- Prototype of third solution

- fix

- compilation fixes

- fix

- fixe

- fix

- fix

- compilation fix

- comment fix

- lint

update mkldnn conv_elementwise_add_fuse_pass ut

- NHWC changes to prelu

- alhpa dims

- UT fix

- fix to UT

- lint

- Some fixes

- added to BWD of prelu NHWC support

- reverted removal of resetting cu_layout in clearing of caching

paddle/fluid/framework/executor.cc

@@ -174,10 +174,11 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
                    bool force_disable_gc, bool keep_kid_scopes) {
   platform::RecordBlock b(block_id);
   if (FLAGS_use_mkldnn) EnableMKLDNN(pdesc);
+  auto ctx = Prepare(pdesc, block_id, skip_ref_cnt_vars, force_disable_gc);
 #ifdef PADDLE_WITH_MKLDNN
   platform::AttachPointerHashToMKLDNNKey(this, place_);
+  platform::RegisterModelLayout(ctx->ops_);
 #endif
-  auto ctx = Prepare(pdesc, block_id, skip_ref_cnt_vars, force_disable_gc);
   RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars,
                      keep_kid_scopes);
 }

paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.cc

@@ -117,7 +117,7 @@ ResidualConnectionMKLDNNFusePass::ResidualConnectionMKLDNNFusePass() {
       .IsType<std::vector<int>>()
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "AnyLayout"})
+      .IsStringIn({"NHWC", "NCHW", "AnyLayout"})
       .End();
 
   AddOpCompat(OpCompat("elementwise_add"))

paddle/fluid/framework/naive_executor.cc

@@ -41,6 +41,7 @@ void NaiveExecutor::Prepare(Scope *scope, const ProgramDesc &program_desc,
 void NaiveExecutor::Run() {
 #ifdef PADDLE_WITH_MKLDNN
   platform::AttachPointerHashToMKLDNNKey(this, place_);
+  platform::RegisterModelLayout(ops_);
 #endif
   platform::ScopedFlushDenormal flush;
   for (auto &op : ops_) {

paddle/fluid/operators/mkldnn/prelu_mkldnn_op.cc

@@ -50,13 +50,8 @@ class PReluMKLDNNHandler
       if (weights->dims().size() != x->dims().size()) {
         auto new_weights_dims = std::vector<int64_t>(x->dims().size(), 1);
         if (mode == "channel") {
-          if (data_format == "NHWC") {
-            new_weights_dims[x->dims().size() - 1] =
-                *std::max_element(weights_dims.begin(), weights_dims.end());
-          } else {
-            new_weights_dims[1] =
-                *std::max_element(weights_dims.begin(), weights_dims.end());
-          }
+          new_weights_dims[1] =
+              *std::max_element(weights_dims.begin(), weights_dims.end());
         }
         weights_dims = std::move(new_weights_dims);
       }

paddle/fluid/operators/mkldnn/test_mkldnn_op_nhwc.cc

@@ -94,7 +94,7 @@ TEST(test_pool2d_transpose_nhwc, cpu_place) {
 
 TEST(test_pool2d_relu_relu_nhwc, cpu_place) {
   framework::DDim dims({1, 4, 8, 512});           // NHWC shape
-  framework::DDim expected_dims({1, 512, 3, 7});  // NHWC expected shape
+  framework::DDim expected_dims({1, 512, 3, 7});  // NCHW expected shape
   platform::CPUPlace p;
   framework::Scope scope;
 

