[GitHub] [incubator-tvm] yzhliu commented on a change in pull request #4664: [Docs] Convert Layout pass.

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yzhliu commented on a change in pull request #4664: [Docs] Convert Layout pass.
URL: https://github.com/apache/incubator-tvm/pull/4664#discussion_r364905522
 
 

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 File path: docs/dev/convert_layout.rst
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+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+..    http://www.apache.org/licenses/LICENSE-2.0
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+===================
+Convert Layout Pass
+===================
+**Author**: `Animesh Jain <https://github.com/anijain2305>`_
+
+*************
+1. Background
+*************
+
+Data layout format describes how the data is laid out in the memory. For example, Tensorflow framework default data layout for convolution operator is NHWC, i.e, the data is 4-dimensions and is laid out in row-major format with N being the first dimension and C being the last dimension. Data layout has a major role in model performance, significantly affecting spatial and temporal locality. For example, Intel x86 backend in TVM prefers layout as NCHWc where the C dimension is tiled in 2 dimensions to exploit data locality efficiently. Similarly, CUDA backend prefers the data layout to be in NCHW format.
+
+Essentially, TVM has to deal with data layouts throughout the compiler toolchain - Framework parsers, Relay layout transformations, and TOPI schedules. As we move towards third-party codegen integration, which might have their own data layout restrictions, handling layouts at all levels in TVM toolchain is going to become even more challenging. Therefore, we developed a new Relay pass - **ConvertLayout** -- to reduce some of the complications that arise due to layout handling.
+
+If you directly want to understand the usage of ConvertLayout Pass, directly jump to Section 4 - Usage.
+
+*************
+2. Motivation
+*************
+
+Lets look at a simple scenario to understand the complications that arise due to different layouts - Suppose we want to compile a Tensorflow NHWC graph for an ARM edge device. But, suppose we currently support only NCHW schedules in TOPI for ARM. So, there is a mismatch between framework layout and TOPI-supported layout. One way to deal with this mismatch is to insert layout transforms before each and after convolution, such that resulting convolution has NCHW input data layout and can use TOPI schedules. However, this can lead to performance degradation because of the presence of too many layout transforms.
 
 Review comment:
   Lets -> Let's

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