC in TVM.">
+ <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will show how TVM's Auto Scheduling feature can find optimal schedules wit...">
.. only:: html
- .. figure:: /tutorials/get_started/images/thumb/sphx_glr_cross_compilation_and_rpc_thumb.png
+ .. figure:: /tutorials/get_started/images/thumb/sphx_glr_auto_scheduler_matmul_x86_thumb.png
- :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py`
+ :ref:`sphx_glr_tutorials_get_started_auto_scheduler_matmul_x86.py`
.. raw:: html
@@ -158,17 +158,17 @@ Getting Started With TVM
.. toctree::
:hidden:
- /tutorials/get_started/cross_compilation_and_rpc
+ /tutorials/get_started/auto_scheduler_matmul_x86
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to build a neural network with Relay python frontend and generates a run...">
+ <div class="sphx-glr-thumbcontainer" tooltip="This tutorial introduces cross compilation and remote device execution with RPC in TVM.">
.. only:: html
- .. figure:: /tutorials/get_started/images/thumb/sphx_glr_relay_quick_start_thumb.png
+ .. figure:: /tutorials/get_started/images/thumb/sphx_glr_cross_compilation_and_rpc_thumb.png
- :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py`
+ :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py`
.. raw:: html
@@ -178,17 +178,17 @@ Getting Started With TVM
.. toctree::
:hidden:
- /tutorials/get_started/relay_quick_start
+ /tutorials/get_started/cross_compilation_and_rpc
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will show how TVM's Auto Scheduling feature can find optimal schedules wit...">
+ <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to build a neural network with Relay python frontend and generates a run...">
.. only:: html
- .. figure:: /tutorials/get_started/images/thumb/sphx_glr_tune_matmul_x86_thumb.png
+ .. figure:: /tutorials/get_started/images/thumb/sphx_glr_relay_quick_start_thumb.png
- :ref:`sphx_glr_tutorials_get_started_tune_matmul_x86.py`
+ :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py`
.. raw:: html
@@ -198,7 +198,7 @@ Getting Started With TVM
.. toctree::
:hidden:
- /tutorials/get_started/tune_matmul_x86
+ /tutorials/get_started/relay_quick_start
.. raw:: html
<div style='clear:both'></div>
@@ -628,6 +628,26 @@ Tensor Expression and Schedules
.. raw:: html
+ <div class="sphx-glr-thumbcontainer" tooltip="This is an introduction material on how to do reduction in TVM. Associative reduction operators...">
+
+.. only:: html
+
+ .. figure:: /tutorials/language/images/thumb/sphx_glr_reduction_thumb.png
+
+ :ref:`sphx_glr_tutorials_language_reduction.py`
+
+.. raw:: html
+
+ </div>
+
+
+.. toctree::
+ :hidden:
+
+ /tutorials/language/reduction
+
+.. raw:: html
+
<div class="sphx-glr-thumbcontainer" tooltip="While TVM supports basic arithmetic operations. In many cases usually we will need more complic...">
.. only:: html
@@ -745,26 +765,6 @@ Tensor Expression and Schedules
:hidden:
/tutorials/language/tedd
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This is an introduction material on how to do reduction in TVM. Associative reduction operators...">
-
-.. only:: html
-
- .. figure:: /tutorials/language/images/thumb/sphx_glr_reduction_thumb.png
-
- :ref:`sphx_glr_tutorials_language_reduction.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /tutorials/language/reduction
.. raw:: html
<div style='clear:both'></div>
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index 4aa7ae9..dfb78dc 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -449,13 +449,13 @@ of computation of `C`.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
- C: Buffer(C_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
B: Buffer(B_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
- B_2[(i*stride_2)] = ((float32*)A_2[(i*stride)] + 1f32)
- C_2[(i*stride_1)] = ((float32*)B_2[(i*stride_2)]*2f32)
+ B_2[(i*stride_2)] = ((float32*)A_2[(i*stride_1)] + 1f32)
+ C_2[(i*stride)] = ((float32*)B_2[(i*stride_2)]*2f32)
}
}
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index 0cfdd51..0e91283 100644
--- a/docs/_sources/tutorials/language/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**00:04.178** total execution time for **tutorials_language** files:
+**00:04.248** total execution time for **tutorials_language** files:
-- **00:01.550**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.747**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.549**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.542**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:01.605**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.736**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.554**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.550**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
