Release/1.4

cmake/anakin_subgraph.cmake

@@ -25,8 +25,9 @@ endif()
 
 if(ANAKIN_FOUND)
     message(STATUS "Current ANAKIN header is ${ANAKIN_INCLUDE_DIR}/anakin_config.h. ")
+    include_directories(${ANAKIN_ROOT})
     include_directories(${ANAKIN_ROOT}/include)
-    include_directories(${ANAKIN_ROOT}/include/saber)
+    include_directories(${ANAKIN_ROOT}/saber)
     link_directories(${ANAKIN_ROOT})
     add_definitions(-DPADDLE_WITH_ANAKIN)
 endif()

paddle/fluid/API.spec

@@ -13,6 +13,7 @@ paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, d
 paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', '7d9a51fc9cf3c5245b5227080a8064c3'))
 paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', '4c0cd83f0b401fc2ff84c70974e5d210'))
 paddle.fluid.cuda_pinned_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd0c3ebd813c39958c92b78e3eef7e912'))
+paddle.fluid.in_dygraph_mode (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f06314a1cb30c96b5808dde2219c2dae'))
 paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f5369953dd0c443961cf79f7a00e1a03'))
 paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100)), ('document', '9c7decb955b9c4f718114179c8985581'))
@@ -124,7 +125,7 @@ paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed
 paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6'))
 paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbf2adbd79683dc93db03454dfa18c2'))
 paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571'))
-paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '6e428384ce6a77207fa2c70d9f011990'))
+paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '35c6a241bcc1a1fc89508860d82ad62b'))
 paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'b4cbe1ac451005df6dad12e9ffdccca9'))
 paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd3570c02f71bcd78e60b3f31dc8f5b32'))
 paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', 'aaba49c038ba927f0a8e45c0c9a686ab'))
@@ -235,6 +236,7 @@ paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], vararg
 paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '776d536cac47c89073abc7ee524d5aec'))
 paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '34ea12ac9f10a65dccbc50100d12e607'))
 paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '46994d10276dd4cb803b4062b5d14329'))
+paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '731b21c62a4add60a33bd76d802ffc5c'))
 paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', 'b76ccca3735bea4a58a0dbf0d77c5393'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139'))
 paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc'))
@@ -360,7 +362,7 @@ paddle.fluid.layers.polynomial_decay (ArgSpec(args=['learning_rate', 'decay_step
 paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None), ('document', 'c717d9d1d78a53c809d01b8bc56f3cae'))
 paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'd9a95746353fd574be36dc28d8726c28'))
 paddle.fluid.layers.append_LARS (ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None), ('document', 'd24fa1e7d62ac8a534fc6a86002f84f8'))
-paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', '9588c64c26ffaef3c466e404a6af9d9b'))
+paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', 'f8b2727bccf0f368c997d7cf05847e49'))
 paddle.fluid.layers.linear_lr_warmup (ArgSpec(args=['learning_rate', 'warmup_steps', 'start_lr', 'end_lr'], varargs=None, keywords=None, defaults=None), ('document', '2ef3f5ca5cd71ea4217c418e5a7a0565'))
 paddle.fluid.contrib.InitState.__init__ (ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.contrib.StateCell.__init__ (ArgSpec(args=['self', 'inputs', 'states', 'out_state', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

paddle/fluid/framework/details/build_strategy.h

@@ -83,11 +83,11 @@ struct BuildStrategy {
 
   bool sync_batch_norm_{false};
 
-  bool memory_optimize_{true};
-  // TODO(dzhwinter):
-  // make enable_inplace, memory_optimize_
-  // memory_early_delete_ true by default
-  bool enable_inplace_{true};
+  // FIXME(liuwei1031) disable memory_optimzie and enable_inplace in 1.4
+  // to open them by default, we need to solve the fetch variable issue
+  bool memory_optimize_{false};
+
+  bool enable_inplace_{false};
 
   bool enable_sequential_execution_{false};
 

paddle/fluid/framework/ir/fc_fuse_pass.cc

@@ -48,17 +48,37 @@ void FCFusePass::ApplyImpl(ir::Graph* graph) const {
     GET_IR_NODE_FROM_SUBGRAPH(elementwise_add, elementwise_add, fc_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(mul_out, mul_out, fc_pattern);
 