paddle/fluid/operators/prelu_op.cc

@@ -53,7 +53,7 @@ class PReluOp : public framework::OperatorWithKernel {
                             "For mode 'channel', data_format must be one of "
                             "NCHW and NHWC. But recevied data_format: %s",
                             data_format_str));
-      if (data_format_str == "NCHW") {
+      if (data_format_str == "NCHW" || ctx->IsRunMKLDNNKernel()) {
         PADDLE_ENFORCE_EQ(
             product(ctx->GetInputDim("Alpha")) == x_dim[1], true,
             platform::errors::InvalidArgument(
@@ -128,6 +128,24 @@ class PReluOp : public framework::OperatorWithKernel {
 #endif
     return framework::OpKernelType(input_data_type, ctx.GetPlace());
   }
+
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string &var_name, const Tensor &tensor,
+      const framework::OpKernelType &expected_kernel_type) const {
+#ifdef PADDLE_WITH_MKLDNN
+    // All inputs (including alpha) need shape rotating
+    if ((expected_kernel_type.data_layout_ == framework::DataLayout::kMKLDNN) &&
+        (tensor.layout() != framework::DataLayout::kMKLDNN) &&
+        paddle::platform::MKLDNNDeviceContext::tls()
+                .get_cur_paddle_data_layout() == framework::DataLayout::kNHWC) {
+      return framework::OpKernelType(expected_kernel_type.data_type_,
+                                     tensor.place(),
+                                     framework::DataLayout::kNHWC);
+    }
+#endif
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   tensor.place(), tensor.layout());
+  }
 };
 
 class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -212,6 +230,24 @@ class PReluGradOp : public framework::OperatorWithKernel {
 #endif
     return framework::OpKernelType(input_data_type, ctx.GetPlace());
   }
+
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string &var_name, const Tensor &tensor,
+      const framework::OpKernelType &expected_kernel_type) const {
+#ifdef PADDLE_WITH_MKLDNN
+    // All inputs (including alpha) need shape rotating
+    if ((expected_kernel_type.data_layout_ == framework::DataLayout::kMKLDNN) &&
+        (tensor.layout() != framework::DataLayout::kMKLDNN) &&
+        paddle::platform::MKLDNNDeviceContext::tls()
+                .get_cur_paddle_data_layout() == framework::DataLayout::kNHWC) {
+      return framework::OpKernelType(expected_kernel_type.data_type_,
+                                     tensor.place(),
+                                     framework::DataLayout::kNHWC);
+    }
+#endif
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   tensor.place(), tensor.layout());
+  }
 };
 
 template <typename T>

paddle/fluid/platform/mkldnn_helper.h

@@ -559,6 +559,27 @@ inline void GetGroupConvWeightsTz(std::vector<int64_t>& weights_tz,  // NOLINT
   }
 }
 
+inline void RegisterModelLayout(
+    std::vector<std::unique_ptr<framework::OperatorBase>>& ops) {
+  // Make some lambda with param of string
+  for (auto& op : ops) {
+    if (op->HasAttr("data_format")) {
+      auto data_format = op->Attr<std::string>("data_format");
+      platform::MKLDNNDeviceContext::tls().set_cur_paddle_data_layout(
+          data_format.compare("NHWC") == 0 ? framework::DataLayout::kNHWC
+                                           : framework::DataLayout::kNCHW);
+      return;
+    }
+    if (op->HasAttr("data_layout")) {
+      auto data_format = op->Attr<std::string>("data_layout");
+      platform::MKLDNNDeviceContext::tls().set_cur_paddle_data_layout(
+          data_format.compare("NHWC") == 0 ? framework::DataLayout::kNHWC
+                                           : framework::DataLayout::kNCHW);
+      return;
+    }
+  }
+}
+
 inline bool HasOpINT8DataType(const paddle::framework::OpDesc* op) {
   return (op->GetAttrIfExists<std::string>("mkldnn_data_type") == "int8" ||
           op->GetAttrIfExists<bool>("use_quantizer"));

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_elementwise_add_fuse_pass.py