- **00:00.248**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.187**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.178**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
-- **00:00.177**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:00.188**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.185**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:00.181**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/tutorials/language/tensorize.rst.txt b/docs/_sources/tutorials/language/tensorize.rst.txt
index 24ae6a6..77375cf 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -237,8 +237,8 @@ such placeholder can be put to let TVM automatically bind the inferred value for
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
- A: Buffer(A_2: Pointer(float32), float32, [1024, 64], []),
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 64], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
B: Buffer(B_2: Pointer(float32), float32, [512, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, 1024) {
@@ -309,12 +309,12 @@ The importing needs to happen before the tensorized GEMV being executed.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 64], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 64], []),
B: Buffer(B_2: Pointer(float32), float32, [512, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp5lqx02yg/input0.cc'
- source_filename = "/tmp/tmp5lqx02yg/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpgivmczqi/input0.cc'
+ source_filename = "/tmp/tmpgivmczqi/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index fc81652..401864a 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -64,15 +64,15 @@ together in the next schedule procedure.
primfn(A0_1: handle, A1_1: handle, B.v0_1: handle, B.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B.v0: Buffer(B.v0_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- B.v1: Buffer(B.v1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+ buffers = {B.v1: Buffer(B.v1_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B.v0: Buffer(B.v0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, B.v0_1: B.v0, B.v1_1: B.v1} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B.v0_2[((i*stride) + (j*stride_1))] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
- B.v1_2[((i*stride_4) + (j*stride_5))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
+ B.v0_2[((i*stride_2) + (j*stride_3))] = ((float32*)A0_2[((i*stride_4) + (j*stride_5))] + 2f32)
+ B.v1_2[((i*stride) + (j*stride_1))] = ((float32*)A1_2[((i*stride_6) + (j*stride_7))]*3f32)
}
}
}
@@ -135,17 +135,17 @@ with :py:func:`te.comm_reducer` as below:
primfn(idx_1: handle, val_1: handle, T.v0_1: handle, T.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {T.v1: Buffer(T.v1_2: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
- idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_1: int32, stride_2: int32], type="auto"),
- T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m], [stride_3: int32], type="auto"),
+ buffers = {T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
+ T.v1: Buffer(T.v1_2: Pointer(int32), int32, [m], [stride_1: int32], type="auto"),
+ idx: Buffer(idx_2: Pointer(int32), int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
val: Buffer(val_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {idx_1: idx, val_1: val, T.v0_1: T.v0, T.v1_1: T.v1} {
for (i: int32, 0, m) {
- T.v0_2[(i*stride_3)] = -1
- T.v1_2[(i*stride)] = -2147483648
+ T.v0_2[(i*stride)] = -1
+ T.v1_2[(i*stride_1)] = -2147483648
for (k: int32, 0, n) {
- T.v0_2[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v0_2[(i*stride_3)], (int32*)idx_2[((i*stride_1) + (k*stride_2))], dtype=int32)
- T.v1_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v1_2[(i*stride)], (int32*)val_2[((i*stride_4) + (k*stride_5))], dtype=int32)
+ T.v0_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T.v1_2[(i*stride_1)]), (int32*)T.v0_2[(i*stride)], (int32*)idx_2[((i*stride_2) + (k*stride_3))], dtype=int32)
+ T.v1_2[(i*stride_1)] = @tir.if_then_else(((int32*)val_2[((i*stride_4) + (k*stride_5))] <= (int32*)T.v1_2[(i*stride_1)]), (int32*)T.v1_2[(i*stride_1)], (int32*)val_2[((i*stride_4) + (k*stride_5))], dtype=int32)
}
}
}
@@ -193,8 +193,8 @@ in terms of operation.
