Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into move_sgd_to_phi

paddle/fluid/eager/api/utils/hook_utils.cc

@@ -52,49 +52,44 @@ void RegisterReduceHookForTensor(const paddle::experimental::Tensor& tensor,
   }
 }
 
-static void RetainGradForRegularNode(
-    const paddle::experimental::Tensor& tensor) {
-  AutogradMeta* meta = EagerUtils::unsafe_autograd_meta(tensor);
-  if (meta->RetainGrads()) {
+void RetainGradForTensor(const paddle::experimental::Tensor& tensor) {
+  if (IsLeafTensor(tensor)) {
+    // Leaf tensor's grad will always be retained
+    // Refer to implementation of AccumulationNode for more details
     return;
   } else {
-    meta->SetRetainGrads(true);
-  }
+    AutogradMeta* meta = EagerUtils::unsafe_autograd_meta(tensor);
+    if (meta->RetainGrads()) {
+      return;
+    } else {
+      meta->SetRetainGrads(true);
+    }
 
-  std::weak_ptr<paddle::experimental::Tensor> weak_grad_tensor =
-      meta->WeakGrad();
+    std::weak_ptr<paddle::experimental::Tensor> weak_grad_tensor =
+        meta->WeakGrad();
 
-  // Define Hook
-  auto hook = [weak_grad_tensor](const paddle::experimental::Tensor& t) {
-    if (!weak_grad_tensor.expired()) {
-      auto grad_tensor = weak_grad_tensor.lock();
-      if (t.defined()) {
-        VLOG(7) << "Set impl for RetainGrad Hook for tensor: " << t.name();
-        // Simply Copy impl() to grad_tensor
-        grad_tensor->set_impl(t.impl());
-        return *grad_tensor.get();
+    // Define Hook
+    auto hook = [weak_grad_tensor](const paddle::experimental::Tensor& t) {
+      if (!weak_grad_tensor.expired()) {
+        auto grad_tensor = weak_grad_tensor.lock();
+        if (t.defined()) {
+          VLOG(7) << "Set impl for RetainGrad Hook for tensor: " << t.name();
+          // Simply Copy impl() to grad_tensor
+          grad_tensor->set_impl(t.impl());
+          return *grad_tensor.get();
+        } else {
+          VLOG(7) << "Retain NULL paddle::experimental::Tensor in Grad Hook";
+          return paddle::experimental::Tensor();
+        }
       } else {
         VLOG(7) << "Retain NULL paddle::experimental::Tensor in Grad Hook";
         return paddle::experimental::Tensor();
       }
-    } else {
-      VLOG(7) << "Retain NULL paddle::experimental::Tensor in Grad Hook";
-      return paddle::experimental::Tensor();
-    }
-  };
+    };
 
-  // Append to GradientHooks
-  RegisterGradientHookForTensor(tensor,
-                                std::make_shared<egr::CppTensorHook>(hook));
-}
-
-void RetainGradForTensor(const paddle::experimental::Tensor& tensor) {
-  if (IsLeafTensor(tensor)) {
-    // Leaf tensor's grad will always be retained
-    // Refer to implementation of AccumulationNode for more details
-    return;
-  } else {
-    RetainGradForRegularNode(tensor);
+    // Append to GradientHooks
+    RegisterGradientHookForTensor(tensor,
+                                  std::make_shared<egr::CppTensorHook>(hook));
   }
 }
 

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -1156,11 +1156,13 @@ static std::string GenerateGradNodeCreationContent(
       grad_node_creation_str += paddle::string::Sprintf(
           SET_OUT_RANK_TEMPLATE, output_autograd_name, output_position);
 
-      const char* SET_HISTORY_TEMPLATE =
-          "    egr::EagerUtils::SetHistory(&%s, grad_node);\n";
-      grad_node_creation_str +=
-          paddle::string::Sprintf(SET_HISTORY_TEMPLATE, output_autograd_name);
-
+      // Intermediate Tensor does not require SetHistory
+      if (!output.intermediate()) {
+        const char* SET_HISTORY_TEMPLATE =
+            "    egr::EagerUtils::SetHistory(&%s, grad_node);\n";
+        grad_node_creation_str +=
+            paddle::string::Sprintf(SET_HISTORY_TEMPLATE, output_autograd_name);
+      }
       const char* SET_GRAD_IN_META_TEMPLATE =
           "    grad_node->SetGradInMeta(&%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
@@ -1173,17 +1175,20 @@ static std::string GenerateGradNodeCreationContent(
       grad_node_creation_str += paddle::string::Sprintf(
           SET_OUT_RANK_TEMPLATE, output_autograd_name, output_position);
 
-      const char* SET_HISTORY_TEMPLATE =
-          "    egr::EagerUtils::SetHistory(%s, grad_node);\n";
-      grad_node_creation_str +=
-          paddle::string::Sprintf(SET_HISTORY_TEMPLATE, output_autograd_name);
-
+      // Intermediate Tensor does not require SetHistory
+      if (!output.intermediate()) {
+        const char* SET_HISTORY_TEMPLATE =
+            "    egr::EagerUtils::SetHistory(%s, grad_node);\n";
+        grad_node_creation_str +=
+            paddle::string::Sprintf(SET_HISTORY_TEMPLATE, output_autograd_name);
+      }
       const char* SET_GRAD_IN_META_TEMPLATE =
           "    grad_node->SetGradInMeta(%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
           SET_GRAD_IN_META_TEMPLATE, output_autograd_name, output_position);
     }
 
+    // Intermediate Tensor does not require CheckAndRetainGrad
     if (!output.intermediate()) {
       VLOG(6) << "Generated Call RetainGradForTensor";
       const char* RETAIN_GRAD_TEMPLATE =

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

@@ -24,6 +24,17 @@ core_ops_args_info = {}
 core_ops_args_type_info = {}
 
 
+yaml_types_mapping = {
+    'int' : 'int', 'int32_t' : 'int32_t', 'int64_t' : 'int64_t',  'size_t' : 'size_t', \
+  'float' : 'float', 'double' : 'double', 'bool' : 'bool', \
+  'Backend' : 'Backend', 'DataLayout' : 'DataLayout', 'DataType' : 'DataType', \
+  'int64_t[]' : 'std::vector<int64_t>', 'int[]' : 'std::vector<int>',
+    'Tensor' : 'Tensor',
+    'Tensor[]' : 'std::vector<Tensor>',
+    'Tensor[Tensor[]]' : 'std::vector<std::vector<Tensor>>'
+}
+
+
 def ParseArguments():
     parser = argparse.ArgumentParser(
         description='Eager Code Generator Args Parser')
@@ -59,7 +70,9 @@ def IsPlainTensorType(string):
 
 
 def IsVectorTensorType(string):
-    vector_tensor_types = ['list(Tensor)']
+    vector_tensor_types = [
+        'std::vector<std::vector<Tensor>>', 'std::vector<Tensor>'
+    ]
     if string in vector_tensor_types:
         return True
     return False
@@ -180,6 +193,9 @@ def ParseYamlArgs(string):
         arg_name = m.group(3).split("=")[0].strip()
         default_value = m.group(3).split("=")[1].strip() if len(
             m.group(3).split("=")) > 1 else None
+
+        assert arg_type in yaml_types_mapping.keys()
+        arg_type = yaml_types_mapping[arg_type]
         if "Tensor" in arg_type:
             assert default_value is None
             inputs_list.append([arg_name, arg_type, i])
@@ -219,6 +235,10 @@ def ParseYamlReturnsWithName(string):
         m = re.search(pattern, ret)
         ret_type = m.group(1)
         ret_name = m.group(2)
+
+        assert ret_type in yaml_types_mapping.keys()
+        ret_type = yaml_types_mapping[ret_type]
+
         assert "Tensor" in ret_type
         returns_list.append([ret_name, ret_type, i])
 

paddle/fluid/eager/backward.cc

@@ -221,10 +221,11 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
                 << " 's name is: " << grad_output_tensor.name();
 
         auto* next_node = next_node_shared.get();
-
         if (!node_input_buffers_dict.count(next_node)) {
-          node_input_buffers_dict[next_node] =
-              std::make_unique<GradTensorHolder>(next_node->InputMeta());
+          const auto& input_meta = next_node->InputMeta();
+          auto grad_tensor_holder =
+              std::make_unique<GradTensorHolder>(input_meta);
+          node_input_buffers_dict[next_node] = std::move(grad_tensor_holder);
         }
         VLOG(6) << "Sum grad inputs for edge slot: " << edge_rank.first
                 << ", rank: " << edge_rank.second;

paddle/fluid/eager/grad_node_info.cc

@@ -244,7 +244,7 @@ GradNodeBase::ApplyGradientHooks(
     if (!out.defined() || !out.initialized()) {
       out = (*hook)(tensors[slot_id][rank]);
     } else {
-      // If more than one hook is registered, the input to the next hook func
+      // If more than one hook is registered, the input to the next hook func
       // should be the output of the previous hook
       out = (*hook)(out);
     }

paddle/fluid/eager/utils.cc

@@ -122,12 +122,21 @@ paddle::experimental::Tensor* EagerUtils::mutable_grad(
 void EagerUtils::SetHistory(std::vector<AutogradMeta*>* autograd_metas,
                             const std::shared_ptr<GradNodeBase>& grad_node) {
   for (const auto& autograd_meta : *autograd_metas) {
+    if (dynamic_cast<GradNodeAccumulation*>(autograd_meta->GradNode())) {
+      VLOG(6) << "Warning: Reseting GradNodeAccumulation for leaf tensor is "
+                 "detected";
+    }
     autograd_meta->SetGradNode(grad_node);
   }
 }
 
 void EagerUtils::SetHistory(AutogradMeta* autograd_meta,
                             const std::shared_ptr<GradNodeBase>& grad_node) {
+  if (dynamic_cast<GradNodeAccumulation*>(autograd_meta->GradNode())) {
+    VLOG(6)
+        << "Warning: Reseting GradNodeAccumulation for leaf tensor is detected";
+  }
+
   autograd_meta->SetGradNode(grad_node);
 }
 

paddle/fluid/framework/infershape_utils.cc

@@ -88,6 +88,8 @@ class InferShapeArgumentMappingContext : public phi::ArgumentMappingContext {
     return var_types[0] == proto::VarType::SELECTED_ROWS;
   }
 
+  bool IsForInferShape() const override { return true; }
+
  private:
   const InferShapeContext& ctx_;
 };
@@ -127,7 +129,9 @@ class CompatMetaTensor : public phi::MetaTensor {
       }
     } else {
       auto* var = BOOST_GET_CONST(VarDesc*, var_);
-      return phi::make_ddim(var->GetShape());
+
+      return var->GetShape().empty() ? phi::make_ddim({0UL})
+                                     : phi::make_ddim(var->GetShape());
     }
   }
 

paddle/fluid/framework/operator.h

@@ -489,6 +489,8 @@ class ExecutionArgumentMappingContext : public phi::ArgumentMappingContext {
     return ctx_.OutputVar(name)->IsType<phi::SelectedRows>();
   }
 
