From 91417e256e55670f35660f550e0805d528363515 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 6 Jul 2020 17:13:30 -0700 Subject: [PATCH] [MLIR:TPU] Add document for space to depth transform pass. 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z>o#*p$^@GU%U3K}ItnYURa8v#Llp+gJM3g?%G!DFn;SN{URIW?QvByHL81^6IuQ4A z2xx>m1n6GB4QdU*gh25ORJh(7~0hz46EsagiR7@Zw+s4LQ9m7Zk zhXpZ=j$32$tCg|I9b;TOSd2`RXtl{8f06m5kS(_ZK1W0SiQ;SjL);|HC&s9GRN z7CJ50i%m|7n`4!!+`Og25Kp0)9k8xpLDahKdj+w{B{Mp1J$86=3ATuW^e#su5u7qn xLwz(7M|0xa(VU1<&Vb9e(c*NpI33<~;*b3nFSq8bzn;VZ0-mmZF6*2UngHmNG}QnA literal 0 HcmV?d00001 diff --git a/tensorflow/compiler/mlir/tensorflow/g3doc/space_to_depth.md b/tensorflow/compiler/mlir/tensorflow/g3doc/space_to_depth.md new file mode 100644 index 00000000000..417ea356a6f --- /dev/null +++ b/tensorflow/compiler/mlir/tensorflow/g3doc/space_to_depth.md @@ -0,0 +1,195 @@ +# Automatic Space to Depth Transform in MLIR Bridge + +Author: wangtao@, yuanzx@, hinsu@, lyandy@, chiachenc@, aminim@, jpienaar@, +dehao@ + +## TL;DR + +_This document describes an automatic space to depth transform for the first +convolution in the new MLIR bridge to improve MXU efficiency of low batch size +convolutions._ + +## Background + +For image models, the first layer is usually not MXU friendly as it has a +feature size of 3. This results in poor performance especially with small batch. + +One way to address this issue is to use the `space-to-depth` transform. This +optimization tiles the 2x2 space dimensions to the feature dimension so that the +feature dimension becomes 3\*4=12, which is more MXU friendly. In order to make +this optimization efficient, the shape of the weight needs to be padded and +transposed to the shape that the convolution emitter expects. The input also +needs to be transposed on the host and padded on the device to make the +convolution efficient. Although a 2x2 space-to-depth transform works only when +the first convolution has a stride of 2, many image models, ResNet-like in +particular, have a stride-2 convolution in the first layer. + +Space to depth helped models such as MaskRCNN, SSD and I3D gain more than 2X +speedup and reduce memory usage in the first convolution. + +The first convolution in many image models, including ResNet or ResNet-like, is +a (kernel=7, stride=2) 2D convolution. The input of the convolution is images, +which usually has RGB channels. The input of this first convolution is of shape +[batch\_size, height, width, 3] and the kernel size is [kernel\_size, +kernel\_size, 3, out\_channel]. Space to depth is to transform this first +convolution's input to [batch\_size, height // stride, width // stride, 3 \* +stride \* stride] and the kernel to [kernel\_size // stride, kernel\_size // +stride, 3 \* stride \* stride, out\_channel] to improve TPU MXU utilization. + +![drawings](images/sapce_to_depth_transform.png) + +This optimization can be automatically done by the graph optimizer where weight +transformation is done at variable loading time and the input transformation is +done for every inference invocation. A further optimization can fuse this (at +host) with the double transpose to minimize memory operation on host. + +## Proposed Method + +**block\_size** is defined as the number of space sizes transformed to the depth +dimension. _stride % block\_size == 0_ and _stride >= block\_size_ is required +to do the transform. There are three parts of automatically space to depth +transformation: + +1. Transform input on the host. + + Space-to-depth performs the following permutation, which is equivalent to + `tf.nn.space_to_depth`. + + ``` + images = tf.reshape(images, [batch, h // block_size, block_size, + w // block_size, block_size, c]) + images = tf.transpose(images, [0, 1, 3, 2, 4, 5]) + images = tf.reshape(images, [batch, h // block_size, w // block_size, + c * (block_size ** 2)]) + ``` + + `SpaceToDepthOp` can be called on the host to perform the transform. + +1. Weight Transformation + + Weight Transformation is similar to Input Transform. Weight transform is + needed to apply space to depth optimization for a model that needs to load a + pre-train checkpoint. This transform can be done on the host or TPU device + based on the cost. As the size of the kernel is relatively small, this won't + add additional cost to TPU device time. Below is the logic to transform the + kernel of shape [7, 7, 3, 64] to [4, 4, 12, 84]. + + ``` + conv0 = tf.compat.v1.layers.Conv2D( + filters=filters, + kernel_size=kernel_size, + strides=2, + padding=('SAME' if