Update layout autotune for module with no modified (#46541)

paddle/fluid/eager/auto_code_generator/generator/eager_gen.py

@@ -1093,7 +1093,7 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
         tensors_vector_list_str = "{ " + ",".join(
             amp_tensors_vector_list) + " }"
 
-        if len(amp_tensors_vector_list) == 0:
+        if len(amp_tensors_vector_list) == 0:  # or forward_api_name == "shape":
             layout_logic_str = ""
         else:
             after_call_str = f"{returns_type_str} {result_name} = {forward_function_name}({layout_inputs_call_args_str});\n"

paddle/fluid/eager/eager_layout_auto_tune.h

@@ -32,70 +32,50 @@ inline bool NeedTransLayout(
   }
   return false;
 }
-inline std::shared_ptr<EagerLayoutTransformer> BaseTransformer(
-    const std::string& op_name,
-    const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
-                               kSlotSmallVectorSize>& tensors_vector) {
-  std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
-  bool unstart =
-      (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
-       paddle::experimental::DataLayout::UNDEFINED);
-  auto first_layout = tensors_vector[0][0].layout();
-  VLOG(3) << "Layout autotune was is start ? " << (!unstart) << op_name
-          << "'s layout is " << first_layout;
 
-  transposer = std::make_shared<EagerLayoutTransformer>(
-      op_name, tensors_vector, first_layout);
-  return transposer;
-}
-
-// For agnostic op like add, relu, exp
 inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
     const std::string& op_name,
     const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
                                kSlotSmallVectorSize>& tensors_vector) {
-  auto desired_layout =
-      paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
-  auto default_layout =
-      paddle::imperative::LayoutAutoTune::Instance().GetDefaultLayout();
+  // For agnostic op like add, relu, exp
   auto first_layout = tensors_vector[0][0].layout();
-  if (NeedTransLayout(tensors_vector, first_layout)) {
+  auto desired_layout = DesiredLayout();
+  bool is_started =
+      !(desired_layout == paddle::experimental::DataLayout::UNDEFINED);
+  if (is_started && NeedTransLayout(tensors_vector, first_layout)) {
     bool need_trans_back = false;
     for (size_t i = 0; i < tensors_vector.size(); i++) {
       for (size_t idx = 0; idx < tensors_vector[0].size(); idx++) {
         if (4 != tensors_vector[i][idx].shape().size()) {
           need_trans_back = true;
-          VLOG(3) << "Agnostic op " << op_name << " shape is "
-                  << tensors_vector[i][idx].shape().size() << " and layout is "
-                  << tensors_vector[i][idx].layout();
         }
       }
     }
-    auto final_layout = need_trans_back ? default_layout : desired_layout;
+    auto final_layout = need_trans_back ? DefaultLayout() : desired_layout;
+    VLOG(4) << op_name << "'s has different layout, need trans to "
+            << final_layout;
     return std::make_shared<EagerLayoutTransformer>(
         op_name, tensors_vector, final_layout);
   }
-  return BaseTransformer(op_name, tensors_vector);
+  return std::make_shared<EagerLayoutTransformer>(
+      op_name, tensors_vector, first_layout);
 }
 
-// For lightly op like reduce
 template <typename T>
 inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
     const std::string& op_name,
     const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
                                kSlotSmallVectorSize>& tensors_vector,
     T* attr) {
-  VLOG(3) << "Lightly op " << op_name << "'s shape is "
-          << tensors_vector[0][0].shape().size() << " and layout is "
-          << tensors_vector[0][0].layout();
-
-  std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
-  transposer =
-      std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
-  return transposer;
+  // For lightly op like reduce
+  if (!(DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED)) {
+    VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
+    return std::make_shared<EagerLayoutTransformer>(
+        op_name, tensors_vector, tensors_vector[0][0].layout());
+  }
+  return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
 }
 
