Merge remote-tracking branch 'ups/develop' into refine/infershape

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)

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -130,12 +130,13 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
 
   std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryFromPrimitive(
       const std::shared_ptr<mkldnn::memory> user_weights_memory_p,
-      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
+      std::vector<mkldnn::primitive>& 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<mkldnn::memory> AcquireBiasMemoryFromPrimitive(
@@ -266,6 +267,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                    "It must use CPUPlace.");
 
+    const bool is_test = ctx.Attr<bool>("is_test");
+
     auto& dev_ctx =
         ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
     const auto& mkldnn_engine = dev_ctx.GetEngine();
@@ -296,6 +299,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    bool fuse_relu = ctx.Attr<bool>("fuse_relu");
     int groups = ctx.Attr<int>("groups");
 
     // TODO(pzelazko-intel) add support for group convolution and dilation
@@ -348,11 +352,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       bias_tz = paddle::framework::vectorize2int(bias->dims());
       auto bias_md = platform::MKLDNNMemDesc(
           bias_tz, platform::MKLDNNGetDataType<T>(), 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<T> {
     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<T>(output_data));
 
@@ -402,11 +407,26 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
   }
 
  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<mkldnn::convolution_forward::primitive_desc>
   ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                        const memory::desc& dst, const std::vector<int>& strides,
                        const std::vector<int>& 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<T> {
         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<mkldnn::convolution_forward::primitive_desc>(
         p_conv_pd);
@@ -427,7 +452,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
                        const memory::desc& bias, const memory::desc& dst,
                        const std::vector<int>& strides,
                        const std::vector<int>& 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<T> {
         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<mkldnn::convolution_forward::primitive_desc>(
         p_conv_pd);

paddle/fluid/operators/conv_op.cc

@@ -109,6 +109,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
 }
 
 void Conv2DOpMaker::Make() {
+  AddAttr<bool>("is_test", "").SetDefault(false);
   AddInput(
       "Input",
       "(Tensor) The input tensor of convolution operator. "
@@ -161,6 +162,8 @@ void Conv2DOpMaker::Make() {
   AddAttr<bool>("use_mkldnn",
                 "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
+  AddAttr<bool>("fuse_relu", "(bool, default false) Only used in mkldnn kernel")
+      .SetDefault(false);
   AddAttr<std::string>(
       "data_format",
       "(string, default NCHW) Only used in "

paddle/fluid/platform/mkldnn_helper.h

@@ -192,7 +192,8 @@ class MKLDNNHandler {
       mkldnn::memory::primitive_desc& user_mpd,  // NOLINT
       const std::shared_ptr<mkldnn::memory> user_memory_p,
       const std::string& suffix,
-      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
+      std::vector<mkldnn::primitive>& 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<mkldnn::reorder>(
           dev_ctx_.GetBlob(key_reorder_p));

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()