+  bool IsForInferShape() const override { return false; }
+
  private:
   const ExecutionContext& ctx_;
 };

paddle/fluid/framework/phi_utils.cc

@@ -125,6 +125,15 @@ phi::KernelKey FallBackToCpu(const OpKernelType& expected_kernel_key,
     return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(),
                           kernel_key.dtype());
   }
+#endif
+#ifdef PADDLE_WITH_IPU
+  if (platform::is_ipu_place(expected_kernel_key.place_)) {
+    VLOG(3) << "pten missing IPU kernel: " << op.Type()
+            << ", expected_kernel_key:" << expected_kernel_key
+            << ", fallbacking to CPU one!";
+    return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(),
+                          kernel_key.dtype());
+  }
 #endif
   return phi::KernelKey();
 }

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -453,6 +453,23 @@ if(WITH_MKLDNN)
   download_int8_data_without_verify(${INT8_GOOGLENET_MODEL_DIR} "GoogleNet_int8_model.tar.gz" )
   inference_analysis_api_int8_test_run_custom_warmup_batch_size(test_analyzer_int8_googlenet ${INT8_IMG_CLASS_TEST_APP} ${INT8_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH} 10)
 
+ # mobilenetv3_large_x1_0 int8
+ set(INT8_MOBILENETV3_LARGE_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv3_large")
+ set(INT8_MOBILENETV3_FILE_NAME "MobileNetV3_large_x1_0_infer.tar")
+ if (NOT EXISTS ${INT8_MOBILENETV3_LARGE_MODEL_DIR}/${INT8_MOBILENETV3_FILE_NAME})
+    inference_download_and_uncompress_without_verify(${INT8_MOBILENETV3_LARGE_MODEL_DIR} "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/" ${INT8_MOBILENETV3_FILE_NAME})
+ endif()
+ inference_analysis_test_run(test_analyzer_int8_mobilenetv3_large 
+    COMMAND ${INT8_IMG_CLASS_TEST_APP} 
+    ARGS --infer_model=${INT8_MOBILENETV3_LARGE_MODEL_DIR}/MobileNetV3_large_x1_0_infer
+        --infer_data=${IMAGENET_DATA_PATH} 
+        --warmup_batch_size=50
+        --batch_size=1
+        --enable_int8=true 
+        --cpu_num_threads=${CPU_NUM_THREADS_ON_CI} 
+        --iterations=100
+        --with_accuracy_layer=false)
+
   ### BFLOAT16 tests
 
   # build test binary to be used in subsequent tests
@@ -472,6 +489,17 @@ if(WITH_MKLDNN)
   # mobilenetv2 bfloat16
   inference_analysis_api_bfloat16_test_run(test_analyzer_bfloat16_mobilenetv2 ${BF16_IMG_CLASS_TEST_APP} ${INT8_MOBILENETV2_MODEL_DIR} ${IMAGENET_DATA_PATH})
 
+  # mobilenetv3_large 
+  inference_analysis_test_run(test_analyzer_bfloat16_mobilenetv3_large
+  COMMAND ${BF16_IMG_CLASS_TEST_APP}
+      ARGS --infer_model=${INT8_MOBILENETV3_LARGE_MODEL_DIR}/MobileNetV3_large_x1_0_infer
+        --infer_data=${IMAGENET_DATA_PATH} 
+        --batch_size=1
+        --enable_bf16=true
+        --paddle_num_threads=${CPU_NUM_THREADS_ON_CI}
+        --iterations=100
+        --with_accuracy_layer=false)
+
   ### Object detection models
   set(PASCALVOC_DATA_PATH "${INT8_DATA_DIR}/pascalvoc_val_head_300.bin")
   set(INT8_OBJ_DETECT_TEST_APP "test_analyzer_int8_object_detection")
@@ -739,6 +767,7 @@ if(WITH_MKLDNN)
     set_tests_properties(test_analyzer_quant_performance_benchmark PROPERTIES TIMEOUT 120)
     set_tests_properties(test_analyzer_int8_mobilenetv2 PROPERTIES TIMEOUT 120)
     set_tests_properties(test_analyzer_int8_mobilenetv1 PROPERTIES TIMEOUT 120)
+    set_tests_properties(test_analyzer_int8_mobilenetv3_large PROPERTIES TIMEOUT 120)
 endif()
 
 set_tests_properties(lite_resnet50_test PROPERTIES TIMEOUT 120)

paddle/fluid/inference/tests/api/analyzer_bfloat16_image_classification_tester.cc

@@ -14,13 +14,19 @@ limitations under the License. */
 
 #include "paddle/fluid/inference/api/paddle_analysis_config.h"
 #include "paddle/fluid/inference/tests/api/tester_helper.h"
+#include "paddle/fluid/platform/cpu_info.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
 void SetConfig(AnalysisConfig *cfg) {
-  cfg->SetModel(FLAGS_infer_model);
+  std::ifstream model_file(FLAGS_infer_model + "/__model__");
+  if (model_file.good())
+    cfg->SetModel(FLAGS_infer_model);
+  else
+    cfg->SetModel(FLAGS_infer_model + "/inference.pdmodel",
+                  FLAGS_infer_model + "/inference.pdiparams");
   cfg->DisableGpu();
   cfg->SwitchIrOptim();
   cfg->SwitchSpecifyInputNames();
@@ -38,7 +44,12 @@ TEST(Analyzer_bfloat16_image_classification, bfloat16) {
   // read data from file and prepare batches with test data
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInputs(&input_slots_all);
-  b_cfg.EnableMkldnnBfloat16();
+  if (FLAGS_enable_bf16 &&
+      platform::MayIUse(platform::cpu_isa_t::avx512_bf16)) {
+    b_cfg.EnableMkldnnBfloat16();
+  } else {
+    FLAGS_enable_bf16 = false;
+  }
   CompareBFloat16AndAnalysis(&cfg, &b_cfg, input_slots_all);
 }
 

paddle/fluid/inference/tests/api/analyzer_int8_image_classification_tester.cc

@@ -22,7 +22,12 @@ namespace inference {
 namespace analysis {
 
 void SetConfig(AnalysisConfig *cfg) {
-  cfg->SetModel(FLAGS_infer_model);
+  std::ifstream model_file(FLAGS_infer_model + "/__model__");
+  if (model_file.good())
+    cfg->SetModel(FLAGS_infer_model);
+  else
+    cfg->SetModel(FLAGS_infer_model + "/inference.pdmodel",
+                  FLAGS_infer_model + "/inference.pdiparams");
   cfg->DisableGpu();
   cfg->SwitchIrOptim();
   cfg->SwitchSpecifyInputNames();

paddle/fluid/inference/tests/api/tester_helper.h

@@ -213,15 +213,15 @@ std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
                     element_in_batch * 3 * 224 * 224,
                 3 * 224 * 224,
                 static_cast<float *>(images.data.data()) + i * 3 * 224 * 224);
-
-    std::copy_n(static_cast<int64_t *>(test_data[batch][1].data.data()) +
-                    element_in_batch,
-                1, static_cast<int64_t *>(labels.data.data()) + i);
+    if (FLAGS_with_accuracy_layer)
+      std::copy_n(static_cast<int64_t *>(test_data[batch][1].data.data()) +
+                      element_in_batch,
+                  1, static_cast<int64_t *>(labels.data.data()) + i);
   }
-
-  auto warmup_data = std::make_shared<std::vector<PaddleTensor>>(2);
+  auto warmup_data = std::make_shared<std::vector<PaddleTensor>>(
+      FLAGS_with_accuracy_layer ? 2 : 1);
   (*warmup_data)[0] = std::move(images);
-  (*warmup_data)[1] = std::move(labels);
+  if (FLAGS_with_accuracy_layer) (*warmup_data)[1] = std::move(labels);
   return warmup_data;
 }
 
@@ -254,9 +254,13 @@ void SetInputs(std::vector<std::vector<PaddleTensor>> *inputs,
   }
   for (auto i = 0; i < iterations; i++) {
     auto images = image_reader.NextBatch();
-    auto labels = label_reader.NextBatch();
-    inputs->emplace_back(
-        std::vector<PaddleTensor>{std::move(images), std::move(labels)});
+    std::vector<PaddleTensor> tmp_vec;
+    tmp_vec.push_back(std::move(images));
+    if (FLAGS_with_accuracy_layer) {
+      auto labels = label_reader.NextBatch();
+      tmp_vec.push_back(std::move(labels));
+    }
+    inputs->push_back(std::move(tmp_vec));
   }
 }
 
@@ -825,7 +829,8 @@ void CompareQuantizedAndAnalysis(
   SummarizePerformance("FP32", sample_latency_fp32, "INT8",
                        sample_latency_int8);
 
-  CompareAccuracy(quantized_outputs, analysis_outputs, compared_idx);
+  if (FLAGS_with_accuracy_layer)
+    CompareAccuracy(quantized_outputs, analysis_outputs, compared_idx);
 }
 
 void CompareBFloat16AndAnalysis(
@@ -864,7 +869,8 @@ void CompareBFloat16AndAnalysis(
   SummarizePerformance("FP32", sample_latency_fp32, "BF16",
                        sample_latency_bf16);
 
-  CompareAccuracy(bf16_outputs, analysis_outputs, compared_idx);
+  if (FLAGS_with_accuracy_layer)
+    CompareAccuracy(bf16_outputs, analysis_outputs, compared_idx);
 }
 
 void CompareAnalysisAndAnalysis(

paddle/fluid/operators/reduce_ops/reduce_mean_op.cc

@@ -18,6 +18,10 @@
 #include <utility>
 #include <vector>
 
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/unary.h"
+
 namespace paddle {
 namespace operators {
 
@@ -92,9 +96,13 @@ class __reduce_meanMaker__ : public ops::ReduceOpMaker {
   virtual std::string GetOpType() const { return "Reduce reduce_mean"; }
 };
 
+DELCARE_INFER_SHAPE_FUNCTOR(reduce_mean, ReduceMeanInferShapeFunctor,
+                            PT_INFER_META(phi::MeanRawInferMeta));
+
 REGISTER_OPERATOR(reduce_mean, ops::ReduceOp, __reduce_meanMaker__,
                   ops::ReduceMeanOpGradMaker<paddle::framework::OpDesc>,
-                  ops::ReduceMeanOpGradMaker<paddle::imperative::OpBase>);
+                  ops::ReduceMeanOpGradMaker<paddle::imperative::OpBase>,
+                  ReduceMeanInferShapeFunctor);
 REGISTER_OPERATOR(reduce_mean_grad, ops::ReduceGradOp,
                   ops::ReduceMeanDoubleGradDescMaker,
                   ops::ReduceMeanDoubleGradOpBaseMaker,

paddle/fluid/operators/reduce_ops/reduce_sum_op.cc

@@ -16,6 +16,10 @@
 
 #include <string>
 
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/unary.h"
+
 namespace paddle {
 namespace framework {
 class OpDesc;
@@ -98,10 +102,14 @@ class ReduceSumOpMaker : public ops::ReduceOpMaker {
   virtual std::string GetOpType() const { return "Reduce reduce_sum"; }
 };
 