@@ -25,17 +25,120 @@
 import hypothesis.strategies as st
 
 
-class TestConvElementwiseAddMkldnnFusePass(PassAutoScanTest):
+# the two inputs of elementwise_add are tensor
+class TestConvElementwiseAddMkldnnFusePass1(PassAutoScanTest):
     def is_program_valid(self, program_config: ProgramConfig) -> bool:
         attrs = [
             program_config.ops[i].attrs
             for i in range(len(program_config.ops))
         ]
-        # If the problem has been fixed, the judgment 
-        # needs to be deleted!!!
-        if attrs[1]['data_format'] == "NHWC":
+        if attrs[1]['data_format'] == "NHWC" and attrs[3]['axis'] == 0:
+            return False
+        if attrs[1]['data_format'] == "NCHW" and attrs[3]['axis'] == -1:
             return False
+        return True
+
+    def sample_program_config(self, draw):
+        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
+        dilations = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.sampled_from([1, 2, 4]))
+        paddings = draw(st.sampled_from([[0, 3], [1, 1], [1, 2, 3, 4]]))
+        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        axis = draw(st.sampled_from([-1, 0]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input():
+            if data_format == "NCHW":
+                return np.random.random(
+                    [batch_size, 48, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [batch_size, 64, 64, 48]).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(
+                [48, int(48 / groups), 3, 3]).astype(np.float32)
+
+        relu_op = OpConfig(
+            type="relu",
+            inputs={"X": ["input_data"]},
+            outputs={"Out": ["relu_out"]},
+            attrs={})
+
+        conv2d_op1 = OpConfig(
+            type="conv2d",
+            inputs={"Input": ["relu_out"],
+                    "Filter": ["conv_weight1"]},
+            outputs={"Output": ["conv_output1"]},
+            attrs={
+                "data_format": data_format,
+                "dilations": dilations,
+                "padding_algorithm": padding_algorithm,
+                "groups": groups,
+                "paddings": paddings,
+                "strides": strides
+            })
+
+        conv2d_op2 = OpConfig(
+            type="conv2d",
+            inputs={"Input": ["input_data"],
+                    "Filter": ["conv_weight2"]},
+            outputs={"Output": ["conv_output2"]},
+            attrs={
+                "data_format": data_format,
+                "dilations": dilations,
+                "padding_algorithm": padding_algorithm,
+                "groups": groups,
+                "paddings": paddings,
+                "strides": strides
+            })
+
+        elt_op = OpConfig(
+            type="elementwise_add",
+            inputs={"X": ["conv_output1"],
+                    "Y": ["conv_output2"]},
+            outputs={"Out": ["elementwise_output"]},
+            attrs={'axis': axis})
 
+        model_net = [relu_op, conv2d_op1, conv2d_op2, elt_op]
+
+        program_config = ProgramConfig(
+            ops=model_net,
+            weights={
+                "conv_weight1": TensorConfig(data_gen=partial(generate_weight)),
+                "conv_weight2": TensorConfig(data_gen=partial(generate_weight))
+            },
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input))
+            },
+            outputs=["elementwise_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["relu", "conv2d", "conv2d"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["conv_elementwise_add_mkldnn_fuse_pass"])
+
+
+'''
+class TestConvElementwiseAddMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        if "elementwise_weight" in program_config.weights:
+            if program_config.weights["elementwise_weight"].shape[0] == program_config.inputs["input_data1"].shape[1]:
+                if attrs[2]['axis'] != 1:
+                    return False
+            if program_config.weights["elementwise_weight"].shape[0] == program_config.inputs["input_data1"].shape[3]:
+                if attrs[2]['axis'] != -1:
+                    return False
         return True
 
     def sample_program_config(self, draw):
@@ -101,7 +204,7 @@ def generate_weight2():
                 "strides": strides
             })
 
-        if axis == -1 or axis == 0:
+        if axis == 0:
             elt_op = OpConfig(
                 type="elementwise_add",
                 inputs={"X": ["input_data1"],
@@ -118,14 +221,12 @@ def generate_weight2():
 
         model_net = [relu_op, conv2d_op, elt_op]
 
-        if axis == 1:
+        if axis == 0:
             program_config = ProgramConfig(
                 ops=model_net,
                 weights={
                     "conv_weight":
-                    TensorConfig(data_gen=partial(generate_weight1)),
-                    "elementwise_weight":
-                    TensorConfig(data_gen=partial(generate_weight2))
+                    TensorConfig(data_gen=partial(generate_weight1))
                 },
                 inputs={
                     "input_data1":
@@ -137,7 +238,9 @@ def generate_weight2():
                 ops=model_net,
                 weights={
                     "conv_weight":
-                    TensorConfig(data_gen=partial(generate_weight1))
+                    TensorConfig(data_gen=partial(generate_weight1)),
+                    "elementwise_weight":
+                    TensorConfig(data_gen=partial(generate_weight2))
                 },
                 inputs={
                     "input_data1":
@@ -154,7 +257,7 @@ def sample_predictor_configs(self, program_config):
     def test(self):
         self.run_and_statis(
             quant=False, passes=["conv_elementwise_add_mkldnn_fuse_pass"])
-
+'''
 
 if __name__ == "__main__":
     unittest.main()