diff --git a/benchmark/fluid/models/resnet.py b/benchmark/fluid/models/resnet.py index ae1baa48e17e40448e457052fd1464b9604a2128..d71b855612ae32083b2b2e3448db3749c340633b 100644 --- a/benchmark/fluid/models/resnet.py +++ b/benchmark/fluid/models/resnet.py @@ -20,6 +20,7 @@ import functools import numpy as np import time import os +import math import cProfile, pstats, StringIO @@ -27,128 +28,120 @@ import paddle import paddle.fluid as fluid import paddle.fluid.core as core import paddle.fluid.profiler as profiler -# from recordio_converter import imagenet_train, imagenet_test from imagenet_reader import train, val +train_parameters = { + "input_size": [3, 224, 224], + "input_mean": [0.485, 0.456, 0.406], + "input_std": [0.229, 0.224, 0.225], + "learning_strategy": { + "name": "piecewise_decay", + "batch_size": 256, + "epochs": [30, 60, 90], + "steps": [0.1, 0.01, 0.001, 0.0001] + } +} + + +class ResNet(): + def __init__(self, layers=50, is_train=True): + self.params = train_parameters + self.layers = layers + self.is_train = is_train + + def net(self, input, class_dim=1000): + layers = self.layers + supported_layers = [50, 101, 152] + assert layers in supported_layers, \ + "supported layers are {} but input layer is {}".format(supported_layers, layers) + + if layers == 50: + depth = [3, 4, 6, 3] + elif layers == 101: + depth = [3, 4, 23, 3] + elif layers == 152: + depth = [3, 8, 36, 3] + num_filters = [64, 128, 256, 512] + + conv = self.conv_bn_layer( + input=input, num_filters=64, filter_size=7, stride=2, act='relu') + conv = fluid.layers.pool2d( + input=conv, + pool_size=3, + pool_stride=2, + pool_padding=1, + pool_type='max') + + for block in range(len(depth)): + for i in range(depth[block]): + conv = self.bottleneck_block( + input=conv, + num_filters=num_filters[block], + stride=2 if i == 0 and block != 0 else 1) + + pool = fluid.layers.pool2d( + input=conv, pool_size=7, pool_type='avg', global_pooling=True) + stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) + out = fluid.layers.fc(input=pool, + size=class_dim, + act='softmax', + param_attr=fluid.param_attr.ParamAttr( + initializer=fluid.initializer.Uniform(-stdv, + stdv))) + return out + + def conv_bn_layer(self, + input, + num_filters, + filter_size, + stride=1, + groups=1, + act=None): + conv = fluid.layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + stride=stride, + padding=(filter_size - 1) // 2, + groups=groups, + act=None, + bias_attr=False) + return fluid.layers.batch_norm( + input=conv, act=act, is_test=not self.is_train) + + def shortcut(self, input, ch_out, stride): + ch_in = input.shape[1] + if ch_in != ch_out or stride != 1: + return self.conv_bn_layer(input, ch_out, 1, stride) + else: + return input -def conv_bn_layer(input, - ch_out, - filter_size, - stride, - padding, - act='relu', - is_train=True): - conv1 = fluid.layers.conv2d( - input=input, - filter_size=filter_size, - num_filters=ch_out, - stride=stride, - padding=padding, - act=None, - bias_attr=False) - return fluid.layers.batch_norm(input=conv1, act=act, is_test=not is_train) - - -def shortcut(input, ch_out, stride, is_train=True): - ch_in = input.shape[1] # if args.data_format == 'NCHW' else input.shape[-1] - if ch_in != ch_out: - return conv_bn_layer( - input, ch_out, 1, stride, 0, None, is_train=is_train) - else: - return input - - -def basicblock(input, ch_out, stride, is_train=True): - short = shortcut(input, ch_out, stride, is_train=is_train) - conv1 = conv_bn_layer(input, ch_out, 3, stride, 1, is_train=is_train) - conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None, is_train=is_train) - return fluid.layers.elementwise_add(x=short, y=conv2, act='relu') - - -def bottleneck(input, ch_out, stride, is_train=True): - short = shortcut(input, ch_out * 4, stride, is_train=is_train) - conv1 = conv_bn_layer(input, ch_out, 1, stride, 0, is_train=is_train) - conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, is_train=is_train) - conv3 = conv_bn_layer( - conv2, ch_out * 4, 1, 1, 0, act=None, is_train=is_train) - return fluid.layers.elementwise_add(x=short, y=conv3, act='relu') - - -def layer_warp(block_func, input, ch_out, count, stride): - res_out = block_func(input, ch_out, stride) - for i in range(1, count): - res_out = block_func(res_out, ch_out, 1) - return res_out - + def