+DELCARE_INFER_SHAPE_FUNCTOR(reduce_sum, ReduceSumInferShapeFunctor,
+                            PT_INFER_META(phi::ReduceInferMetaBase));
+
 REGISTER_OPERATOR(reduce_sum, ops::ReduceOp, ReduceSumOpMaker,
                   ops::ReduceSumVarTypeInference,
                   ops::ReduceSumOpGradMaker<paddle::framework::OpDesc>,
-                  ops::ReduceSumOpGradMaker<paddle::imperative::OpBase>);
+                  ops::ReduceSumOpGradMaker<paddle::imperative::OpBase>,
+                  ReduceSumInferShapeFunctor);
 REGISTER_OPERATOR(reduce_sum_grad, ops::ReduceGradOp,
                   ops::ReduceSumDoubleOpGradMaker<paddle::framework::OpDesc>,
                   ops::ReduceSumDoubleOpGradMaker<paddle::imperative::OpBase>,

paddle/fluid/operators/selu_op.cc

@@ -12,8 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/selu_op.h"
-
 #include <memory>
 #include <string>
 #include <unordered_map>
@@ -127,9 +125,3 @@ REGISTER_OPERATOR(selu, ops::SeluOp, ops::SeluOpMaker, ops::SeluOpInferVarType,
                   ops::SeluGradMaker<paddle::framework::OpDesc>,
                   ops::SeluGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(selu_grad, ops::SeluGradOp);
-REGISTER_OP_CPU_KERNEL(
-    selu, ops::SeluKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::SeluKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    selu_grad, ops::SeluGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::SeluGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/selu_op.cu

-/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-#include "paddle/fluid/operators/selu_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    selu, ops::SeluKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::SeluKernel<paddle::platform::CUDADeviceContext, double>);
-REGISTER_OP_CUDA_KERNEL(
-    selu_grad, ops::SeluGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::SeluGradKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/platform/device/ipu/ipu_strategy.cc

@@ -120,121 +120,151 @@ IpuStrategy::IpuStrategy() {
   RegisterGetter(options_getter, options_type, #name, "string",         \
                  [&]() { return popart_options.aliased_name; })
 