-// For lightly op like argmax
 template <typename T1, typename T2>
 inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
     const std::string& op_name,
@@ -103,28 +83,23 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
                                kSlotSmallVectorSize>& tensors_vector,
     T1* axis,
     T2* keep_dim) {
-  VLOG(3) << "Lightly op " << op_name << "'s shape is "
-          << tensors_vector[0][0].shape().size() << " and layout is "
-          << tensors_vector[0][0].layout();
-
+  // For lightly op like argmax
   return EagerLayoutAutotune<T1>(op_name, tensors_vector, axis);
 }
 
-// heavily string data_format, data_layout
 template <>
 inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
     const std::string& op_name,
     const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
                                kSlotSmallVectorSize>& tensors_vector,
     std::string* attr) {
-  auto first_layout = tensors_vector[0][0].layout();
+  // Heavily op with (string) data_format, data_layout
   auto transposer = std::make_shared<EagerLayoutTransformer>(
-      op_name, tensors_vector, first_layout);
-  if (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
-      paddle::experimental::DataLayout::UNDEFINED) {
+      op_name, tensors_vector, tensors_vector[0][0].layout());
+  if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
     // Layout autotune only supports model with convolutional layers
-    VLOG(3) << "Optimze Layout was not started " << op_name;
     if (op_name != "conv2d") {
+      VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
       return transposer;
     } else {
       auto data_type = tensors_vector[0][0].dtype();
@@ -134,7 +109,8 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
       bool is_tune_fp16 =
           (data_type == paddle::experimental::DataType::FLOAT16) &&
           (*attr == "NCHW");
-      VLOG(3) << "Conv2d_dy's dtype " << data_type << " format" << (*attr);
+      VLOG(4) << "LayoutAutoTune assert with dtype and layout, Current op : "
+              << op_name;
       if (is_tune_fp32) {
         paddle::imperative::LayoutAutoTune::Instance().SetDesiredLayout(
             paddle::experimental::DataLayout::NCHW);
@@ -147,58 +123,45 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
         paddle::imperative::LayoutAutoTune::Instance().SetDefaultLayout(
             paddle::experimental::DataLayout::NCHW);
       } else {
+        VLOG(4) << "DisableLayoutAutoTune accoding to Conv op"
+                << " dtype : " << data_type << " format : " << (*attr);
         egr::Controller::Instance().DisableLayoutAutoTune();
         return transposer;
       }
-      VLOG(3)
-          << "Tune the layout from " << *attr << " to "
-          << paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
+      VLOG(4) << "LayoutAutoTune from " << *attr << " to " << DesiredLayout();
     }
   }
 
   if (paddle::imperative::LayoutAutoTune::Instance().IsHeavilyLayoutSensitive(
           op_name)) {
-    VLOG(3)
-        << op_name
-        << "'s LayoutTransformer is EagerHeavilyLayoutSensitiveOpTransformer";
-    auto heavily_transposer =
-        std::make_shared<EagerHeavilyLayoutSensitiveOpTransformer>(op_name,
-                                                                   attr);
-    return heavily_transposer;
+    return std::make_shared<EagerHeavilyLayoutSensitiveOpTransformer>(op_name,
+                                                                      attr);
   }
-
-  VLOG(3) << op_name << "'s LayoutTransformer is unimplemented. Use default.";
-  return transposer;
+  return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
 }
 
-// lightly  transpose
 template <>
 inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
     const std::string& op_name,
     const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
                                kSlotSmallVectorSize>& tensors_vector,
     std::vector<int>* attr) {
-  auto first_layout = tensors_vector[0][0].layout();
-  std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
-  if (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
-      paddle::experimental::DataLayout::UNDEFINED) {
-    VLOG(3) << "Optimze Layout was not started" << op_name;
-    transposer = std::make_shared<EagerLayoutTransformer>(
-        op_name, tensors_vector, first_layout);
-    return transposer;
+  // lightly  transpose
+  if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
+    VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
+    return std::make_shared<EagerLayoutTransformer>(
+        op_name, tensors_vector, tensors_vector[0][0].layout());
   }
+
   if (op_name == "transpose2" &&
-      (tensors_vector[0][0].layout() ==
-       paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout())) {
+      (tensors_vector[0][0].layout() == DesiredLayout())) {
     auto trans = std::make_shared<EagerTransposeOpTransformer>(op_name);
     trans->SetAttr(attr,
                    tensors_vector[0][0].layout() ==
                        paddle::experimental::DataLayout::NHWC);
     return trans;
   }
-  transposer =
-      std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
-  return transposer;
+  return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
 }
 