bottleneck_block(self, input, num_filters, stride): + conv0 = self.conv_bn_layer( + input=input, num_filters=num_filters, filter_size=1, act='relu') + conv1 = self.conv_bn_layer( + input=conv0, + num_filters=num_filters, + filter_size=3, + stride=stride, + act='relu') + conv2 = self.conv_bn_layer( + input=conv1, num_filters=num_filters * 4, filter_size=1, act=None) -def resnet_imagenet(input, - class_dim, - depth=50, - data_format='NCHW', - is_train=True): + short = self.shortcut(input, num_filters * 4, stride) - cfg = { - 18: ([2, 2, 2, 1], basicblock), - 34: ([3, 4, 6, 3], basicblock), - 50: ([3, 4, 6, 3], bottleneck), - 101: ([3, 4, 23, 3], bottleneck), - 152: ([3, 8, 36, 3], bottleneck) - } - stages, block_func = cfg[depth] - conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3) - pool1 = fluid.layers.pool2d( - input=conv1, pool_type='avg', pool_size=3, pool_stride=2) - res1 = layer_warp(block_func, pool1, 64, stages[0], 1) - res2 = layer_warp(block_func, res1, 128, stages[1], 2) - res3 = layer_warp(block_func, res2, 256, stages[2], 2) - res4 = layer_warp(block_func, res3, 512, stages[3], 2) - pool2 = fluid.layers.pool2d( - input=res4, - pool_size=7, - pool_type='avg', - pool_stride=1, - global_pooling=True) - out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax') - return out - - -def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'): - assert (depth - 2) % 6 == 0 - - n = (depth - 2) // 6 - - conv1 = conv_bn_layer( - input=input, ch_out=16, filter_size=3, stride=1, padding=1) - res1 = layer_warp(basicblock, conv1, 16, n, 1) - res2 = layer_warp(basicblock, res1, 32, n, 2) - res3 = layer_warp(basicblock, res2, 64, n, 2) - pool = fluid.layers.pool2d( - input=res3, pool_size=8, pool_type='avg', pool_stride=1) - out = fluid.layers.fc(input=pool, size=class_dim, act='softmax') - return out + return fluid.layers.elementwise_add(x=short, y=conv2, act='relu') def _model_reader_dshape_classdim(args, is_train): - model = resnet_cifar10 + model = None reader = None - if args.data_set == "cifar10": - class_dim = 10 - if args.data_format == 'NCHW': - dshape = [3, 32, 32] - else: - dshape = [32, 32, 3] - model = resnet_cifar10 - if is_train: - reader = paddle.dataset.cifar.train10() - else: - reader = paddle.dataset.cifar.test10() - elif args.data_set == "flowers": + if args.data_set == "flowers": class_dim = 102 if args.data_format == 'NCHW': dshape = [3, 224, 224] else: dshape = [224, 224, 3] - model = resnet_imagenet if is_train: reader = paddle.dataset.flowers.train() else: @@ -159,7 +152,6 @@ def _model_reader_dshape_classdim(args, is_train): dshape = [3, 224, 224] else: dshape = [224, 224, 3] - model = resnet_imagenet if not args.data_path: raise Exception( "Must specify --data_path when training with imagenet") @@ -173,12 +165,11 @@ def _model_reader_dshape_classdim(args, is_train): reader = train(xmap=False) else: reader = val(xmap=False) - return model, reader, dshape, class_dim + return reader, dshape, class_dim def get_model(args, is_train, main_prog, startup_prog): - model, reader, dshape, class_dim = _model_reader_dshape_classdim(args, - is_train) + reader, dshape, class_dim = _model_reader_dshape_classdim(args, is_train) pyreader = None trainer_count = int(os.getenv("PADDLE_TRAINERS")) @@ -198,7 +189,8 @@ def get_model(args, is_train, main_prog, startup_prog): label = fluid.layers.data( name='label', shape=[1], dtype='int64') - predict = model(input, class_dim, is_train=is_train) + model = ResNet(is_train=is_train) + predict = model.net(input, class_dim=class_dim) cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(x=cost) @@ -216,15 +208,14 @@ def get_model(args, is_train, main_prog, startup_prog): total_images = 1281167 / trainer_count step = int(total_images / args.batch_size + 1) - epochs = [30, 60, 80, 90] + epochs = [30, 60, 90] bd = [step * e for e in epochs] base_lr = args.learning_rate lr = [] lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)] optimizer = fluid.optimizer.Momentum( - learning_rate=base_lr, - #learning_rate=fluid.layers.piecewise_decay( - # boundaries=bd, values=lr), + learning_rate=fluid.layers.piecewise_decay( + boundaries=bd, values=lr), momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) optimizer.minimize(avg_cost) diff --git a/paddle/fluid/operators/conv_mkldnn_op.cc