-#define ADD_POPART_ENUM_OPTION(name, EnumType) \
-  ADD_POPART_ENUM_OPTION_ALIAS(name, name, EnumType)
-
-#define ADD_POPART_BOOL_OPTION(name) ADD_POPART_BOOL_OPTION_ALIAS(name, name)
-
-#define ADD_POPART_UINT64_OPTION(name) \
-  ADD_POPART_UINT64_OPTION_ALIAS(name, name)
-
-#define ADD_POPART_DOUBLE_OPTION(name) \
-  ADD_POPART_DOUBLE_OPTION_ALIAS(name, name)
-
-#define ADD_POPART_STRING_OPTION(name) \
-  ADD_POPART_STRING_OPTION_ALIAS(name, name)
-
-  ADD_POPART_ENUM_OPTION(autodiffSettings.stitchStrategy,
-                         AutodiffStitchStrategy);
-  ADD_POPART_ENUM_OPTION(batchSerializationSettings.transformContext,
-                         BatchSerializationTransformContext);
-  ADD_POPART_ENUM_OPTION(batchSerializationSettings.method,
-                         BatchSerializationMethod);
-  ADD_POPART_ENUM_OPTION(batchSerializationSettings.batchSchedule,
-                         BatchSerializationBatchSchedule);
-  ADD_POPART_ENUM_OPTION(autoRecomputation, RecomputationType);
-  ADD_POPART_ENUM_OPTION(mergeVarUpdate, MergeVarUpdateType);
-  ADD_POPART_ENUM_OPTION(virtualGraphMode, VirtualGraphMode);
-  ADD_POPART_ENUM_OPTION(syntheticDataMode, SyntheticDataMode);
-  ADD_POPART_ENUM_OPTION(subgraphCopyingStrategy, SubgraphCopyingStrategy);
-  ADD_POPART_ENUM_OPTION(accumulationAndReplicationReductionType,
-                         ReductionType);
-  ADD_POPART_ENUM_OPTION(meanAccumulationAndReplicationReductionStrategy,
-                         MeanReductionStrategy);
-
-  ADD_POPART_STRING_OPTION(logDir);
-  ADD_POPART_STRING_OPTION(cachePath);
-  ADD_POPART_STRING_OPTION(partialsTypeMatMuls);
-  ADD_POPART_STRING_OPTION(customCodeletCompileFlags);
-  ADD_POPART_STRING_OPTION(serializedPoprithmsShiftGraphsDir);
-  ADD_POPART_STRING_OPTION(kahnTieBreaker);
-
-  ADD_POPART_UINT64_OPTION(executionPhaseSettings.phases);
-  ADD_POPART_UINT64_OPTION(executionPhaseSettings.stages);
-  ADD_POPART_UINT64_OPTION(batchSerializationSettings.factor);
-  ADD_POPART_UINT64_OPTION(firstDotOp);
-  ADD_POPART_UINT64_OPTION(finalDotOp);
-  ADD_POPART_UINT64_OPTION(numIOTiles);
-  ADD_POPART_UINT64_OPTION(mergeVarUpdateMemThreshold);
-  ADD_POPART_UINT64_OPTION(looseThresholdAtPeak);
-  ADD_POPART_UINT64_OPTION(accumulationFactor);
-  ADD_POPART_UINT64_OPTION(swapLimitScheduler);
-  ADD_POPART_UINT64_OPTION(globalReplicationFactor);
-  ADD_POPART_UINT64_OPTION(globalReplicaOffset);
-  ADD_POPART_UINT64_OPTION(defaultPrefetchBufferingDepth);
-  ADD_POPART_UINT64_OPTION(compilationProgressTotal);
-  ADD_POPART_UINT64_OPTION(transitiveClosureOptimizationThreshold);
-
-  ADD_POPART_BOOL_OPTION(batchSerializationSettings.concatOnVirtualGraphChange);
-  ADD_POPART_BOOL_OPTION(
+  ADD_POPART_ENUM_OPTION_ALIAS(autodiff_settings.stitch_strategy,
+                               autodiffSettings.stitchStrategy,
+                               AutodiffStitchStrategy);
+  ADD_POPART_ENUM_OPTION_ALIAS(batch_serialization_settings.transform_context,
+                               batchSerializationSettings.transformContext,
+                               BatchSerializationTransformContext);
+  ADD_POPART_ENUM_OPTION_ALIAS(batch_serialization_settings.method,
+                               batchSerializationSettings.method,
+                               BatchSerializationMethod);
+  ADD_POPART_ENUM_OPTION_ALIAS(batch_serialization_settings.batch_schedule,
+                               batchSerializationSettings.batchSchedule,
+                               BatchSerializationBatchSchedule);
+  ADD_POPART_ENUM_OPTION_ALIAS(auto_recomputation, autoRecomputation,
+                               RecomputationType);
+  ADD_POPART_ENUM_OPTION_ALIAS(merge_var_update, mergeVarUpdate,
+                               MergeVarUpdateType);
+  ADD_POPART_ENUM_OPTION_ALIAS(virtual_graph_mode, virtualGraphMode,
+                               VirtualGraphMode);
+  ADD_POPART_ENUM_OPTION_ALIAS(synthetic_data_mode, syntheticDataMode,
+                               SyntheticDataMode);
+  ADD_POPART_ENUM_OPTION_ALIAS(subgraph_copying_strategy,
+                               subgraphCopyingStrategy,
+                               SubgraphCopyingStrategy);
+  ADD_POPART_ENUM_OPTION_ALIAS(accumulation_and_replication_reduction_type,
+                               accumulationAndReplicationReductionType,
+                               ReductionType);
+  ADD_POPART_ENUM_OPTION_ALIAS(
+      mean_accumulation_and_replication_reduction_strategy,
+      meanAccumulationAndReplicationReductionStrategy, MeanReductionStrategy);
+
+  ADD_POPART_STRING_OPTION_ALIAS(log_dir, logDir);
+  ADD_POPART_STRING_OPTION_ALIAS(cache_path, cachePath);
+  ADD_POPART_STRING_OPTION_ALIAS(partials_type_matmuls, partialsTypeMatMuls);
+  ADD_POPART_STRING_OPTION_ALIAS(custom_codelet_compile_flags,
+                                 customCodeletCompileFlags);
+  ADD_POPART_STRING_OPTION_ALIAS(serialized_poprithms_shift_graphs_dir,
+                                 serializedPoprithmsShiftGraphsDir);
+  ADD_POPART_STRING_OPTION_ALIAS(kahn_tie_breaker, kahnTieBreaker);
+
+  ADD_POPART_UINT64_OPTION_ALIAS(execution_phase_settings.phases,
+                                 executionPhaseSettings.phases);
+  ADD_POPART_UINT64_OPTION_ALIAS(execution_phase_settings.stages,
+                                 executionPhaseSettings.stages);
+  ADD_POPART_UINT64_OPTION_ALIAS(batch_serialization_settings.factor,
+                                 batchSerializationSettings.factor);
+  ADD_POPART_UINT64_OPTION_ALIAS(first_dot_op, firstDotOp);
+  ADD_POPART_UINT64_OPTION_ALIAS(final_dot_op, finalDotOp);
+  ADD_POPART_UINT64_OPTION_ALIAS(num_io_tiles, numIOTiles);
+  ADD_POPART_UINT64_OPTION_ALIAS(merge_var_update_mem_threshold,
+                                 mergeVarUpdateMemThreshold);
+  ADD_POPART_UINT64_OPTION_ALIAS(loose_threshold_at_peak, looseThresholdAtPeak);
+  ADD_POPART_UINT64_OPTION_ALIAS(accumulation_factor, accumulationFactor);
+  ADD_POPART_UINT64_OPTION_ALIAS(swap_limit_scheduler, swapLimitScheduler);
+  ADD_POPART_UINT64_OPTION_ALIAS(global_replication_factor,
+                                 globalReplicationFactor);
+  ADD_POPART_UINT64_OPTION_ALIAS(global_replica_offset, globalReplicaOffset);
+  ADD_POPART_UINT64_OPTION_ALIAS(default_prefetch_buffering_depth,
+                                 defaultPrefetchBufferingDepth);
+  ADD_POPART_UINT64_OPTION_ALIAS(compilation_progress_total,
+                                 compilationProgressTotal);
+  ADD_POPART_UINT64_OPTION_ALIAS(transitive_closure_optimization_threshold,
+                                 transitiveClosureOptimizationThreshold);
+
+  ADD_POPART_BOOL_OPTION_ALIAS(
+      batch_serialization_settings.concat_on_virtual_graph_change,
+      batchSerializationSettings.concatOnVirtualGraphChange);
+  ADD_POPART_BOOL_OPTION_ALIAS(
+      batch_serialization_settings.concat_on_execution_phase_change,
       batchSerializationSettings.concatOnExecutionPhaseChange);
-  ADD_POPART_BOOL_OPTION(
+  ADD_POPART_BOOL_OPTION_ALIAS(
+      batch_serialization_settings.concat_on_pipeline_stage_change,
       batchSerializationSettings.concatOnPipelineStageChange);
-  ADD_POPART_BOOL_OPTION(strictOpVersions);
-  ADD_POPART_BOOL_OPTION(opxAliasChecking);
-  ADD_POPART_BOOL_OPTION(opxModifyChecking);
-  ADD_POPART_BOOL_OPTION(dotOpNames);
-  ADD_POPART_BOOL_OPTION(exportPoplarComputationGraph);
-  ADD_POPART_BOOL_OPTION(exportPoplarVertexGraph);
-  ADD_POPART_BOOL_OPTION(separateCallOpPdfs);
-  ADD_POPART_BOOL_OPTION(enableOutlining);
-  ADD_POPART_BOOL_OPTION(enableOutliningCopyCostPruning);
-  ADD_POPART_BOOL_OPTION(rearrangeAnchorsOnHost);
-  ADD_POPART_BOOL_OPTION(enablePrefetchDatastreams);
-  ADD_POPART_BOOL_OPTION(enableNonStableSoftmax);
-  ADD_POPART_BOOL_OPTION(enableReplicatedGraphs);
-  ADD_POPART_BOOL_OPTION(enableGradientAccumulation);
-  ADD_POPART_BOOL_OPTION(instrumentWithHardwareCycleCounter);
-  ADD_POPART_BOOL_OPTION(enablePipelining);
+  ADD_POPART_BOOL_OPTION_ALIAS(strict_op_versions, strictOpVersions);
+  ADD_POPART_BOOL_OPTION_ALIAS(opx_alias_checking, opxAliasChecking);
+  ADD_POPART_BOOL_OPTION_ALIAS(opx_modify_checking, opxModifyChecking);
+  ADD_POPART_BOOL_OPTION_ALIAS(dot_op_names, dotOpNames);
+  ADD_POPART_BOOL_OPTION_ALIAS(export_poplar_computation_graph,
+                               exportPoplarComputationGraph);
+  ADD_POPART_BOOL_OPTION_ALIAS(export_poplar_vertex_graph,
+                               exportPoplarVertexGraph);
+  ADD_POPART_BOOL_OPTION_ALIAS(separate_call_op_pdfs, separateCallOpPdfs);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_outlining, enableOutlining);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_outlining_copy_cost_pruning,
+                               enableOutliningCopyCostPruning);
+  ADD_POPART_BOOL_OPTION_ALIAS(rearrange_anchors_on_host,
+                               rearrangeAnchorsOnHost);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_prefetch_datastreams,
+                               enablePrefetchDatastreams);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_non_stable_softmax,
+                               enableNonStableSoftmax);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_replicated_graphs,
+                               enableReplicatedGraphs);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_gradient_accumulation,
+                               enableGradientAccumulation);
+  ADD_POPART_BOOL_OPTION_ALIAS(instrument_with_hardware_cycle_counter,
+                               instrumentWithHardwareCycleCounter);
   ADD_POPART_BOOL_OPTION_ALIAS(enable_pipelining, enablePipelining);
-  ADD_POPART_BOOL_OPTION(disableGradAccumulationTensorStreams);
-  ADD_POPART_BOOL_OPTION(compileEngine);
-  ADD_POPART_BOOL_OPTION(constantWeights);
-  ADD_POPART_BOOL_OPTION(enableEngineCaching);
-  ADD_POPART_BOOL_OPTION(enableMergeExchange);
-  ADD_POPART_BOOL_OPTION(enableFloatingPointChecks);
-  ADD_POPART_BOOL_OPTION(enableStochasticRounding);
+  ADD_POPART_BOOL_OPTION_ALIAS(disable_grad_accumulation_tensor_streams,
+                               disableGradAccumulationTensorStreams);
+  ADD_POPART_BOOL_OPTION_ALIAS(compile_engine, compileEngine);
+  ADD_POPART_BOOL_OPTION_ALIAS(constant_weights, constantWeights);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_engine_caching, enableEngineCaching);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_merge_exchange, enableMergeExchange);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_floating_point_checks,
+                               enableFloatingPointChecks);
   ADD_POPART_BOOL_OPTION_ALIAS(enable_stochastic_rounding,
                                enableStochasticRounding);
-  ADD_POPART_BOOL_OPTION(explicitRecomputation);
-  ADD_POPART_BOOL_OPTION(enableExplicitMainLoops);
-  ADD_POPART_BOOL_OPTION(useHostCopyOps);
-  ADD_POPART_BOOL_OPTION(aliasZeroCopy);
-  ADD_POPART_BOOL_OPTION(delayVarUpdates);
-  ADD_POPART_BOOL_OPTION(enableFullyConnectedPass);
-  ADD_POPART_BOOL_OPTION(enableSerializedMatmuls);
-  ADD_POPART_BOOL_OPTION(enableStableNorm);
-  ADD_POPART_BOOL_OPTION(decomposeGradSum);
-  ADD_POPART_BOOL_OPTION(enableDistributedReplicatedGraphs);
-  ADD_POPART_BOOL_OPTION(groupHostSync);
-  ADD_POPART_BOOL_OPTION(automaticLossScalingSettings.enabled);
-  ADD_POPART_BOOL_OPTION(instrumentWithHardwareCycleCounter);
-  ADD_POPART_BOOL_OPTION(enableSupportedDataTypeCasting);
-  ADD_POPART_BOOL_OPTION(groupNormStridedChannelGrouping);
-  ADD_POPART_BOOL_OPTION(scheduleNonWeightUpdateGradientConsumersEarly);
-
-  ADD_POPART_DOUBLE_OPTION(outlineSequenceBreakCost);
-  ADD_POPART_DOUBLE_OPTION(outlineThreshold);
-  ADD_POPART_DOUBLE_OPTION(timeLimitScheduler);
-  ADD_POPART_DOUBLE_OPTION(automaticLossScalingSettings.binEdgeLocation);
-  ADD_POPART_DOUBLE_OPTION(
+  ADD_POPART_BOOL_OPTION_ALIAS(explicit_recomputation, explicitRecomputation);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_explicit_main_loops,
+                               enableExplicitMainLoops);
+  ADD_POPART_BOOL_OPTION_ALIAS(use_host_copy_ops, useHostCopyOps);
+  ADD_POPART_BOOL_OPTION_ALIAS(alias_zero_copy, aliasZeroCopy);
+  ADD_POPART_BOOL_OPTION_ALIAS(delay_var_updates, delayVarUpdates);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_fully_connected_pass,
+                               enableFullyConnectedPass);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_serialized_matmuls,
+                               enableSerializedMatmuls);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_stable_norm, enableStableNorm);
+  ADD_POPART_BOOL_OPTION_ALIAS(decompose_grad_sum, decomposeGradSum);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_distributed_replicated_graphs,
+                               enableDistributedReplicatedGraphs);
+  ADD_POPART_BOOL_OPTION_ALIAS(group_host_sync, groupHostSync);
+  ADD_POPART_BOOL_OPTION_ALIAS(automatic_loss_scaling_settings.enabled,
+                               automaticLossScalingSettings.enabled);
+  ADD_POPART_BOOL_OPTION_ALIAS(instrument_with_hardware_cycle_counter,
+                               instrumentWithHardwareCycleCounter);
+  ADD_POPART_BOOL_OPTION_ALIAS(enable_supported_data_type_casting,
+                               enableSupportedDataTypeCasting);
+  ADD_POPART_BOOL_OPTION_ALIAS(group_norm_strided_channel_grouping,
+                               groupNormStridedChannelGrouping);
+  ADD_POPART_BOOL_OPTION_ALIAS(
+      schedule_non_weight_update_gradient_consumers_early,
+      scheduleNonWeightUpdateGradientConsumersEarly);
+
+  ADD_POPART_DOUBLE_OPTION_ALIAS(outline_sequence_break_cost,
+                                 outlineSequenceBreakCost);
+  ADD_POPART_DOUBLE_OPTION_ALIAS(outline_threshold, outlineThreshold);
+  ADD_POPART_DOUBLE_OPTION_ALIAS(time_limit_scheduler, timeLimitScheduler);
+  ADD_POPART_DOUBLE_OPTION_ALIAS(
+      automatic_loss_scaling_settings.bin_edge_location,
+      automaticLossScalingSettings.binEdgeLocation);
+  ADD_POPART_DOUBLE_OPTION_ALIAS(
+      automatic_loss_scaling_settings.threshold_upper_count_proportion,
       automaticLossScalingSettings.thresholdUpperCountProportion);
 
-#undef ADD_POPART_STRING_OPTION
-#undef ADD_POPART_DOUBLE_OPTION
-#undef ADD_POPART_UINT64_OPTION
-#undef ADD_POPART_BOOL_OPTION
-#undef ADD_POPART_ENUM_OPTION
 #undef ADD_POPART_STRING_OPTION_ALIAS
 #undef ADD_POPART_DOUBLE_OPTION_ALIAS
 #undef ADD_POPART_UINT64_OPTION_ALIAS
@@ -278,14 +308,14 @@ IpuStrategy::IpuStrategy() {
       });
 
   RegisterSetter(
-      container_options, "dotChecks",
+      container_options, "dot_checks",
       [&](const std::pair<std::string, std::string>& p) {
         std::uint64_t value = std::stoul(p.first);
         popart_options.dotChecks.insert(static_cast<popart::DotCheck>(value));
       });
 
   RegisterGetter(
-      vector_options_getter, options_type, "dotChecks", "vector", [&]() {
+      vector_options_getter, options_type, "dot_checks", "vector", [&]() {
         std::vector<std::string> res;
         for (auto x : popart_options.dotChecks) {
           res.push_back(std::to_string(static_cast<std::uint64_t>(x)));
@@ -293,7 +323,7 @@ IpuStrategy::IpuStrategy() {
         return res;
       });
 
-  RegisterSetter(container_options, "hardwareInstrumentations",
+  RegisterSetter(container_options, "hardware_instrumentations",
                  [&](const std::pair<std::string, std::string>& p) {
                    std::uint64_t value = std::stoul(p.first);
                    popart_options.hardwareInstrumentations.insert(
@@ -301,8 +331,8 @@ IpuStrategy::IpuStrategy() {
                  });
 
   RegisterGetter(
-      vector_options_getter, options_type, "hardwareInstrumentations", "vector",
-      [&]() {
+      vector_options_getter, options_type, "hardware_instrumentations",
+      "vector", [&]() {
         std::vector<std::string> res;
         for (auto x : popart_options.hardwareInstrumentations) {
           res.push_back(std::to_string(static_cast<std::uint64_t>(x)));
@@ -310,12 +340,12 @@ IpuStrategy::IpuStrategy() {
         return res;
       });
 