primfn(A0_1: handle, A1_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ buffers = {A0: Buffer(A0_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ C: Buffer(C_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, C_1: C} {
attr [B.v0: Pointer(float32)] "storage_scope" = "global";
@@ -203,11 +203,11 @@ in terms of operation.
allocate(B.v1, float32, [n]);
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B.v0[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))] + 2f32)
- B.v1[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))]*3f32)
+ B.v0[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))] + 2f32)
+ B.v1[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))]*3f32)
}
for (j_1: int32, 0, n) {
- C_2[((i*stride) + (j_1*stride_1))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
+ C_2[((i*stride_2) + (j_1*stride_3))] = ((float32*)A1_2[((i*stride_4) + (j_1*stride_5))] + (float32*)B.v0[j_1])
}
}
}
diff --git a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
index dffa234..ef59ab1 100644
--- a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:06.972** total execution time for **tutorials_micro** files:
+**00:07.117** total execution time for **tutorials_micro** files:
-- **00:06.803**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.169**: :ref:`sphx_glr_tutorials_micro_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:06.942**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.175**: :ref:`sphx_glr_tutorials_micro_micro_reference_vm.py` (``micro_reference_vm.py``)
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index 8b52d3d..01ca2a2 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -296,7 +296,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 19.760386 ms
+ Convolution: 19.893361 ms
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index a4b51a4..4fd963a 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -523,8 +523,8 @@ by mapping the 2D convolution to tensor intrinsics
primfn(A_1: handle, W_1: handle, Conv_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], []),
- Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ buffers = {Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], []),
W: Buffer(W_2: Pointer(float16), float16, [3, 3, 16, 32, 16, 16], [])}
buffer_map = {A_1: A, W_1: W, Conv_1: Conv} {
attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 196;
@@ -624,7 +624,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 6.615429 ms
+ conv2d with tensor core: 6.621363 ms
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index 07a78d0..1a32a88 100644
--- a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.006920
- Baseline: 5.884067
+ Numpy running time: 0.007027
+ Baseline: 5.872383
@@ -206,7 +206,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.114601
+ Opt1: 0.114696
@@ -300,7 +300,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.112050
+ Opt2: 0.113036
@@ -389,7 +389,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.060178
+ Opt3: 0.060389
@@ -499,7 +499,7 @@ the corresponding value from the packed array.
.. code-block:: none
- Opt4: 0.061825
+ Opt4: 0.061581
@@ -523,8 +523,8 @@ Here is the generated IR after array packing.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
- C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], []),
B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [packedB: Pointer(float32x32)] "storage_scope" = "global";
@@ -609,7 +609,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.060100
+ Opt5: 0.059310
@@ -725,7 +725,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.016985
+ Opt6: 0.016747
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index 8c71b96..2f6bf61 100644
--- a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:26.797** total execution time for **tutorials_optimize** files:
+**00:26.687** total execution time for **tutorials_optimize** files:
-- **00:24.949**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:00.966**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:00.883**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:24.894**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:00.980**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.813**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index 2f9b308..58d4b3a 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -231,7 +231,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, placeholder(a, 0x133e4fe30)), stage(b, placeholder(b, 0x13da9a600)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range( [...]
+ [stage(a, placeholder(a, 0x13f33a400)), stage(b, placeholder(b, 0x13efe7e30)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range( [...]
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index fb131a7..a5d1531 100644
--- a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:00.614** total execution time for **tutorials_topi** files:
+**00:00.640** total execution time for **tutorials_topi** files:
-- **00:00.614**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.640**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 1afa404..f879fa2 100644
--- a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:06.033** total execution time for **vta_tutorials_autotvm** files:
+**00:06.031** total execution time for **vta_tutorials_autotvm** files:
-- **00:05.859**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.174**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:05.854**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.178**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
index 6964c03..b67b024 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -499,7 +499,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
.. code-block:: none
Extract tasks...