 // lightly int argmax
@@ -210,19 +173,14 @@ EagerLayoutAutotune<paddle::experimental::Scalar, bool>(
                                kSlotSmallVectorSize>& tensors_vector,
     paddle::experimental::Scalar* axis,
     bool* keep_dim) {
-  auto first_layout = tensors_vector[0][0].layout();
-  std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
-  if (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
-      paddle::experimental::DataLayout::UNDEFINED) {
-    VLOG(3) << "Optimze Layout was not started" << op_name;
-    transposer = std::make_shared<EagerLayoutTransformer>(
-        op_name, tensors_vector, first_layout);
-    return transposer;
+  if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
+    VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
+    return std::make_shared<EagerLayoutTransformer>(
+        op_name, tensors_vector, tensors_vector[0][0].layout());
   }
-  auto desired_layout =
-      paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
+
   if (op_name == "argmax" &&
-      (tensors_vector[0][0].layout() == desired_layout) && (*keep_dim)) {
+      (tensors_vector[0][0].layout() == DesiredLayout()) && (*keep_dim)) {
     std::shared_ptr<EagerArgmaxOpTransformer> argmax_transform = nullptr;
     argmax_transform = std::make_shared<EagerArgmaxOpTransformer>(op_name);
     argmax_transform->SetAttr(axis,
@@ -230,12 +188,9 @@ EagerLayoutAutotune<paddle::experimental::Scalar, bool>(
                                   paddle::experimental::DataLayout::NHWC);
     return argmax_transform;
   }
-  transposer =
-      std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
-  return transposer;
+  return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
 }
 
-// lightly for flatten
 template <>
 inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune<int, int>(
     const std::string& op_name,
@@ -243,32 +198,22 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune<int, int>(
                                kSlotSmallVectorSize>& tensors_vector,
     int* start_axis,
     int* stop_axis) {
-  auto first_layout = tensors_vector[0][0].layout();
-  std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
-  auto desired_layout =
-      paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
-  if (desired_layout == paddle::experimental::DataLayout::UNDEFINED) {
-    VLOG(3) << "Optimze Layout was not started" << op_name;
-    transposer = std::make_shared<EagerLayoutTransformer>(
-        op_name, tensors_vector, first_layout);
-    return transposer;
+  if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
+    VLOG(4) << "Optimze Layout was not started" << op_name;
+    return std::make_shared<EagerLayoutTransformer>(
+        op_name, tensors_vector, tensors_vector[0][0].layout());
   }
-  bool no_tranpose = tensors_vector[0][0].layout() == desired_layout;
+
+  bool no_tranpose = tensors_vector[0][0].layout() == DesiredLayout();
   bool is_valid = ((*start_axis) == 1 && (*stop_axis) == 3);
   if (op_name == "flatten" || op_name == "flatten_contiguous_range") {
     if (no_tranpose && is_valid) {
-      std::shared_ptr<EagerFlattenOpTransformer> flatten_transform = nullptr;
-      flatten_transform = std::make_shared<EagerFlattenOpTransformer>(op_name);
-      return flatten_transform;
+      return std::make_shared<EagerFlattenOpTransformer>(op_name);
     }
   }
-
-  transposer =
-      std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
-  return transposer;
+  return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
 }
 