-  RegisterSetter(container_options, "customCodelets",
+  RegisterSetter(container_options, "custom_codelets",
                  [&](const std::pair<std::string, std::string>& p) {
                    popart_options.customCodelets.push_back(p.first);
                  });
 
-  RegisterGetter(vector_options_getter, options_type, "customCodelets",
+  RegisterGetter(vector_options_getter, options_type, "custom_codelets",
                  "vector", [&]() {
                    std::vector<std::string> res;
                    for (auto x : popart_options.customCodelets) {
@@ -324,44 +354,44 @@ IpuStrategy::IpuStrategy() {
                    return res;
                  });
 
-  RegisterSetter(container_options, "engineOptions",
+  RegisterSetter(container_options, "engine_options",
                  [&](const std::pair<std::string, std::string>& p) {
                    popart_options.engineOptions.emplace(p);
                  });
 
-  RegisterGetter(map_options_getter, options_type, "engineOptions", "map",
+  RegisterGetter(map_options_getter, options_type, "engine_options", "map",
                  [&]() { return popart_options.engineOptions; });
 
-  RegisterSetter(container_options, "reportOptions",
+  RegisterSetter(container_options, "report_options",
                  [&](const std::pair<std::string, std::string>& p) {
                    popart_options.reportOptions.emplace(p);
                  });
 
-  RegisterGetter(map_options_getter, options_type, "reportOptions", "map",
+  RegisterGetter(map_options_getter, options_type, "report_options", "map",
                  [&]() { return popart_options.reportOptions; });
 
-  RegisterSetter(container_options, "convolutionOptions",
+  RegisterSetter(container_options, "convolution_options",
                  [&](const std::pair<std::string, std::string>& p) {
                    popart_options.convolutionOptions.emplace(p);
                  });
 
-  RegisterGetter(map_options_getter, options_type, "convolutionOptions", "map",
+  RegisterGetter(map_options_getter, options_type, "convolution_options", "map",
                  [&]() { return popart_options.convolutionOptions; });
 
-  RegisterSetter(container_options, "lstmOptions",
+  RegisterSetter(container_options, "lstm_options",
                  [&](const std::pair<std::string, std::string>& p) {
                    popart_options.lstmOptions.emplace(p);
                  });
 
-  RegisterGetter(map_options_getter, options_type, "lstmOptions", "map",
+  RegisterGetter(map_options_getter, options_type, "lstm_options", "map",
                  [&]() { return popart_options.lstmOptions; });
 
-  RegisterSetter(container_options, "gclOptions",
+  RegisterSetter(container_options, "gcl_options",
                  [&](const std::pair<std::string, std::string>& p) {
                    popart_options.gclOptions.emplace(p);
                  });
 
-  RegisterGetter(map_options_getter, options_type, "gclOptions", "map",
+  RegisterGetter(map_options_getter, options_type, "gcl_options", "map",
                  [&]() { return popart_options.gclOptions; });
 }
 
@@ -415,21 +445,21 @@ void IpuStrategy::SetTensorLocation(const std::string& tensor,
         "Unknown tensor location: %s", tensor));
   }
 
-  if (opt == "minElementsForOffChip") {
+  if (opt == "min_elements_for_off_chip") {
     settings->minElementsForOffChip = value;
-  } else if (opt == "minElementsForReplicatedTensorSharding") {
+  } else if (opt == "min_elements_for_replicated_tensor_sharding") {
     settings->minElementsForReplicatedTensorSharding = value;
-  } else if (opt == "onChip") {
+  } else if (opt == "on_chip") {
     settings->location.storage = value > 0 ? popart::TensorStorage::OnChip
                                            : popart::TensorStorage::OffChip;
-  } else if (opt == "useReplicatedTensorSharding") {
+  } else if (opt == "use_replicated_tensor_sharding") {
     settings->location.replicatedTensorSharding =
         value > 0 ? popart::ReplicatedTensorSharding::On
                   : popart::ReplicatedTensorSharding::Off;
-  } else if (opt == "useIOTilesToLoad") {
+  } else if (opt == "use_io_tiles_to_load") {
     settings->location.loadTileSet =
         value > 0 ? popart::TileSet::IO : popart::TileSet::Compute;
-  } else if (opt == "useIOTilesToStore") {
+  } else if (opt == "use_io_tiles_to_store") {
     settings->location.storageTileSet =
         value > 0 ? popart::TileSet::IO : popart::TileSet::Compute;
   } else {
@@ -464,6 +494,20 @@ std::string IpuStrategy::GetOptionType(const std::string& option) {
   return options_type[option];
 }
 
+std::vector<std::string> IpuStrategy::GetAllOptionNames() {
+  std::vector<std::string> names;
+  for (auto& option : options_getter) {
+    names.push_back(option.first);
+  }
+  for (auto& option : vector_options_getter) {
+    names.push_back(option.first);
+  }
+  for (auto& option : map_options_getter) {
+    names.push_back(option.first);
+  }
+  return names;
+}
+
 void IpuStrategy::EnablePattern(const std::string& t) {
   VLOG(10) << "enable popart pattern: " << t;
   popart_patterns.enablePattern(t, true);

paddle/fluid/platform/device/ipu/ipu_strategy.h

@@ -24,7 +24,8 @@ namespace paddle {
 namespace platform {
 namespace ipu {
 
-struct IpuStrategy {
+class IpuStrategy {
+ public:
   IpuStrategy();
 
   // TODO(alleng) create PaddleOptions
@@ -75,22 +76,30 @@ struct IpuStrategy {
   // custom ops
   std::vector<IpuCustomOpIdentifier> custom_ops;
 
- private:
-  std::map<std::string, std::function<void(bool)>> bool_options;
-  std::map<std::string, std::function<void(std::uint64_t)>> uint64_options;
-  std::map<std::string, std::function<void(double)>> double_options;
-  std::map<std::string, std::function<void(std::string)>> string_options;
-  std::map<std::string,
-           std::function<void(std::pair<std::string, std::string>)>>
-      container_options;
+ public:
+  void AddBoolOption(const std::string &option, bool value);
+  void AddUint64Option(const std::string &option, std::uint64_t value);
+  void AddDoubleOption(const std::string &option, double value);
+  void AddStringOption(const std::string &option, const std::string &value);
+  void InsertStringOption(const std::string &option, const std::string &value);
+  void InsertStringPairOption(const std::string &option, const std::string &key,
+                              const std::string &value);
+  void SetTensorLocation(const std::string &tensor, const std::string &option,
+                         std::uint64_t value);
+  void AddCustomOp(const std::string &paddle_op, const std::string &popart_op,
+                   const std::string &domain, int version);
 
-  std::map<std::string, std::function<std::string()>> options_getter;
-  std::map<std::string, std::function<std::vector<std::string>()>>
-      vector_options_getter;
-  std::map<std::string, std::function<std::map<std::string, std::string>()>>
-      map_options_getter;
-  std::map<std::string, std::string> options_type;
+  std::string GetOption(const std::string &);
+  std::vector<std::string> GetVectorOption(const std::string &);
+  std::map<std::string, std::string> GetMapOption(const std::string &);
+  std::string GetOptionType(const std::string &);
+  std::vector<std::string> GetAllOptionNames();
+
+  void EnablePattern(const std::string &t);
+  void DisablePattern(const std::string &t);
+  const bool IsPatternEnabled(const std::string &t);
 
+ private:
   template <typename ValueType>
   void set(
       const std::string &key, ValueType value,
@@ -117,27 +126,20 @@ struct IpuStrategy {
     return it->second();
   }
 
- public:
-  void AddBoolOption(const std::string &option, bool value);
-  void AddUint64Option(const std::string &option, std::uint64_t value);
-  void AddDoubleOption(const std::string &option, double value);
-  void AddStringOption(const std::string &option, const std::string &value);
-  void InsertStringOption(const std::string &option, const std::string &value);
-  void InsertStringPairOption(const std::string &option, const std::string &key,
-                              const std::string &value);
-  void SetTensorLocation(const std::string &tensor, const std::string &option,
-                         std::uint64_t value);
-  void AddCustomOp(const std::string &paddle_op, const std::string &popart_op,
-                   const std::string &domain, int version);
-
-  std::string GetOption(const std::string &);
-  std::vector<std::string> GetVectorOption(const std::string &);
-  std::map<std::string, std::string> GetMapOption(const std::string &);
-  std::string GetOptionType(const std::string &);
+  std::map<std::string, std::function<void(bool)>> bool_options;
+  std::map<std::string, std::function<void(std::uint64_t)>> uint64_options;
+  std::map<std::string, std::function<void(double)>> double_options;
+  std::map<std::string, std::function<void(std::string)>> string_options;
+  std::map<std::string,
+           std::function<void(std::pair<std::string, std::string>)>>
+      container_options;
 
-  void EnablePattern(const std::string &t);
-  void DisablePattern(const std::string &t);
-  const bool IsPatternEnabled(const std::string &t);
+  std::map<std::string, std::function<std::string()>> options_getter;
+  std::map<std::string, std::function<std::vector<std::string>()>>
+      vector_options_getter;
+  std::map<std::string, std::function<std::map<std::string, std::string>()>>
+      map_options_getter;
+  std::map<std::string, std::string> options_type;
 };
 
 }  // namespace ipu

paddle/fluid/pybind/pybind.cc

@@ -3919,6 +3919,8 @@ All parameter, weight, gradient are variables in Paddle.
              }
              return res;
            })
+      .def("get_all_option_names",
+           &platform::ipu::IpuStrategy::GetAllOptionNames)
       .def("enable_pattern", &platform::ipu::IpuStrategy::EnablePattern)
       .def("disable_pattern", &platform::ipu::IpuStrategy::DisablePattern)
       .def("is_pattern_enabled", &platform::ipu::IpuStrategy::IsPatternEnabled);

paddle/infrt/dialect/phi/pass/proto_arg_map_context.h

@@ -46,6 +46,8 @@ class ProtoArgumentMappingContext : public phi::ArgumentMappingContext {
   bool IsDenseTensorOutput(const std::string& name) const override;
   bool IsSelectedRowsOutput(const std::string& name) const override;
 
+  bool IsForInferShape() const override { return false; }
+
  private:
   mlir::Operation* op_;
   const std::unordered_map<std::string, uint8_t>& input_map_;

paddle/phi/core/compat/arg_map_context.h

@@ -91,6 +91,10 @@ class ArgumentMappingContext {
 
   virtual bool IsDenseTensorOutput(const std::string& name) const = 0;
   virtual bool IsSelectedRowsOutput(const std::string& name) const = 0;
+
+  // use this function to mark it comes from InferShapeArgumentMappingContext
+  // and will be used in infershape
+  virtual bool IsForInferShape() const = 0;
 };
 