-
...1%, 0.01 MB, 48 KB/s, 0 seconds passed
...2%, 0.02 MB, 96 KB/s, 0 seconds passed
...3%, 0.02 MB, 144 KB/s, 0 seconds passed
...4%, 0.03 MB, 192 KB/s, 0 seconds passed
...5%, 0.04 MB, 240 KB/s, 0 seconds passed
...6%, 0.05 MB, 288 KB/s, 0 seconds passed
...7%, 0.05 MB, 336 KB/s, 0 seconds passed
...8%, 0.06 MB, 384 KB/s, 0 seconds passed
...9%, 0.07 MB, 431 KB/s, 0 seconds passed
...10%, 0.08 MB, 477 KB/s, 0 seconds passed
...11%, 0.09 MB, 525 KB/s, 0 seconds passed
...13%, 0.09 MB, 572 KB/s, 0 seconds passed
...14%, 0.10 MB, 619 KB/s, 0 seconds passed
...15%, 0.11 MB, 665 KB/s, 0 seconds passed
...16%, 0.12 MB, 711 KB/s, 0 seconds passed
...17%, 0.12 MB, 757 KB/s, 0 seconds passed
...18%, 0.13 MB, 804 KB/s, 0 seconds passed
...19%, 0.14 MB, 850 KB/s, 0 seconds passed
...20%, 0.15 MB, 896 KB/s, 0 seconds passed
...21%, 0.16 MB, 942 KB/s, 0 seconds passed
...22%, 0.16 MB, 988 KB/s, 0 seconds passed
...23%, 0.17 MB, 1034 KB/s, 0 seconds passed
...24%, 0.18 MB, 1080 KB/s, 0 seconds passed
...26%, 0.19 MB, 1126 KB/s, 0 seconds passed
...27%, 0.20 MB, 1172 KB/s, 0 seconds passed
...28%, 0.20 MB, 1215 KB/s, 0 seconds passed
...29%, 0.21 MB, 1261 KB/s, 0 seconds passed
...30%, 0.22 MB, 1306 KB/s, 0 seconds passed
...31%, 0.23 MB, 1351 KB/s, 0 seconds passed
...32%, 0.23 MB, 1396 KB/s, 0 seconds passed
...33%, 0.24 MB, 1441 KB/s, 0 seconds passed
...34%, 0.25 MB, 1486 KB/s, 0 seconds passed
...35%, 0.26 MB, 1531 KB/s, 0 seconds passed
...36%, 0.27 MB, 1576 KB/s, 0 seconds passed
...38%, 0.27 MB, 1620 KB/s, 0 seconds passed
...39%, 0.28 MB, 1664 KB/s, 0 seconds passed
...40%, 0.29 MB, 1709 KB/s, 0 seconds passed
...41%, 0.30 MB, 1753 KB/s, 0 seconds passed
...42%, 0.30 MB, 1798 KB/s, 0 seconds passed
...43%, 0.31 MB, 1841 KB/s, 0 seconds passed
...44%, 0.32 MB, 1885 KB/s, 0 seconds passed
...45%, 0.33
MB, 1929 KB/s, 0 seconds passed
...46%, 0.34 MB, 1973 KB/s, 0 seconds passed
...47%, 0.34 MB, 2017 KB/s, 0 seconds passed
...48%, 0.35 MB, 2061 KB/s, 0 seconds passed
...49%, 0.36 MB, 2105 KB/s, 0 seconds passed
...51%, 0.37 MB, 2146 KB/s, 0 seconds passed
...52%, 0.38 MB, 2190 KB/s, 0 seconds passed
...53%, 0.38 MB, 2234 KB/s, 0 seconds passed
...54%, 0.39 MB, 2277 KB/s, 0 seconds passed
...55%, 0.40 MB, 2321 KB/s, 0 seconds passed
...56%, 0.41 MB, 2364 KB/s, 0 seconds passed
...57%, 0.41 MB, 2407 KB/s, 0 seconds passed
...58%, 0.42 MB, 2450 KB/s, 0 seconds passed
...59%, 0.43 MB, 2493 KB/s, 0 seconds passed
...60%, 0.44 MB, 2536 KB/s, 0 seconds passed
...61%, 0.45 MB, 2579 KB/s, 0 seconds passed
...63%, 0.45 MB, 2621 KB/s, 0 seconds passed
...64%, 0.46 MB, 2665 KB/s, 0 seconds passed
...65%, 0.47 MB, 2705 KB/s, 0 seconds passed
...66%, 0.48 MB, 2748 KB/s, 0 seconds passed
...67%, 0.48 MB, 2786 KB/s, 0
seconds passed
...68%, 0.49 MB, 2829 KB/s, 0 seconds passed