-// lightly int Concat
 template <>
 inline std::shared_ptr<EagerLayoutTransformer>
 EagerLayoutAutotune<paddle::experimental::Scalar>(
@@ -276,27 +221,26 @@ EagerLayoutAutotune<paddle::experimental::Scalar>(
     const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
                                kSlotSmallVectorSize>& tensors_vector,
     paddle::experimental::Scalar* axis) {
-  auto desired_layout =
-      paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
-  auto first_layout = tensors_vector[0][0].layout();
-  std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
-  if (desired_layout == paddle::experimental::DataLayout::UNDEFINED) {
-    VLOG(3) << "Optimze Layout was not started" << op_name;
-    transposer = std::make_shared<EagerLayoutTransformer>(
-        op_name, tensors_vector, first_layout);
-    return transposer;
+  if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
+    VLOG(4) << "Optimze Layout was not started" << op_name;
+    return std::make_shared<EagerLayoutTransformer>(
+        op_name, tensors_vector, tensors_vector[0][0].layout());
   }
 
+  auto desired_layout = DesiredLayout();
   if (NeedTransLayout(tensors_vector, desired_layout)) {
-    VLOG(3) << op_name << " need transpose to default layout";
-    transposer =
-        std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
-    return transposer;
-  } else {
-    auto trans = std::make_shared<EagerConcatOpTransformer>(op_name);
-    trans->SetAttr(axis, desired_layout);
-    return trans;
+    VLOG(4) << op_name << "'s has different layout";
+    return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
+  }
+  if (op_name == "Concat") {
+    if (desired_layout == tensors_vector[0][0].layout() &&
+        tensors_vector[0][0].shape().size() == 4) {
+      auto trans = std::make_shared<EagerConcatOpTransformer>(op_name);
+      trans->SetAttr(axis, desired_layout);
+      return trans;
+    }
   }
+  return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
 }
 
 }  // namespace egr

paddle/fluid/imperative/layout_autotune.cc

@@ -194,8 +194,10 @@ paddle::imperative::NameVarMap<VarType> AutoTuneLayout(
           (conv_in_type == framework::proto::VarType::FP16);
       if (is_tune_fp32) {
         LayoutAutoTune::Instance().SetDesiredLayout(DataLayout::NCHW);
+        LayoutAutoTune::Instance().SetDefaultLayout(DataLayout::NHWC);
       } else if (is_tune_fp16) {
         LayoutAutoTune::Instance().SetDesiredLayout(DataLayout::NHWC);
+        LayoutAutoTune::Instance().SetDefaultLayout(DataLayout::NCHW);
       } else {
         tracer->DisableLayoutAutoTune();
         return ins;

paddle/fluid/pybind/eager_properties.cc

@@ -184,6 +184,42 @@ PyObject* tensor_properties_get_shape(TensorObject* self, void* closure) {
     }
   }
 
+  auto desired_layout =
+      paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
+  auto default_layout =
+      paddle::imperative::LayoutAutoTune::Instance().GetDefaultLayout();
+  bool change_dim =
+      (desired_layout != default_layout &&
+       self->tensor.layout() == desired_layout && value.size() == 4);
+  VLOG(6) << "eager_properties 'Shape' method, layout autotune "
+          << " desired_layout: " << desired_layout
+          << " default_layout: " << default_layout
+          << " tensor layout: " << self->tensor.layout()
+          << " tensor's shape size is : " << value.size();
+  std::vector<int64_t> dims = value;
+  if (change_dim &&
+      paddle::framework::DataLayoutToString(desired_layout) == "NCHW") {
+    // NCHW -> NHWC
+    VLOG(6) << "layout autotune get Shape from NCHW -> NHWC " << value[0] << " "
+            << value[1] << " " << value[2] << " " << value[3] << " to "
+            << dims[0] << " " << dims[2] << " " << dims[3] << " " << dims[1];
+    value[0] = dims[0];
+    value[1] = dims[2];
+    value[2] = dims[3];
+    value[3] = dims[1];
+  } else if (change_dim &&
+             paddle::framework::DataLayoutToString(desired_layout) == "NHWC") {
+    // NHWC -> NCHW
+    VLOG(6) << "layout autotune get Shape from NHWC -> NCHW " << value[0] << " "
+            << value[1] << " " << value[2] << " " << value[3] << " to "
+            << dims[0] << " " << dims[3] << " " << dims[1] << " " << dims[2]
+            << " " << dims[1];
+    value[0] = dims[0];
+    value[1] = dims[3];
+    value[2] = dims[1];
+    value[3] = dims[2];
+  }
+
   return ToPyObject(value);
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }

paddle/fluid/pybind/imperative.cc

@@ -2044,8 +2044,49 @@ void BindImperative(py::module *m_ptr) {
           "shape",
           [](imperative::VarBase &self) {
             if (self.Var().IsType<framework::LoDTensor>()) {
-              return phi::vectorize<int>(
+              auto value = phi::vectorize<int>(
                   self.Var().Get<framework::LoDTensor>().dims());
+              auto tensor = self.Var().Get<framework::LoDTensor>();
+              auto tmp_value = value;
+              auto desired_layout =
+                  paddle::imperative::LayoutAutoTune::Instance()
+                      .GetDesiredLayout();
+              auto default_layout =
+                  paddle::imperative::LayoutAutoTune::Instance()
+                      .GetDefaultLayout();
+              bool change_dim =
+                  (desired_layout != default_layout &&
+                   tensor.layout() == desired_layout && value.size() == 4);
+              VLOG(6) << "'Shape' method, layout autotune,"
+                      << " desired_layout: " << desired_layout
+                      << " default_layout: " << default_layout
+                      << " tensor layout: " << tensor.layout()
+                      << " tensor's shape size is : " << value.size();
+
+              if (change_dim && paddle::framework::DataLayoutToString(
+                                    desired_layout) == "NCHW") {
+                VLOG(6) << "layout autotune get Shape from NHWC -> NCHW "
+                        << value[0] << " " << value[1] << " " << value[2] << " "
+                        << value[3] << " to " << tmp_value[3] << " "
+                        << tmp_value[1] << " " << tmp_value[2] << " "
+                        << tmp_value[1];
+                // NCHW -> NHWC
+                value[1] = tmp_value[2];
+                value[2] = tmp_value[3];
+                value[3] = tmp_value[1];
+              } else if (change_dim && paddle::framework::DataLayoutToString(
+                                           desired_layout) == "NHWC") {
+                VLOG(6) << "layout autotune get Shape from NHWC -> NCHW "
+                        << value[0] << " " << value[1] << " " << value[2] << " "
+                        << value[3] << " to " << tmp_value[0] << " "
+                        << tmp_value[3] << " " << tmp_value[1] << " "
+                        << tmp_value[2];
+                // NHWC -> NCHW
+                value[1] = tmp_value[3];
+                value[2] = tmp_value[1];
+                value[3] = tmp_value[2];
+              }
+              return value;
             } else if (self.Var().IsType<phi::SelectedRows>()) {
               return phi::vectorize<int>(
                   self.Var().Get<phi::SelectedRows>().value().dims());

paddle/phi/api/lib/data_transform.cc

@@ -205,7 +205,8 @@ phi::DenseTensor TransformData(phi::DenseTensor* tensor,
   if (NeedTransformLayout(tensor->layout(),
                           target_args_def.layout,
                           tensor->place(),
-                          transform_flag)) {
+                          transform_flag) &&
+      tensor->dims().size() != 1) {
     out = TransDataLayout(out, target_args_def.layout);
     trans_layout = true;
   }

python/paddle/fluid/tests/unittests/test_layout_autotune.py

@@ -93,18 +93,9 @@ class LayoutAutoTune(unittest.TestCase):
         return conv_out, predict
 
     def test_enable_autotune(self):
-        if self.use_autoune():
-            conv_out, predict = self.train(data_format="NCHW")
-            if paddle.fluid.core.use_layout_autotune():
-                self.assertEqual(conv_out.shape, [1, 14, 14, 8])
-                self.assertEqual(predict.shape, [1, 2])
-            else:
-                self.assertEqual(conv_out.shape, [1, 8, 14, 14])
-                self.assertEqual(predict.shape, [1, 2])
-        else:
-            conv_out, predict = self.train(data_format="NCHW")
-            self.assertEqual(conv_out.shape, [1, 8, 14, 14])
-            self.assertEqual(predict.shape, [1, 2])
+        conv_out, predict = self.train(data_format="NCHW")
+        self.assertEqual(conv_out.shape, [1, 8, 14, 14])
+        self.assertEqual(predict.shape, [1, 2])
 
     def test_transpose_op_transposer(self):
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
@@ -124,12 +115,8 @@ class LayoutAutoTune(unittest.TestCase):
         scaled.backward()
         scaler.minimize(optimizer, scaled)
 