 }  // namespace phi

paddle/phi/infermeta/unary.cc

@@ -375,7 +375,7 @@ void ReshapeWithXShapeInferMeta(const MetaTensor& x,
   ReshapeInferMeta(x, shape, out, config);
 }
 
-/*  Why not use ReduceInferMeta directly?
+/*  Why not use ReduceInferMetaBase directly?
     Because we need make InferMetaFunction's args follow the design of api.yaml
 */
 void SumInferMeta(const MetaTensor& x,
@@ -383,22 +383,53 @@ void SumInferMeta(const MetaTensor& x,
                   DataType dtype,
                   bool keep_dim,
                   MetaTensor* out) {
-  ReduceInferMetaBase(x, axis, keep_dim, dtype, out);
+  bool reduce_all = false;
+  ReduceInferMetaBase(x, axis, keep_dim, reduce_all, dtype, out);
 }
 
 void ReduceInferMetaBase(const MetaTensor& x,
                          const std::vector<int64_t>& axis,
                          bool keep_dim,
+                         bool reduce_all,
                          DataType dtype,
                          MetaTensor* out) {
-  bool reduce_all = true;
-  std::set<int64_t> dims_set(axis.begin(), axis.end());
+  auto x_rank = x.dims().size();
+
+  std::vector<int64_t> formated_axis = axis;
+  for (size_t i = 0; i < axis.size(); ++i) {
+    PADDLE_ENFORCE_LT(axis[i],
+                      x_rank,
+                      errors::InvalidArgument(
+                          "The reduce dim index %d should be in the "
+                          "range [-dimension(X), dimension(X)] "
+                          "which dimesion = %d. But received dim index = %d.",
+                          i,
+                          x_rank,
+                          axis[i]));
+    PADDLE_ENFORCE_GE(axis[i],
+                      -x_rank,
+                      errors::InvalidArgument(
+                          "The reduce dim index %d should be in the "
+                          "range [-dimension(X), dimension(X)] "
+                          "which dimesion = %d. But received dim index = %d.",
+                          i,
+                          x_rank,
+                          axis[i]));
+
+    if (axis[i] < 0) {
+      formated_axis[i] = axis[i] + x_rank;
+    }
+  }
+
+  bool full_dim = true;
+  std::set<int64_t> dims_set(formated_axis.begin(), formated_axis.end());
   for (int64_t i = 0; i < x.dims().size(); ++i) {
     if (dims_set.find(i) == dims_set.end()) {
-      reduce_all = false;
+      full_dim = false;
       break;
     }
   }
+  reduce_all = reduce_all || full_dim;
 
   std::vector<int64_t> out_dim_vector;
   if (keep_dim) {
@@ -441,11 +472,20 @@ void ReduceInferMetaBase(const MetaTensor& x,
   out->set_layout(x.layout());
 }
 
-void ReduceInferMeta(const MetaTensor& x,
-                     const std::vector<int64_t>& axis,
-                     bool keep_dim,
-                     MetaTensor* out) {
-  ReduceInferMetaBase(x, axis, keep_dim, DataType::UNDEFINED, out);
+void MeanRawInferMeta(const MetaTensor& x,
+                      const std::vector<int64_t>& axis,
+                      bool keep_dim,
+                      bool reduce_all,
+                      MetaTensor* out) {
+  ReduceInferMetaBase(x, axis, keep_dim, reduce_all, DataType::UNDEFINED, out);
+}
+
+void MeanInferMeta(const MetaTensor& x,
+                   const std::vector<int64_t>& axis,
+                   bool keep_dim,
+                   MetaTensor* out) {
+  bool reduce_all = false;
+  ReduceInferMetaBase(x, axis, keep_dim, reduce_all, DataType::UNDEFINED, out);
 }
 
 void TransferLayoutInferMeta(const MetaTensor& x,

paddle/phi/infermeta/unary.h

@@ -86,13 +86,20 @@ void ReshapeWithXShapeInferMeta(const MetaTensor& x,
 void ReduceInferMetaBase(const MetaTensor& x,
                          const std::vector<int64_t>& axis,
                          bool keep_dim,
+                         bool reduce_all,
                          DataType dtype,
                          MetaTensor* out);
 
-void ReduceInferMeta(const MetaTensor& x,
-                     const std::vector<int64_t>& axis,
-                     bool keep_dim,
-                     MetaTensor* out);
+void MeanRawInferMeta(const MetaTensor& x,
+                      const std::vector<int64_t>& axis,
+                      bool keep_dim,
+                      bool reduce_all,
+                      MetaTensor* out);
+
+void MeanInferMeta(const MetaTensor& x,
+                   const std::vector<int64_t>& axis,
+                   bool keep_dim,
+                   MetaTensor* out);
 
 void SumInferMeta(const MetaTensor& x,
                   const std::vector<int64_t>& axis,

paddle/phi/kernels/cpu/selu_grad_kernel.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/selu_grad_kernel.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/selu_grad_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    selu_grad, CPU, ALL_LAYOUT, phi::SeluGradKernel, float, double) {}

paddle/phi/kernels/cpu/selu_kernel.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/selu_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/selu_kernel_impl.h"
+
+PD_REGISTER_KERNEL(selu, CPU, ALL_LAYOUT, phi::SeluKernel, float, double) {}

paddle/phi/kernels/gpu/selu_grad_kernel.cu

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/selu_grad_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/selu_grad_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    selu_grad, GPU, ALL_LAYOUT, phi::SeluGradKernel, float, double) {}

paddle/phi/kernels/gpu/selu_kernel.cu

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/selu_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/selu_kernel_impl.h"
+
+PD_REGISTER_KERNEL(selu, GPU, ALL_LAYOUT, phi::SeluKernel, float, double) {}

paddle/phi/kernels/impl/selu_grad_kernel_impl.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle/phi/kernels/impl/selu_kernel_impl.h"
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+template <typename T, typename Context>
+void SeluGradKernel(const Context& dev_ctx,
+                    const DenseTensor& out,
+                    const DenseTensor& dout,
+                    float scale,
+                    float alpha,
+                    DenseTensor* dx) {
+  auto dx_ptr = dev_ctx.template Alloc<T>(dx);
+  SeluGradFunctor<T> functor(
+      out.data<T>(), dout.data<T>(), alpha, scale, dx_ptr);
+  size_t limit = static_cast<size_t>(out.numel());
+  paddle::platform::ForRange<Context> for_range(dev_ctx, limit);
+  for_range(functor);
+}
+}  // namespace phi

paddle/fluid/operators/selu_op.h → paddle/phi/kernels/impl/selu_kernel_impl.h

-/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
 
 #pragma once
 #include <string>
-#include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math.h"
 #include "paddle/fluid/platform/for_range.h"
+#include "paddle/phi/core/dense_tensor.h"
 
-namespace paddle {
-namespace operators {
+namespace phi {
 
 template <typename T>
 struct SeluFunctor {
@@ -32,7 +31,7 @@ struct SeluFunctor {
   HOSTDEVICE void operator()(size_t idx) const {
     T x_ele = x_data_ptr_[idx];
     if (x_ele <= 0) {
-      x_ele = alpha_ * real_exp(x_ele) - alpha_;
+      x_ele = alpha_ * paddle::operators::real_exp(x_ele) - alpha_;
     }
     y_data_ptr_[idx] = scale_ * x_ele;
   }
@@ -44,8 +43,11 @@ struct SeluFunctor {
 
 template <typename T>
 struct SeluGradFunctor {
-  SeluGradFunctor(const T* y_data_ptr, const T* dy_data_ptr, float alpha,
-                  float scale, T* dx_data_ptr)
+  SeluGradFunctor(const T* y_data_ptr,
+                  const T* dy_data_ptr,
+                  float alpha,
+                  float scale,
+                  T* dx_data_ptr)
       : y_data_ptr_(y_data_ptr),
         dy_data_ptr_(dy_data_ptr),
         alpha_(alpha),
@@ -71,53 +73,16 @@ struct SeluGradFunctor {
   T* dx_data_ptr_;
 };
 
-template <typename DeviceContext, typename T>
-class SeluKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    using Tensor = framework::Tensor;
-
-    auto* x = context.Input<Tensor>("X");
-    auto* out = context.Output<Tensor>("Out");
-
-    float alpha = context.Attr<float>("alpha");
-    float scale = context.Attr<float>("scale");
-
-    auto out_ptr = out->mutable_data<T>(context.GetPlace());
-
-    SeluFunctor<T> functor(x->data<T>(), alpha, scale, out_ptr);
-
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    size_t limit = static_cast<size_t>(x->numel());
-    platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
-    for_range(functor);
-  }
-};
-
-template <typename DeviceContext, typename T>
-class SeluGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    using Tensor = framework::Tensor;
-
-    auto* out = context.Input<Tensor>("Out");
-    auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
-    auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
-
-    float alpha = context.Attr<float>("alpha");
-    float scale = context.Attr<float>("scale");
-
-    auto dx_ptr = dx->mutable_data<T>(context.GetPlace());
-
-    SeluGradFunctor<T> functor(out->data<T>(), dout->data<T>(), alpha, scale,
-                               dx_ptr);
-
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    size_t limit = static_cast<size_t>(out->numel());
-    platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
-    for_range(functor);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
+template <typename T, typename Context>
+void SeluKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                float scale,
+                float alpha,
+                DenseTensor* out) {
+  auto out_ptr = dev_ctx.template Alloc<T>(out);
+  SeluFunctor<T> functor(x.data<T>(), alpha, scale, out_ptr);
+  size_t limit = static_cast<size_t>(x.numel());
+  paddle::platform::ForRange<Context> for_range(dev_ctx, limit);
+  for_range(functor);
+}
+}  // namespace phi

paddle/phi/kernels/math_kernel.h

@@ -156,7 +156,7 @@ DenseTensor Mean(const Context& dev_ctx,
                  bool keep_dim) {
   auto dense_out = phi::Empty<T, Context>(dev_ctx);
   MetaTensor meta_out(&dense_out);
-  ReduceInferMetaBase(x, axis, keep_dim, x.dtype(), &meta_out);
+  ReduceInferMetaBase(x, axis, keep_dim, false, x.dtype(), &meta_out);
   MeanKernel<T, Context>(dev_ctx, x, axis, keep_dim, &dense_out);
   return dense_out;
 }

paddle/phi/kernels/primitive/compute_primitives.h

@@ -136,7 +136,9 @@ __device__ __forceinline__ T BlockYReduce(T val, ReduceOp reducer) {
   return shared_memory[threadIdx.x];
 }
 
-// Swap data
+/**
+ * @brief Swap data
+ */
 template <typename T>
 __device__ __forceinline__ void Swap(T* first_value, T* second_value) {
   T t_value;
@@ -145,7 +147,9 @@ __device__ __forceinline__ void Swap(T* first_value, T* second_value) {
   (*second_value) = t_value;
 }
 