+    auto base_op_desc = mul->Op();
     // Create an FC Node.
+    // OpDesc desc(base_op_desc, nullptr);
     OpDesc desc;
     std::string fc_x_in = subgraph.at(x)->Name();
     std::string fc_Y_in = w->Name();
     std::string fc_bias_in = fc_bias->Name();
     std::string fc_out_out = fc_out->Name();
+
     desc.SetInput("Input", std::vector<std::string>({fc_x_in}));
     desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
     desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
     desc.SetOutput("Out", std::vector<std::string>({fc_out_out}));
     desc.SetAttr("in_num_col_dims", mul->Op()->GetAttr("x_num_col_dims"));
+
+    // For anakin subgraph int8
+    // When in anakin subgraph int8 mode, the pattern like "fake_quant + mul +
+    // fake_dequant"
+    // can be detected by the quant_dequant_fuse_pass. This pass will add
+    // "input_scale",
+    // "weight_scale" which are extracted from fake_quant op and fake_dequant op
+    // to mul op,
+    // and then delete the fake_quant op and fake_dequant op in the graph. If
+    // the mul op
+    // has the scale info, we should add those to the fused fc.
+    if (base_op_desc->HasAttr("enable_int8")) {
+      desc.SetAttr("enable_int8", base_op_desc->GetAttr("enable_int8"));
+      desc.SetAttr("input_scale", base_op_desc->GetAttr("input_scale"));
+      desc.SetAttr("weight_scale", base_op_desc->GetAttr("weight_scale"));
+    }
+
     desc.SetType("fc");
     auto fc_node = g->CreateOpNode(&desc);  // OpDesc will be copied.
     GraphSafeRemoveNodes(graph, {mul, elementwise_add, mul_out});

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1640,32 +1640,31 @@ PDNode *patterns::FillConstantElementWiseMulFuse::operator()(
 void patterns::QuantDequantOpFuse::operator()(PDNode *quant_op_input,
                                               const std::string &op_type,
                                               const std::string &weight_name,
-                                              int times) {
+                                              int times,
+                                              const std::string &quant_type) {
   const int kNumFields = 5;
   const int kQuantizedWeightOffset = 0;
   const int kQuantizedOpOffset = 1;
   const int kQuantizedOpOutOffset = 2;
   const int kDequantOpOffset = 3;
   const int kDequantOpOutOffset = 4;
   // the quant op always be one.
-  auto quant_op_in_scale =
-      pattern->NewNode(GetNodeName("quant_op_in_scale"))
-          ->assert_is_op_input("fake_quantize_range_abs_max", "InScale")
-          ->AsInput();
-  auto quant_op = pattern->NewNode(GetNodeName("quant_op"))
-                      ->assert_is_op("fake_quantize_range_abs_max");
+  auto quant_op_in_scale = pattern->NewNode(GetNodeName("quant_op_in_scale"))
+                               ->assert_is_op_input(quant_type, "InScale")
+                               ->AsInput();
+  auto quant_op =
+      pattern->NewNode(GetNodeName("quant_op"))->assert_is_op(quant_type);
 
   auto quant_op_out_scale =
       pattern->NewNode(GetNodeName("quant_op_out_scale"))
-          ->assert_is_op_output("fake_quantize_range_abs_max", "OutScale")
+          ->assert_is_op_output(quant_type, "OutScale")
           ->assert_is_op_input("fake_dequantize_max_abs", "Scale")
           ->AsIntermediate();
 
-  auto quant_op_out =
-      pattern->NewNode(GetNodeName("quant_op_out"))
-          ->assert_is_op_output("fake_quantize_range_abs_max", "Out")
-          ->assert_is_op_input(op_type)
-          ->AsIntermediate();
+  auto quant_op_out = pattern->NewNode(GetNodeName("quant_op_out"))
+                          ->assert_is_op_output(quant_type, "Out")
+                          ->assert_is_op_input(op_type)
+                          ->AsIntermediate();
 
   // there are 'times' quantized and dequant op
   std::vector<PDNode *> nodes;