...69%, 0.50 MB, 2872 KB/s, 0 seconds passed
...70%, 0.51 MB, 2915 KB/s, 0 seconds passed
...71%, 0.52 MB, 2958 KB/s, 0 seconds passed
...72%, 0.52 MB, 3001 KB/s, 0 seconds passed
...73%, 0.53 MB, 3044 KB/s, 0 seconds passed
...74%, 0.54 MB, 3087 KB/s, 0 seconds passed
...76%, 0.55 MB, 3125 KB/s, 0 seconds passed
...77%, 0.55 MB, 3168 KB/s, 0 seconds passed
...78%, 0.56 MB, 3210 KB/s, 0 seconds passed
...79%, 0.57 MB, 3253 KB/s, 0 seconds passed
...80%, 0.58 MB, 3295 KB/s, 0 seconds passed
...81%, 0.59 MB, 3337 KB/s, 0 seconds passed
...82%, 0.59 MB, 3380 KB/s, 0 seconds passed
...83%, 0.60 MB, 3422 KB/s, 0 seconds passed
...84%, 0.61 MB, 3456 KB/s, 0 seconds passed
...85%, 0.62 MB, 3499 KB/s, 0 seconds passed
...86%, 0.62 MB, 3540 KB/s, 0 seconds passed
...87%, 0.63 MB, 3582 KB/s, 0 seconds passed
...89%, 0.64 MB, 3624 KB/s, 0 seconds passed
...90%, 0.65 MB, 3665 KB/s, 0 seconds passed
...91%, 0.66 MB, 3707 KB/s, 0 seconds passed
...92%, 0.66 MB, 3742 KB/s, 0 seconds passed
...93%, 0.67 MB, 3785 KB/s, 0 seconds passed
...94%, 0.68 MB, 3822 KB/s, 0 seconds passed
...95%, 0.69 MB, 3865 KB/s, 0 seconds passed
...96%, 0.70 MB, 3907 KB/s, 0 seconds passed
...97%, 0.70 MB, 3950 KB/s, 0 seconds passed
...98%, 0.71 MB, 3990 KB/s, 0 seconds passed
...99%, 0.72 MB, 4032 KB/s, 0 seconds passed
...100%, 0.73 MB, 4072 KB/s, 0 seconds passed
+
...1%, 0.01 MB, 46 KB/s, 0 seconds passed
...2%, 0.02 MB, 91 KB/s, 0 seconds passed
...3%, 0.02 MB, 137 KB/s, 0 seconds passed
...4%, 0.03 MB, 183 KB/s, 0 seconds passed
...5%, 0.04 MB, 229 KB/s, 0 seconds passed
...6%, 0.05 MB, 270 KB/s, 0 seconds passed
...7%, 0.05 MB, 314 KB/s, 0 seconds passed
...8%, 0.06 MB, 359 KB/s, 0 seconds passed
...9%, 0.07 MB, 403 KB/s, 0 seconds passed
...10%, 0.08 MB, 448 KB/s, 0 seconds passed
...11%, 0.09 MB, 491 KB/s, 0 seconds passed
...13%, 0.09 MB, 535 KB/s, 0 seconds passed
...14%, 0.10 MB, 579 KB/s, 0 seconds passed
...15%, 0.11 MB, 623 KB/s, 0 seconds passed
...16%, 0.12 MB, 666 KB/s, 0 seconds passed
...17%, 0.12 MB, 710 KB/s, 0 seconds passed
...18%, 0.13 MB, 753 KB/s, 0 seconds passed
...19%, 0.14 MB, 797 KB/s, 0 seconds passed
...20%, 0.15 MB, 840 KB/s, 0 seconds passed
...21%, 0.16 MB, 884 KB/s, 0 seconds passed
...22%, 0.16 MB, 926 KB/s, 0 seconds passed
...23%, 0.17 MB, 970 KB/s, 0 seconds passed
...24%, 0.18 MB, 1012 KB/s, 0 seconds passed
...26%, 0.19 MB, 1055 KB/s, 0 seconds passed
...27%, 0.20 MB, 1098 KB/s, 0 seconds passed
...28%, 0.20 MB, 1140 KB/s, 0 seconds passed
...29%, 0.21 MB, 1181 KB/s, 0 seconds passed
...30%, 0.22 MB, 1224 KB/s, 0 seconds passed
...31%, 0.23 MB, 1266 KB/s, 0 seconds passed
...32%, 0.23 MB, 1308 KB/s, 0 seconds passed
...33%, 0.24 MB, 1351 KB/s, 0 seconds passed
...34%, 0.25 MB, 1391 KB/s, 0 seconds passed