-// swap with monotonic_type
+/**
+ * @brief Swap data according to  monotonic_type.
+ */
 template <typename T>
 __device__ __forceinline__ void Comparator(T* first_value,
                                            T* second_value,
@@ -155,6 +159,9 @@ __device__ __forceinline__ void Comparator(T* first_value,
   }
 }
 
+/**
+ * @brief Swap data and data index according to  monotonic_type.
+ */
 template <typename T, typename IndexType>
 __device__ __forceinline__ void ComparatorWithIndex(T* first_value,
 
@@ -170,6 +177,18 @@ __device__ __forceinline__ void ComparatorWithIndex(T* first_value,
   }
 }
 
+/**
+ * @brief get the last pow of 2
+ */
+__device__ inline int GetLastPow2(int n) {
+  n |= (n >> 1);
+  n |= (n >> 2);
+  n |= (n >> 4);
+  n |= (n >> 8);
+  n |= (n >> 16);
+  return std::max(1, n - (n >> 1));
+}
+
 }  // namespace details
 
 /**
@@ -453,6 +472,29 @@ __device__ __forceinline__ void Reduce(T* out,
   }
 }
 
+/*
+* @brief Fill register with a constant according to OpFunc
+*
+* @template paraments
+* InT: The data type of in1 and in2.
+* OutT: The data type of out.
+* NX: The number of data columns loaded by each thread.
+* NY: The number of data rows loaded by each thread.
+* BlockSize: Identifies the current device thread index method. Currently only
+* GPU was supported.
+* OpFunc: Compute functor which has an operator() as following
+*     template <typename InT>
+*     struct XxxFunctor {
+*       HOSTDEVICE InT operator()()
+* const {
+*         return a;
+*       }
+*     };
+*
+* @param
+* out: The register pointer of out, the size is NX * NY.
+* compute: Compute function which was declared like OpFunc<InT>().
+*/
 template <typename InT,
           typename OutT,
           int NX,
@@ -466,6 +508,33 @@ __device__ __forceinline__ void ElementwiseConstant(OutT* out, OpFunc compute) {
   }
 }
 
+/*
+* @brief Get ReturnsCount random data fromm compute according to state, state
+* can be curandStatePhilox4_32_10_t, hiprandStatePhilox4_32_10_t which has beed
+* initialized.
+*
+* @template paraments
+* StateType: the type of state, can be curandStatePhilox4_32_10_t or
+* hiprandStatePhilox4_32_10_t.
+* OutT: the type of out register.
+* ReturnsCount: The number of random data generated by OpFunc.
+* BlockSize: Identifies the current device thread index method. Currently only
+* GPU was supported.
+* OpFunc: Compute functor which has an operator() as following
+*     template <typename T>
+*     struct XxxFunctor {
+*       HOSTDEVICE InT operator()(StateType state)
+* const {
+*         return ranomd(state);  // Returns ReturnsCount random numbers with
+* data type T
+*       }
+*     };
+*
+* @param
+* out: The register pointer of out, the size is NX * NY.
+* compute: Compute function which was declared like OpFunc<T>().
+*/
+
 template <typename StateType,
           typename OutT,
           int ReturnsCount,
@@ -481,131 +550,208 @@ __device__ __forceinline__ void ElementwiseRandom(OutT* out,
   }
 }
 
-// attention please set share_size = blockDim.x;
-// data and b are the register pointer
-#define shared_size 64
-template <typename InT,
-          typename OutT,
-          int NX,
-          int NY,
-          int BlockSize,
-          class OpFunc>
+/*
+* @brief Complete the prefix and in the block, each thread calculates 2 data,
+* the size of out and in is 2, and BlockDim.x must be less then 512.
+*
+* @template paraments
+* InT: the type of input register.
+* OutT: the type of out register.
+* BlockSize: Identifies the current device thread index method. Currently only
+* GPU was supported.
+* OpFunc: Compute functor which has an operator() as following
+*     template <typename T>
+*     struct XxxFunctor {
+*       HOSTDEVICE InT operator()(T a, T b)
+* const {
+*         return a + b;
+*       }
+*     };
+*
+* @param
+* out: The register pointer of out, the size is 2;
+* in: The register pointer of input, the size is 2;
+* compute: Compute function which was declared like OpFunc<T>().
+*/
+
+#define SHARED_SIZE_LIMIT 512
+template <typename InT, typename OutT, int BlockSize, class OpFunc>
 __device__ __forceinline__ void Cumsum(OutT* out,
                                        const InT* in,
                                        OpFunc compute) {
-  __shared__ InT temp[shared_size * 2 + (shared_size * 2) / 32];
+  constexpr int kSize = SHARED_SIZE_LIMIT * 2 + (SHARED_SIZE_LIMIT * 2) / 32;
+  __shared__ InT temp[kSize];
+  int stride_size = blockDim.x;
   int tidx = threadIdx.x;
   temp[tidx + tidx / 32] = in[0];
-  temp[shared_size + tidx + (shared_size + tidx) / 32] = in[1];
-  for (int stride = 1; stride <= blockDim.x; stride *= 2) {
+  temp[stride_size + tidx + (stride_size + tidx) / 32] = in[1];
+  for (int stride = 1; stride <= stride_size; stride *= 2) {
     __syncthreads();
     int index = (tidx + 1) * 2 * stride - 1;
     if (index < (blockDim.x * 2)) {
-      temp[index + index / 32] += temp[index - stride + (index - stride) / 32];
+      temp[index + index / 32] =
+          compute(temp[index + index / 2],
+                  temp[index - stride + (index - stride) / 32]);
     }
   }
   for (int stride = (blockDim.x * 2) / 4; stride > 0; stride /= 2) {
     __syncthreads();
     int index = (tidx + 1) * 2 * stride - 1;
     if ((index + stride) < (blockDim.x * 2)) {
-      temp[index + stride + (stride + index) / 32] +=
-          temp[index + (index) / 32];
+      temp[index + stride + (stride + index) / 32] =
+          compute(temp[index + stride + (stride + index) / 32],
+                  temp[index + (index) / 32]);
     }
   }
 
   __syncthreads();
   out[0] = static_cast<OutT>(temp[tidx + tidx / 32]);
   out[1] =
-      static_cast<OutT>(temp[tidx + shared_size + (tidx + shared_size) / 32]);
+      static_cast<OutT>(temp[tidx + stride_size + (tidx + stride_size) / 32]);
 }
-
-#define SHARED_SIZE_LIMIT \
-  1024  // each thread load 2 data from global memory so SHARED_SIZE_LIMIT must
-        // larger than blockDim.x * 2
-// if monotonic_type = 1 then increase
-// if gridDim.x > 1 please set monotonic_type = blockIdx.x & 1; blockIdx.x % 2
-// == 1 the increase
-template <typename T>
-__device__ __forceinline__ void Sort(T* dst,
-                                     const T* src_data,
+#undef SHARED_SIZE_LIMIT
+
+/*
+* @brief Sort data in this block, each thread calculates 2 data, the size of out
+* and in is 2, and BlockDim.x must be less then 512.
+*
+* @template paraments
+* InT: the type of input register.
+* OutT: the type of out register.
+* BlockSize: Identifies the current device thread index method. Currently only
+* GPU was supported.
+*
+* @param
+* out: The register pointer of out, the size is 2.
+* in: The register pointer of input, the size is 2.
+* num: The num of this block
+* monotonic_type: if monotonic_type = 1 then sorted in ascending order, eles
+* sorted in escending.
+*/
+#define SHARED_SIZE_LIMIT 1024
+// each thread load 2 data from global memory so SHARED_SIZE_LIMIT must
+// larger than blockDim.x * 2
+template <typename InT, typename OutT, int BlockSize>
+__device__ __forceinline__ void Sort(OutT* out,
+                                     const InT* in,
                                      int num,
                                      int monotonic_type) {
-  // todo: set  num = Pow2(num)
+  int upper_bound = blockDim.x;
+  // update upper_bound
+  upper_bound = std::min(details::GetLastPow2(num), upper_bound);
   // shareMem for value and index  num must smaller than SHARED_SIZE_LIMIT / 2
-  __shared__ T value[SHARED_SIZE_LIMIT];  // shareMem's size must larger than
-                                          // blockDim * 2
-  // Copy value and index from src and src_index
-  value[threadIdx.x] = src_data[0];
-  value[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = src_data[1];
+  __shared__ InT value[SHARED_SIZE_LIMIT];
+  int stride_size = blockDim.x;
+  // shareMem's size must larger than blockDim * 2
+  // Copy value from in
+  value[threadIdx.x] = in[0];
+  value[threadIdx.x + stride_size] = in[1];
   // make bitonicSort
-  for (int size = 2; size < num; size <<= 1) {
+  for (int size = 2; size < upper_bound; size <<= 1) {
     int bitonic_type = (threadIdx.x & (size / 2)) != 0;
     for (int stride = size / 2; stride > 0; stride >>= 1) {
       __syncthreads();
       int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1));
-      details::Comparator<T>(&value[pos], &value[pos + stride], bitonic_type);
+      details::Comparator<InT>(&value[pos], &value[pos + stride], bitonic_type);
     }
   }
   // last sort
-  for (int stride = SHARED_SIZE_LIMIT / 2; stride > 0; stride >>= 1) {
+  for (int stride = stride_size; stride > 0; stride >>= 1) {
     __syncthreads();
     int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1));
     // last sort when monotonic_type = 1 then increase
-    details::Comparator<T>(&value[pos], &value[pos + stride], monotonic_type);
+    details::Comparator<InT>(&value[pos], &value[pos + stride], monotonic_type);
   }
   __syncthreads();
-  dst[0] = value[threadIdx.x];
-  dst[1] = value[threadIdx.x + (SHARED_SIZE_LIMIT / 2)];
+  out[0] = static_cast<OutT>(value[threadIdx.x]);
+  out[1] = static_cast<OutT>(value[threadIdx.x + stride_size]);
 }
 