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -880,7 +880,8 @@ struct QuantDequantOpFuse : public PatternBase {
       : PatternBase(pattern, name_scope, "quant_dequant_fuse") {}
 
   void operator()(PDNode* quant_op_input, const std::string& op_name,
-                  const std::string& weight_name, int times = 1);
+                  const std::string& weight_name, int times,
+                  const std::string& quant_type);
 
   std::string GetNodeName(const std::string& op_type) {
     return PDNodeName(name_scope_, repr_, id_, op_type);

paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc

@@ -25,7 +25,8 @@ namespace framework {
 namespace ir {
 
 void RunQuantDequant(ir::Graph* graph, Scope* scope, int times,
-                     std::string op_type) {
+                     const std::string& op_type,
+                     const std::string& quant_type) {
   const std::string pattern_name = "quant_dequant_fuse";
   //  FusePassBase::Init(pattern_name, graph);
   const int kNumFields = 5;
@@ -38,14 +39,17 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times,
   GraphPatternDetector gpd;
   auto* x = gpd.mutable_pattern()
                 ->NewNode("x")
-                ->assert_is_op_input("fake_quantize_range_abs_max", "X")
+                ->assert_is_op_input(quant_type, "X")
                 ->AsInput();
 
   std::string quantized_op_type = "";
   std::string weight_name = "";
   if (op_type == "conv2d") {
     quantized_op_type = "conv2d";
     weight_name = "Filter";
+  } else if (op_type == "depthwise_conv2d") {
+    quantized_op_type = "depthwise_conv2d";
+    weight_name = "Filter";
   } else if (op_type == "conv2d_fusion") {
     quantized_op_type = "conv2d_fusion";
     weight_name = "Filter";
@@ -62,7 +66,7 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times,
   }
 
   patterns::QuantDequantOpFuse pattern(gpd.mutable_pattern(), pattern_name);
-  pattern(x, quantized_op_type, weight_name, times);
+  pattern(x, quantized_op_type, weight_name, times, quant_type);
 
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
@@ -103,7 +107,6 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times,
     std::unordered_set<const Node*> delete_nodes;
 
     for (int i = 0; i < times; i++) {
-      // max_range = (range * range) / weight_scale
       float max_range = boost::get<float>(
           nodes[i * kNumFields + kDequantOpOffset]->Op()->GetAttr("max_range"));
       float weight_scale = (range * range) / max_range;
@@ -118,7 +121,8 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times,
       new_op_desc.SetType(quantized_op_type);
 
       if (quantized_op_type == "conv2d" ||
-          quantized_op_type == "conv2d_fusion") {
+          quantized_op_type == "conv2d_fusion" ||
+          quantized_op_type == "depthwise_conv2d") {
         new_op_desc.SetInput("Input", {new_input});
         new_op_desc.SetOutput("Output", {new_output});
       } else if (quantized_op_type == "fc") {
@@ -156,11 +160,17 @@ void QuantDequantFusePass::ApplyImpl(ir::Graph* graph) const {
   const std::string pattern_name = "quant_dequant_fuse";
   FusePassBase::Init(pattern_name, graph);
 
-  std::unordered_set<std::string> quantized_op_types = {"conv2d", "mul"};
+  std::unordered_set<std::string> quant_types = {
+      "fake_quantize_range_abs_max", "fake_quantize_moving_average_abs_max"};
+
+  std::unordered_set<std::string> quantized_op_types = {"conv2d", "mul",
+                                                        "depthwise_conv2d"};
   auto* scope = param_scope();
-  for (auto& op_type : quantized_op_types) {
-    for (int i = 1; i <= 6; i++) {
-      RunQuantDequant(graph, scope, i, op_type);
+  for (auto& quant_type : quant_types) {
+    for (auto& op_type : quantized_op_types) {
+      for (int i = 6; i >= 1; i--) {
+        RunQuantDequant(graph, scope, i, op_type, quant_type);
+      }
     }
   }
 }

paddle/fluid/inference/anakin/convert/CMakeLists.txt

@@ -1,4 +1,9 @@
-cc_library(anakin_op_converter SRCS fc.cc conv2d.cc conv2d_fusion.cc elementwise.cc activation.cc pool2d.cc concat.cc split.cc relu.cc  softmax.cc batch_norm.cc reshape.cc flatten.cc transpose.cc density_prior_box.cc detection_out.cc scale.cc dropout.cc im2sequence.cc sum.cc DEPS anakin_engine framework_proto scope op_registry)
+cc_library(anakin_op_converter SRCS fc.cc conv2d.cc conv2d_fusion.cc
+elementwise.cc activation.cc pool2d.cc concat.cc split.cc relu.cc softmax.cc
+batch_norm.cc reshape.cc flatten.cc transpose.cc density_prior_box.cc
+detection_out.cc scale.cc dropout.cc im2sequence.cc sum.cc affine_channel.cc
+roi_align.cc helper.cc DEPS anakin_engine framework_proto scope op_registry
+gtest)
 