...35%, 0.26 MB, 1433 KB/s, 0 seconds passed
...36%, 0.27 MB, 1474 KB/s, 0 seconds passed
...38%, 0.27 MB, 1515 KB/s, 0 seconds passed
...39%, 0.28 MB, 1558 KB/s, 0 seconds passed
...40%, 0.29 MB, 1599 KB/s, 0 seconds passed
...41%, 0.30 MB, 1640 KB/s, 0 seconds passed
...42%, 0.30 MB, 1681 KB/s, 0 seconds passed
...43%, 0.31 MB, 1723 KB/s, 0 seconds passed
...44%, 0.32 MB, 1764 KB/s, 0 seconds passed
...45%, 0.33
MB, 1805 KB/s, 0 seconds passed
...46%, 0.34 MB, 1847 KB/s, 0 seconds passed
...47%, 0.34 MB, 1887 KB/s, 0 seconds passed
...48%, 0.35 MB, 1926 KB/s, 0 seconds passed
...49%, 0.36 MB, 1968 KB/s, 0 seconds passed
...51%, 0.37 MB, 2009 KB/s, 0 seconds passed
...52%, 0.38 MB, 2048 KB/s, 0 seconds passed
...53%, 0.38 MB, 2090 KB/s, 0 seconds passed
...54%, 0.39 MB, 2131 KB/s, 0 seconds passed
...55%, 0.40 MB, 2170 KB/s, 0 seconds passed
...56%, 0.41 MB, 2210 KB/s, 0 seconds passed
...57%, 0.41 MB, 2248 KB/s, 0 seconds passed
...58%, 0.42 MB, 2289 KB/s, 0 seconds passed
...59%, 0.43 MB, 2330 KB/s, 0 seconds passed
...60%, 0.44 MB, 2370 KB/s, 0 seconds passed
...61%, 0.45 MB, 2410 KB/s, 0 seconds passed
...63%, 0.45 MB, 2451 KB/s, 0 seconds passed
...64%, 0.46 MB, 2490 KB/s, 0 seconds passed
...65%, 0.47 MB, 2530 KB/s, 0 seconds passed
...66%, 0.48 MB, 2567 KB/s, 0 seconds passed
...67%, 0.48 MB, 2590 KB/s, 0
seconds passed
...68%, 0.49 MB, 2630 KB/s, 0 seconds passed
...69%, 0.50 MB, 2670 KB/s, 0 seconds passed
...70%, 0.51 MB, 2709 KB/s, 0 seconds passed
...71%, 0.52 MB, 2749 KB/s, 0 seconds passed
...72%, 0.52 MB, 2789 KB/s, 0 seconds passed
...73%, 0.53 MB, 2829 KB/s, 0 seconds passed
...74%, 0.54 MB, 2869 KB/s, 0 seconds passed
...76%, 0.55 MB, 2907 KB/s, 0 seconds passed
...77%, 0.55 MB, 2946 KB/s, 0 seconds passed
...78%, 0.56 MB, 2986 KB/s, 0 seconds passed
...79%, 0.57 MB, 3025 KB/s, 0 seconds passed
...80%, 0.58 MB, 3064 KB/s, 0 seconds passed
...81%, 0.59 MB, 3103 KB/s, 0 seconds passed
...82%, 0.59 MB, 3142 KB/s, 0 seconds passed
...83%, 0.60 MB, 3179 KB/s, 0 seconds passed
...84%, 0.61 MB, 3218 KB/s, 0 seconds passed
...85%, 0.62 MB, 3257 KB/s, 0 seconds passed
...86%, 0.62 MB, 3296 KB/s, 0 seconds passed
...87%, 0.63 MB, 3335 KB/s, 0 seconds passed
...89%, 0.64 MB, 3374 KB/s, 0 seconds passed
...90%, 0.65 MB, 3409 KB/s, 0 seconds passed
...91%, 0.66 MB, 3447 KB/s, 0 seconds passed
...92%, 0.66 MB, 3486 KB/s, 0 seconds passed
...93%, 0.67 MB, 3524 KB/s, 0 seconds passed
...94%, 0.68 MB, 3563 KB/s, 0 seconds passed
...95%, 0.69 MB, 3601 KB/s, 0 seconds passed
...96%, 0.70 MB, 3638 KB/s, 0 seconds passed
...97%, 0.70 MB, 3675 KB/s, 0 seconds passed
...98%, 0.71 MB, 3715 KB/s, 0 seconds passed
...99%, 0.72 MB, 3744 KB/s, 0 seconds passed
...100%, 0.73 MB, 3781 KB/s, 0 seconds passed
Extracted 10 conv2d tasks:
(1, 14, 14, 256, 512, 1, 1, 0, 0, 2, 2)