strides == 1 else 'VALID'), + use_bias=False, + kernel_initializer=tf.variance_scaling_initializer(), + data_format=data_format) + + # Use the image size without space-to-depth transform as the input of conv0. + batch_size, h, w, channel = inputs.get_shape().as_list() + conv0.build([ + batch_size, h * space_to_depth_block_size, w * space_to_depth_block_size, + channel // (space_to_depth_block_size**2) + ]) + + kernel = conv0.weights[0] + # [7, 7, 3, 64] --> [8, 8, 3, 64] + + kernel = tf.pad( + kernel, + paddings=tf.constant([[1, 0], [1, 0], [0, 0], [0, 0]]), + mode='CONSTANT', + constant_values=0.) + # Transform kernel follows the space-to-depth logic: https://www.tensorflow.org/api_docs/python/tf/nn/space_to_depth) + kernel = tf.reshape( + kernel, + [4, space_to_depth_block_size, 4, space_to_depth_block_size, 3, filters]) + + kernel = tf.transpose(kernel, [0, 2, 1, 3, 4, 5]) + kernel = tf.reshape(kernel, [4, 4, int(channel), filters]) + kernel = tf.cast(kernel, inputs.dtype) + ``` + + If kernel\_size % block\_size != 0, padding is needed for the weight before + transform, input of Convolution needs to be padded as well. + +1. Rewrite the first convolution + + Need to rewrite the first convolution's shape of input from [batch\_size, + height, width, 3] to [batch\_size, height // block\_size, width // + block\_size, 3 \* block\_size \* block\_size] and kernel shape from + [kernel\_size, kernel\_size, 3, out\_channel] to [kernel\_size // + block\_size, kernel\_size // block\_size, 3 \* block\_size \* block\_size, + + This is the proposed workflow for automatic space to depth transformation. + All the transformations will be triggered in a MLIR SpaceToDepthRewritePass, + this Rewrite pass will be triggered before TPURewrite so that no metadata + rewrite is needed. + +* First, the rewrite pass will walk through all the convolutions in func of + tf\_device::LaunchOp and get the first Convolution and its shape; +* Second, the rewrite pass will apply transformations to the first + convolution, the padding before the first convolution, first convolution's + filters and its Conv2DBackPropFilter; +* At last, the rewrite pass will insert SpaceToDepthOp after IteratorGetNext + where the iterator's result has the same shape as the first convolution's + input. + +#### Pseudo MLIR code before and after RewritePass + +``` +// Example: original program: +// +module { + func @while_body { + %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"}: + -> tensor<2x224x224x3xf32> + %device_launch = "tf_device.launch_func"(%input,...) {func = @_func,...) + return ... + } + func @_func(%input: tensor<2x224x224x3xf32>, + %filter: tensor<7x7x3x64xf32>) { + %6 = "tf.Conv2D"(%input, %filter) {strides = [1, 2, 2, 1]}: (tensor<2x230x230x3xf32>, tensor<7x7x3x64xf32>) -> + tensor<2x112x112x64xf32> + } +} + +// With this pass, the program will be transformed into: +module { + func @while_body { + %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"} + -> tensor<2x224x224x3xf32> + %space_to_depth = "tf.SpaceToDepth"(%input) {block_size = 2, ...}: + (tensor<2x224x224x3xf32>) -> tensor<2x112x112x12xf32> + %device_launch = "tf_device.launch_func"(%space_to_depth,...) {func = @_func,...) + return ... + } + func @_func(%input: tensor<2x112x112x12xf32>, + %filter: tensor<7x7x3x64xf32>) { + %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): + tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32> + %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: + (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> + tensor<2x112x112x64xf32> + } +} +``` + +### SpaceToDepth Trigger Condition + +Space to depth will only be triggered when batch size is small and the first +convolution channel size is small. Stride of the convolution should be bigger +than 1 as well. A cost model will be built that takes input shape and host cost +into consideration to trigger the transformation. There will be a flag to +disable this feature as well. + +### Fuse SpaceToDepth with Automatic Double Transpose + +The transpose and reshape op in SpaceToDepthOp on TPU hosts may cause image +model to be infeed bound. To reduce host time, space to depth transform can be +fused with `automatic double transpose` to reduce extra overhead on the host. + +### Extend from Conv2D to Conv3D + +SpaceToDepth not only helps with 2D image models but also 3D image models such +as I3D. The plan is to apply automatic space to depth for Conv2D as the first +step. After Conv2D is well tested, will generalize this technique to Conv3D.