 cc_test(test_anakin_fc SRCS test_fc_op.cc DEPS anakin_op_converter mul_op SERIAL)
 cc_test(test_anakin_conv2d SRCS test_conv2d_op.cc DEPS anakin_op_converter conv_op im2col vol2col depthwise_conv SERIAL)
@@ -14,5 +19,5 @@ cc_test(test_anakin_flatten SRCS test_flatten_op.cc DEPS anakin_op_converter fla
 cc_test(test_anakin_transpose SRCS test_transpose_op.cc DEPS anakin_op_converter transpose_op SERIAL)
 cc_test(test_anakin_batch_norm SRCS test_batch_norm_op.cc DEPS anakin_op_converter batch_norm_op SERIAL)
 cc_test(test_anakin_dropout SRCS test_dropout_op.cc DEPS anakin_op_converter dropout_op SERIAL)
-#cc_test(test_anakin_im2sequence SRCS test_im2sequence_op.cc DEPS anakin_op_converter im2sequence_op im2col)
 cc_test(test_anakin_sum SRCS test_sum_op.cc DEPS  anakin_op_converter sum_op selected_rows_functor SERIAL)
+cc_test(test_anakin_affine_channel SRCS test_affine_channel_op.cc DEPS anakin_op_converter affine_channel_op SERIAL)

paddle/fluid/inference/anakin/convert/activation.cc

@@ -16,36 +16,42 @@
 #include <algorithm>
 #include <map>
 
-using anakin::graph::GraphGlobalMem;
-using anakin::AK_FLOAT;
-using anakin::saber::NV;
-using anakin::saber::Shape;
-
 namespace paddle {
 namespace inference {
 namespace anakin {
 
-ActivationOpConverter::ActivationOpConverter(const std::string &op_type)
+template <typename TargetT, ::anakin::Precision PrecisionT>
+ActivationOpConverter<TargetT, PrecisionT>::ActivationOpConverter(
+    const std::string &op_type)
     : op_type_(op_type) {
   auto it = anakin_op_types_.find(op_type_);
   PADDLE_ENFORCE(it != anakin_op_types_.end(),
                  "activation op type is not support");
   anakin_op_type_ = it->second;
 }
 
-void ActivationOpConverter::operator()(const framework::proto::OpDesc &op,
-                                       const framework::BlockDesc &block_desc,
-                                       const framework::Scope &scope,
-                                       bool test_mode) {
+template <typename TargetT, ::anakin::Precision PrecisionT>
+void ActivationOpConverter<TargetT, PrecisionT>::operator()(
+    const framework::proto::OpDesc &op, const framework::BlockDesc &block_desc,
+    const framework::Scope &scope, bool test_mode) {
   framework::OpDesc op_desc(op, nullptr);
   PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
   PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
 
   auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front();
   auto input_name = op_desc.Input("X").front();
   auto output_name = op_desc.Output("Out").front();
-  engine_->AddOp(op_name, "Activation", {input_name}, {output_name});
-  engine_->AddOpAttr(op_name, "type", anakin_op_type_);
+  this->engine_->AddOp(op_name, "Activation", {input_name}, {output_name});
+  this->engine_->AddOpAttr(op_name, "type", anakin_op_type_);
+
+  if (op_type_ == "swish") {
+    float beta = boost::get<float>(op_desc.GetAttr("beta"));
+    this->engine_->AddOpAttr(op_name, "clip_relu_num", beta);
+  }
+  if (op_type_ == "relu6") {
+    float threshold = boost::get<float>(op_desc.GetAttr("threshold"));
+    this->engine_->AddOpAttr(op_name, "clip_relu_num", threshold);
+  }
 }
 
 }  // namespace anakin
@@ -54,3 +60,5 @@ void ActivationOpConverter::operator()(const framework::proto::OpDesc &op,
 
 REGISTER_ANAKIN_OP_CONVERTER(sigmoid, SigmoidOpConverter);
 REGISTER_ANAKIN_OP_CONVERTER(tanh, TanhOpConverter);
+REGISTER_ANAKIN_OP_CONVERTER(swish, SwishOpConverter);
+REGISTER_ANAKIN_OP_CONVERTER(relu6, Relu6OpConverter);