(1, 28, 28, 128, 256, 1, 1, 0, 0, 2, 2)
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index 810c0c9..ef0a80f 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -158,7 +158,7 @@ Otherwise, if target is 'sim', execute locally.
Build the inference graph executor
----------------------------------
+----------------------------------
Grab vision model from Gluon model zoo and compile with Relay.
The compilation steps are:
@@ -259,8 +259,8 @@ The compilation steps are:
.. code-block:: none
-
...12%, 0.01 MB, 50 KB/s, 0 seconds passed
...25%, 0.02 MB, 100 KB/s, 0 seconds passed
...38%, 0.02 MB, 151 KB/s, 0 seconds passed
...51%, 0.03 MB, 201 KB/s, 0 seconds passed
...64%, 0.04 MB, 251 KB/s, 0 seconds passed
...77%, 0.05 MB, 301 KB/s, 0 seconds passed
...89%, 0.05 MB, 351 KB/s, 0 seconds passed
...100%, 0.06 MB, 400 KB/s, 0 seconds passed
- resnet18_v1 inference graph built in 7.49s!
+
...12%, 0.01 MB, 56 KB/s, 0 seconds passed
...25%, 0.02 MB, 113 KB/s, 0 seconds passed
...38%, 0.02 MB, 169 KB/s, 0 seconds passed
...51%, 0.03 MB, 225 KB/s, 0 seconds passed
...64%, 0.04 MB, 282 KB/s, 0 seconds passed
...77%, 0.05 MB, 337 KB/s, 0 seconds passed
...89%, 0.05 MB, 393 KB/s, 0 seconds passed
...100%, 0.06 MB, 448 KB/s, 0 seconds passed
+ resnet18_v1 inference graph built in 7.63s!
/workspace/docs/../python/tvm/contrib/graph_runtime.py:26: UserWarning: This function has been moved to tvm.contrib.graph_executor and will be removed in the next TVM release
"This function has been moved to tvm.contrib.graph_executor and will be removed "
diff --git a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
index a19c1d6..70ec055 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:22.905** total execution time for **vta_tutorials_frontend** files:
+**00:22.611** total execution time for **vta_tutorials_frontend** files:
-- **00:22.905**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:22.611**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
index ed64902..4ab661e 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -304,8 +304,8 @@ After we construct the schedule, by default the schedule computes
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], []),
- C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], []),
B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
attr [A_buf: Pointer(int8)] "storage_scope" = "global";
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index 9bc6300..43428bc 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -252,8 +252,8 @@ Those include:
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
- data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ buffers = {data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
attr [data_buf: Pointer(int8)] "storage_scope" = "global";
@@ -448,8 +448,8 @@ below.