b/paddle/fluid/operators/conv_mkldnn_op.cc index c5cbadc892904dc064b49ebc461944c4671a69da..3eb02c6b61ce61140bd777647a12477dd9c3c803 100644 --- a/paddle/fluid/operators/conv_mkldnn_op.cc +++ b/paddle/fluid/operators/conv_mkldnn_op.cc @@ -130,12 +130,13 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler { std::shared_ptr AcquireWeightsMemoryFromPrimitive( const std::shared_ptr user_weights_memory_p, - std::vector& pipeline) { // NOLINT + std::vector& pipeline, // NOLINT + bool is_persistent = false) { auto user_weights_pd = user_weights_memory_p->get_primitive_desc(); auto weights_pd = conv_pd_->weights_primitive_desc(); return this->AcquireMemory(weights_pd, user_weights_pd, user_weights_memory_p, "@weights_mem_p", - pipeline); + pipeline, is_persistent); } std::shared_ptr AcquireBiasMemoryFromPrimitive( @@ -266,6 +267,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), "It must use CPUPlace."); + const bool is_test = ctx.Attr("is_test"); + auto& dev_ctx = ctx.template device_context(); const auto& mkldnn_engine = dev_ctx.GetEngine(); @@ -296,6 +299,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { std::vector strides = ctx.Attr>("strides"); std::vector paddings = ctx.Attr>("paddings"); std::vector dilations = ctx.Attr>("dilations"); + bool fuse_relu = ctx.Attr("fuse_relu"); int groups = ctx.Attr("groups"); // TODO(pzelazko-intel) add support for group convolution and dilation @@ -348,11 +352,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { bias_tz = paddle::framework::vectorize2int(bias->dims()); auto bias_md = platform::MKLDNNMemDesc( bias_tz, platform::MKLDNNGetDataType(), memory::format::x); - conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md, - strides, paddings, mkldnn_engine); + conv_pd = + ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md, strides, + paddings, mkldnn_engine, fuse_relu); } else { conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, - paddings, mkldnn_engine); + paddings, mkldnn_engine, fuse_relu); } // Save conv_pd/src_memory/weights_memory for backward pass dev_ctx.SetBlob(key_conv_pd, conv_pd); @@ -371,7 +376,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { auto src_memory_p = handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline); auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive( - user_weights_memory_p, pipeline); + user_weights_memory_p, pipeline, is_test); auto dst_memory_p = handler.AcquireDstMemoryFromPrimitive(to_void_cast(output_data)); @@ -402,11 +407,26 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { } private: + mkldnn::primitive_attr AddRelu() const { + // Fusion with ReLU layer is executed through the PostOps feature. Create a + // PostOps object and configure it to execute an eltwise relu operation. + mkldnn::primitive_attr conv_attr; + constexpr float scale = 1.0f; + constexpr float negative_slope = 0.0f; + constexpr float placeholder = 0.0f; + mkldnn::post_ops post_operations; + post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu, + negative_slope, placeholder); + conv_attr.set_post_ops(post_operations); + return conv_attr; + } + std::unique_ptr ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights, const memory::desc& dst, const std::vector& strides, const std::vector& paddings, - const mkldnn::engine& engine) const { + const mkldnn::engine& engine, + const bool fuse_relu) const { memory::dims stride_dims = {strides[0], strides[1]}; memory::dims padding_dims = {paddings[0], paddings[1]}; @@ -415,8 +435,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { dst, stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero); - auto p_conv_pd = - new mkldnn::convolution_forward::primitive_desc(conv_desc, engine); + mkldnn::primitive_attr conv_attr; + if (fuse_relu) { + conv_attr = AddRelu(); + } + + auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc( + conv_desc, conv_attr, engine); return std::unique_ptr( p_conv_pd); @@ -427,7 +452,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { const memory::desc& bias, const memory::desc& dst, const std::vector& strides, const std::vector& paddings, - const mkldnn::engine& engine) const { + const mkldnn::engine& engine, + const bool fuse_relu) const { memory::dims stride_dims = {strides[0], strides[1]}; memory::dims padding_dims = {paddings[0], paddings[1]}; @@ -436,8 +462,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { bias, dst, stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero); - auto p_conv_pd = - new mkldnn::convolution_forward::primitive_desc(conv_desc, engine); + mkldnn::primitive_attr conv_attr; + if (fuse_relu) { + conv_attr = AddRelu(); + } + + auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc( + conv_desc, conv_attr, engine); return std::unique_ptr( p_conv_pd); diff --git a/paddle/fluid/operators/conv_op.cc b/paddle/fluid/operators/conv_op.cc index 61ca80877a6dfcdf30a0ff346342116e36eec6f2..41d4fcf6de7c8fcb3cfbb2063b0a2ac1a2356168 100644 --- a/paddle/fluid/operators/conv_op.cc +++ b/paddle/fluid/operators/conv_op.cc @@ -109,6 +109,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType( } void Conv2DOpMaker::Make() { + AddAttr("is_test", "").SetDefault(false); AddInput( "Input", "(Tensor) The input tensor of convolution operator. " @@ -161,6 +162,8 @@ void Conv2DOpMaker::Make() { AddAttr("use_mkldnn", "(bool, default false) Only used in mkldnn kernel") .SetDefault(false); + AddAttr("fuse_relu", "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); AddAttr( "data_format", "(string, default NCHW) Only used in " diff --git a/paddle/fluid/platform/mkldnn_helper.h b/paddle/fluid/platform/mkldnn_helper.h index f6e9a52b275353c03c1f350719766922a97f6cb3..c0a2543ba5d8ff8f34cb6231c51cb5053a6a9481 100644 --- a/paddle/fluid/platform/mkldnn_helper.h +++ b/paddle/fluid/platform/mkldnn_helper.h @@ -192,7 +192,8 @@ class MKLDNNHandler { mkldnn::memory::primitive_desc& user_mpd, // NOLINT const std::shared_ptr user_memory_p, const std::string& suffix, - std::vector& pipeline) { // NOLINT + std::vector& pipeline, // NOLINT + bool is_persistent = false) { // create reorder primitive if the input format is not the preferred one auto local_key = key_ + suffix; auto key_reorder_p = key_ + suffix + "reorder_p"; @@ -213,7 +214,7 @@ class MKLDNNHandler { pipeline.push_back(*reorder_p); } dev_ctx_.SetBlob(local_key, target_memory_p); - } else { + } else if (!is_persistent) { // Make reorder if needed auto reorder_p = std::static_pointer_cast( dev_ctx_.GetBlob(key_reorder_p)); diff --git a/python/paddle/fluid/transpiler/inference_transpiler.py b/python/paddle/fluid/transpiler/inference_transpiler.py index 02fefe32df14dfca39de1da24147c284d6d82230..adad2428f7fdc554cf4efd652f52b5c5de0ab527 100644 --- a/python/paddle/fluid/transpiler/inference_transpiler.py +++ b/python/paddle/fluid/transpiler/inference_transpiler.py @@ -60,12 +60,46 @@ class InferenceTranspiler(object): if not isinstance(scope, core.Scope): raise TypeError("scope should be as Scope type or None") use_mkldnn = bool(os.getenv("FLAGS_use_mkldnn", False)) + self._fuse_batch_norm(program, place, scope) if use_mkldnn: - self._fuse_relu_mkldnn(program) self._fuse_conv_bias_mkldnn(program) + self._fuse_conv_relu_mkldnn(program) + self._fuse_bn_relu_mkldnn(program) + + def _fuse_conv_relu_mkldnn(self, program): + ''' + Transpile the program by fused relu activation for MKLDNN program. + Relu activation following convolution OP can be fused by adding + 'fuse_relu' attribute to convolution OP. + The result of fuse is: + - before: + - conv->relu->any_other_op + - after: + - conv->any_other_op + :param program: program to transpile + :type program: Program + ''' + self.block = program.block(0) + + i = 0 + while i < len(self.block.ops): + current_op = self.block.ops[i] + if current_op.type in ['conv2d']: + next_op = self.block.ops[i + 1] + if next_op.type == 'relu': + # modify conv OP to include relu + current_op.set_attr("fuse_relu", True) + # remove conv OP + self.block._remove_op(i + 1) + i = i + 1 + + # TODO(luotao): use clone() method to flush the program.desc in force, + # since some large program.desc will not be flushed immediately. + # And a better solution will be considered later. + program = program.clone() - def _fuse_relu_mkldnn(self, program): + def _fuse_bn_relu_mkldnn(self, program): ''' Transpile the program by fused relu activation for MKLDNN program. @@ -159,7 +193,6 @@ class InferenceTranspiler(object): self._fuse_conv_bias(i, current_op, next_op) self.block._remove_op(i + 1) # Remove old conv self.block._remove_op(i + 1) # Remove elementwise_add - i = i + 1 i = i + 1 self._remove_unused_var()