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [1, 12, 8, 14])
-        else:
-            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [1, 12, 8, 14])
+        self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+        self.assertEqual(out.shape, [1, 12, 8, 14])
 
     def test_flatten_op_transposer(self):
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
@@ -143,12 +130,8 @@ class LayoutAutoTune(unittest.TestCase):
             # because it flatten the C and H dimensions.
             out = flatten(conv_out)
 
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [1, 112, 12])
-        else:
-            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [1, 112, 12])
+        self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+        self.assertEqual(out.shape, [1, 112, 12])
 
     def test_argmax_op_transposer_keep_dims(self):
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
@@ -157,41 +140,8 @@ class LayoutAutoTune(unittest.TestCase):
             conv_out = conv(data)
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.argmax(conv_out, axis=1, keepdim=True)
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [1, 14, 12, 1])
-        else:
-            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [1, 1, 14, 12])
-
-    def test_argmax_op_transposer_ff(self):
-        conv = paddle.nn.Conv2D(3, 8, (3, 3))
-        data = paddle.rand([1, 3, 16, 14])
-        with paddle.amp.auto_cast(level="O2"):
-            conv_out = conv(data)
-            # conv_out.shape = [1, 14, 12, 8] with NHWC
-            out = paddle.argmax(conv_out)
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [1])
-        else:
-            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [1])
-
-    def test_argmax_op_transposer_t(self):
-        conv = paddle.nn.Conv2D(3, 8, (3, 3))
-        data = paddle.rand([1, 3, 16, 14])
-        with paddle.amp.auto_cast(level="O2"):
-            conv_out = conv(data)
-            # conv_out.shape = [1, 14, 12, 8] with NHWC
-            out = paddle.argmax(conv_out)
-
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [1])
-        else:
-            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [1])
+        self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+        self.assertEqual(out.shape, [1, 1, 14, 12])
 
     def test_concat_op_transposer(self):
         in1 = paddle.rand([1, 8, 14, 12])
@@ -202,12 +152,8 @@ class LayoutAutoTune(unittest.TestCase):
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.concat(x=[conv_out, in1], axis=0)
 
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [2, 8, 14, 12])
-        else:
-            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [2, 8, 14, 12])
+        self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+        self.assertEqual(out.shape, [2, 8, 14, 12])
 
     def test_concat_op_no_transposer(self):
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
@@ -219,12 +165,8 @@ class LayoutAutoTune(unittest.TestCase):
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.concat(x=[conv_out1, conv_out2], axis=0)
 
-        if paddle.fluid.core.use_layout_autotune():
-            self.assertEqual(conv_out1.shape, [1, 14, 12, 8])
-            self.assertEqual(out.shape, [2, 14, 12, 8])
-        else:
-            self.assertEqual(conv_out1.shape, [1, 8, 14, 12])
-            self.assertEqual(out.shape, [2, 8, 14, 12])
+        self.assertEqual(conv_out1.shape, [1, 8, 14, 12])
+        self.assertEqual(out.shape, [2, 8, 14, 12])
 
 
 class TestAutoTuneAPI(unittest.TestCase):

python/paddle/nn/functional/conv.py

@@ -152,8 +152,8 @@ def _conv_nd(x,
             channel_dim = channel_dim + len(
                 x.shape) if channel_dim < 0 else channel_dim
             tmp_bias = _C_ops.reshape(
-                bias,
-                bias.shape + [1 for i in range(len(x.shape) - channel_dim - 1)])
+                bias, [1 for i in range(channel_dim)] + bias.shape +
+                [1 for i in range(len(x.shape) - channel_dim - 1)])
             return _C_ops.add(pre_bias, tmp_bias)
         else:
             return pre_bias