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
- data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ buffers = {data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
attr [data_buf: Pointer(int8)] "storage_scope" = "global";
diff --git a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index bde11ab..df221b5 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -189,8 +189,8 @@ Those include:
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
- data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ buffers = {data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], [])}
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diff --git a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
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--- a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:02.885** total execution time for **vta_tutorials_optimize** files:
+**00:02.893** total execution time for **vta_tutorials_optimize** files:
-- **00:02.414**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.472**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.407**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.485**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
index 8539f93..ad233d4 100644
--- a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.883** total execution time for **vta_tutorials** files:
+**00:00.904** total execution time for **vta_tutorials** files:
-- **00:00.444**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.439**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.464**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.440**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
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+<title>Node34->Node16</title>
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+<title>Node34->Node18</title>
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+<!-- Node34->Node36 -->
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+<title>Node34->Node36</title>
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<title>Node40->Node10</title>
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<title>Node40->Node11</title>
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<title>Node40->Node15</title>
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<title>Node40->Node16</title>
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<title>Node40->Node17</title>
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<!-- Node40->Node18 -->
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<title>Node40->Node18</title>
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<title>Node40->Node20</title>
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<!-- Node40->Node21 -->
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<title>Node40->Node21</title>
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<title>Node40->Node23</title>
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<title>Node40->Node28</title>
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<title>Node40->Node30</title>
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<title>Node40->Node31</title>
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+<!-- Node54->Node5 -->
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+<!-- Node54->Node21 -->
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-<title>Node53->Node21</title>
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+<!-- Node54->Node34 -->
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-<title>Node53->Node35</title>
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+<!-- Node56->Node1 -->
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+<title>Node56->Node1</title>
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+<!-- Node56->Node3 -->
<g id="edge173" class="edge">
-<title>Node55->Node3</title>
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+<title>Node56->Node3</title>
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+<!-- Node56->Node16 -->
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+<title>Node56->Node16</title>
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+<!-- Node56->Node17 -->
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-<title>Node55->Node17</title>
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+<title>Node56->Node17</title>
+<path fill="none" stroke="#191970" d="M2966.0218,-722.6006C3013.0413,-717.84 3085.0911,-706.5465 3142,-680 3287.056,-612.3352 3302.1661,-559.8087 3424,-456 3521.1778,-373.1995 3687.7518,-354.2088 3620,-246 3512.6655,-74.5722 2822.5258,-27.2194 2638.8499,-17.5837"/>
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+<!-- Node56->Node21 -->
<g id="edge174" class="edge">
-<title>Node55->Node21</title>
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-<title>Node55->Node32</title>
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+<path fill="none" stroke="#191970" d="M2922.4595,-716.4646C2932.7516,-697.5623 2954,-653.7274 2954,-614 2954,-614 2954,-614 2954,-558 2954,-542.5183 2842.0089,-353.5583 2801.4469,-285.6265"/>
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+<!-- Node56->Node34 -->
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-<title>Node55->Node35</title>
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+<title>Node56->Node34</title>
+<path fill="none" stroke="#191970" d="M2966.1641,-718.6273C3022.164,-707.119 3106,-679.0306 3106,-614 3106,-614 3106,-614 3106,-328.5 3106,-290.3133 3108.144,-273.8336 3082,-246 3063.5793,-226.3888 3037.1466,-214.2298 3012.0065,-206.6977"/>
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+<title>Node56->Node41</title>
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+<!-- Node57->Node4 -->
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-<title>Node56->Node4</title>
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+<path fill="none" stroke="#191970" d="M2138.4006,-549.6767C2041.5469,-538.2582 1869.8138,-518.0117 1784.7108,-507.9785"/>
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+<!-- Node57->Node16 -->
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-<title>Node56->Node16</title>
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-<title>Node56->Node18</title>
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+<title>Node57->Node18</title>
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+<!-- Node61->Node3 -->
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