-template <typename T, typename IndexType>
-__device__ __forceinline__ void Sort(T* dst,
-                                     IndexType* dst_index,
-                                     const T* src_data,
-                                     IndexType* src_index,
+/*
+* @brief Sort data with data_index in this block, each thread calculates 2 data,
+* the size of out and in is 2, and BlockDim.x must be less then 512.
+*
+* @template paraments
+* InT: The type of input register.
+* OutT: The type of out register.
+* IndexType: The type of index.
+* BlockSize: Identifies the current device thread index method. Currently only
+* GPU was supported.
+*
+* @param
+* out: The register pointer of out, the size is 2.
+* out_index: The register pointer of out_index, the size is 2.
+* in: The register pointer of input, the size is 2.
+* in_index: The register pointer of in_index, the size is 2.
+* num: The num of this block.
+* monotonic_type: if monotonic_type = 1 then sorted in ascending order, eles
+* sorted in escending.
+*/
+template <typename InT, typename OutT, typename IndexType, int BlockSize>
+__device__ __forceinline__ void Sort(OutT* out,
+                                     IndexType* out_index,
+                                     const InT* in,
+                                     IndexType* in_index,
                                      int num,
                                      int monotonic_type) {
-  // todo: set  num = Pow2(num)
+  int upper_bound = blockDim.x;
+  // update upper_bound
+  upper_bound = std::min(details::GetLastPow2(num), upper_bound);
   // shareMem for value and index  num must smaller than SHARED_SIZE_LIMIT / 2
-  __shared__ T value[SHARED_SIZE_LIMIT];  // shareMem's size must larger than
-                                          // blockDim * 2
+  __shared__ InT value[SHARED_SIZE_LIMIT];
+  // shareMem's size must larger than blockDim * 2
   __shared__ IndexType index[SHARED_SIZE_LIMIT];
-  // Copy value and index from src and src_index
-  value[threadIdx.x] = src_data[0];
-  value[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = src_data[1];
+  // Copy value and index from in and in_index
+  int stride_size = blockDim.x;
+  value[threadIdx.x] = in[0];
+  value[threadIdx.x + stride_size] = in[1];
   // index
-  index[threadIdx.x] = src_index[0];
-  index[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = src_index[1];
+  index[threadIdx.x] = in_index[0];
+  index[threadIdx.x + stride_size] = in_index[1];
   // make bitonicSort
-  for (int size = 2; size < num; size <<= 1) {
+  for (int size = 2; size < upper_bound; size <<= 1) {
     int bitonic_type = (threadIdx.x & (size / 2)) != 0;
     for (int stride = size / 2; stride > 0; stride >>= 1) {
       __syncthreads();
       int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1));
-      details::ComparatorWithIndex<T, IndexType>(&value[pos],
-                                                 &value[pos + stride],
-                                                 &index[pos],
-                                                 &index[pos + stride],
-                                                 bitonic_type);
+      details::ComparatorWithIndex<InT, IndexType>(&value[pos],
+                                                   &value[pos + stride],
+                                                   &index[pos],
+                                                   &index[pos + stride],
+                                                   bitonic_type);
     }
   }
 
-  for (int stride = SHARED_SIZE_LIMIT / 2; stride > 0; stride >>= 1) {
+  for (int stride = stride_size; stride > 0; stride >>= 1) {
     __syncthreads();
     int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1));
     // last sort when monotonic_type = 1 then increase
-    details::ComparatorWithIndex<T, IndexType>(&value[pos],
-                                               &value[pos + stride],
-                                               &index[pos],
-                                               &index[pos + stride],
-                                               monotonic_type);
+    details::ComparatorWithIndex<InT, IndexType>(&value[pos],
+                                                 &value[pos + stride],
+                                                 &index[pos],
+                                                 &index[pos + stride],
+                                                 monotonic_type);
   }
 
   __syncthreads();
-  dst[0] = value[threadIdx.x];
-  dst[1] = value[threadIdx.x + (SHARED_SIZE_LIMIT / 2)];
-  dst_index[0] = index[threadIdx.x];
-  dst_index[1] = index[threadIdx.x + (SHARED_SIZE_LIMIT / 2)];
+  out[0] = static_cast<OutT>(value[threadIdx.x]);
+  out[1] = static_cast<OutT>(value[threadIdx.x + stride_size]);
+  out_index[0] = index[threadIdx.x];
+  out_index[1] = index[threadIdx.x + stride_size];
+}
+
+template <typename T1, typename T2, typename OutT, typename OpFunc>
+HOSTDEVICE __forceinline__ void OperatorTernary(
+    OutT* out, const T1* in1, const T2* in2, OpFunc func, int num) {
+  func(out, in1, in2, num);
+}
+
+template <typename InT, typename OutT, typename OpFunc>
+HOSTDEVICE __forceinline__ void OperatorBinary(OutT* out,
+                                               const InT* in,
+                                               OpFunc func,
+                                               int num) {
+  func(out, in, num);
 }
 
 }  // namespace kps

paddle/phi/kernels/primitive/compute_primitives_xpu2.h

@@ -348,6 +348,29 @@ __device__ __forceinline__ void Reduce(T* out,
   }
 }
 
+/*
+* @brief Fill register with a constant according to OpFunc
+*
+* @template paraments
+* InT: The data type of in1 and in2.
+* OutT: The data type of out.
+* NX: The number of data columns loaded by each thread.
+* NY: The number of data rows loaded by each thread.
+* BlockSize: Identifies the current device thread index method. For xpu,
+* core_id() is used as the index.
+* OpFunc: Compute functor which has an operator() as following
+*     template <typename InT>
+*     struct XxxFunctor {
+*       HOSTDEVICE InT operator()()
+* const {
+*         return a;
+*       }
+*     };
+*
+* @param
+* out: The register pointer of out, the size is NX * NY.
+* compute: Compute function which was declared like OpFunc<InT>().
+*/
 template <typename InT,
           typename OutT,
           int NX,

paddle/phi/kernels/primitive/datamover_primitives.h

@@ -297,6 +297,24 @@ __device__ __forceinline__ void ReadData(T* dst,
 /**
  * @brief Read 1D data from global memory to register. The difference
  * from the above function is that it supports different data types of inputs.
+ *
+ * @template paraments
+ * T: The type of data.
+ * NX: Each thread load NX data from global memory continuously.
+ * NY: Each thread need to load NY rows, only NY = 1 was supported.
+ * ArgsT: The Type if dst, ArgsT can be std::tuple<T> or std::tuple<Args>
+ * Index: The index of data stored in dst.
+ * BlockSize: Identifies the current device thread index method. For GPU,
+ * threadIdx.x is used as the thread index. Currently only GPU was supported.
+ * IsBoundary: Whether to make an out-of-bounds judgment on access to memory.
+ * When the number of data processed by this block is less than
+ * NX x NY x blockDim.x, boundary judgment is required to avoid memory access
+ * crossing the boundary.
+ *
+ * @param：
+ * dst: The register pointer of the thread, the size is NX * NY.
+ * src: The data pointer of the current block.
+ * size: The current block needs to load size data continuously.
  */
 template <typename T,
           int NX,
@@ -714,6 +732,20 @@ __device__ __forceinline__ void ReadDataBc(
   }
 }
 
+/**
+ * @brief Initialize register with data index.
+ *
+ * @template paraments
+ * T: Data type of register.
+ * NX: Number of data to initialize.
+ * NY: Number of data to initialize, NY only can be 1.
+ * BlockSize: Identifies the current device thread index method. For GPU,
+ * threadIdx.x is used as the thread index. Currently only GPU was supported.
+ *
+ * @param：
+ * dst: The register pointer of the thread, the size is NX.
+ * init_data: The register pointer of init data, the size is NX.
+ */
 template <typename T, int NX, int NY, int BlockSize>
 __device__ __forceinline__ void InitWithDataIndex(T* dst, int block_offset) {
   int thread_offset = block_offset + threadIdx.x * NX;

paddle/phi/kernels/primitive/datamover_primitives_xpu2.h

@@ -244,6 +244,24 @@ __device__ __inline__ void ReadData(T* dst,
 /**
  * @brief Read 1D data from global memory to register. The difference
  * from the above function is that it supports different data types of inputs.
+ *
+ * @template paraments
+ * T: The type of data.
+ * NX: Each thread load NX data from global memory continuously.
+ * NY: Each thread need to load NY rows, only NY = 1 was supported.
+ * ArgsT: The Type if dst, ArgsT can be std::tuple<T> or std::tuple<Args>
+ * Index: The index of data stored in dst.
+ * BlockSize: Identifies the current device thread index method. For xpu,
+ * core_id() is used as the index.
+ * IsBoundary: Whether to make an out-of-bounds judgment on access to memory.
+ * When the number of data processed by this block is less than
+ * NX x NY x blockDim.x, boundary judgment is required to avoid memory access
+ * crossing the boundary.
+ *
+ * @param：
+ * dst: The register pointer of the thread, the size is NX * NY.
+ * src: The data pointer of the current block.
+ * size: The current block needs to load size data continuously.
  */
 template <typename T,
           int NX,
@@ -646,5 +664,28 @@ __device__ __inline__ void ReadDataBc(
   }
 }
 
+/**
+ * @brief Initialize register with data index.
+ *
+ * @template paraments
+ * T: Data type of register.
+ * NX: Number of data to initialize.
+ * NY: Number of data to initialize, NY only can be 1.
+ * BlockSize: Identifies the current device thread index method. For xpu,
+ * core_id() is used as the index.
+ *
+ * @param：
+ * dst: The register pointer of the thread, the size is NX.
+ * init_data: The register pointer of init data, the size is NX.
+ */
+template <typename T, int NX, int NY, int BlockSize>
+__device__ __forceinline__ void InitWithDataIndex(T* dst, int block_offset) {
+  int thread_offset = block_offset + core_id() * NX;
+#pragma unroll
+  for (int nx = 0; nx < NX; ++nx) {
+    dst[nx] = static_cast<T>(thread_offset + nx);
+  }
+}
+
 }  // namespace kps
 }  // namespace phi

paddle/phi/kernels/selu_grad_kernel.h

+
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SeluGradKernel(const Context& dev_ctx,
+                    const DenseTensor& out,
+                    const DenseTensor& d_out,
+                    float scale,
+                    float alpha,
+                    DenseTensor* d_x);
+}  // namespace phi

paddle/phi/kernels/selu_kernel.h

+
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SeluKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                float scale,
+                float alpha,
+                DenseTensor* out);
+}  // phi

paddle/phi/ops/compat/reduce_sig.cc

@@ -17,28 +17,36 @@ limitations under the License. */
 namespace phi {
 
 KernelSignature ReduceSumOpArgumentMapping(const ArgumentMappingContext& ctx) {
-  bool reduce_all = paddle::any_cast<bool>(ctx.Attr("reduce_all"));
   if (ctx.IsDenseTensorInput("X")) {
-    if (!reduce_all) {
-      return KernelSignature(
-          "sum", {"X"}, {"dim", "out_dtype", "keep_dim"}, {"Out"});
+    bool reduce_all = paddle::any_cast<bool>(ctx.Attr("reduce_all"));
+    // When ctx is InferShapeArgumentMappingContext, the reduce_all is used in
+    // InferShape, so we must return the "sum_raw" KernelSignature.
+    // And the InferMeta function(i.e. ReduceInferMetaBase) is accordance with
+    // the "sum_raw" KernelSignature
+    if (ctx.IsForInferShape() || reduce_all) {
+      return KernelSignature("sum_raw",
+                             {"X"},
+                             {"dim", "keep_dim", "reduce_all", "out_dtype"},
+                             {"Out"});
     }
-    return KernelSignature("sum_raw",
-                           {"X"},
-                           {"dim", "keep_dim", "reduce_all", "out_dtype"},
-                           {"Out"});
+    return KernelSignature(
+        "sum", {"X"}, {"dim", "out_dtype", "keep_dim"}, {"Out"});
   }
   return KernelSignature("unregistered", {}, {}, {});
 }
 
 KernelSignature ReduceMeanOpArgumentMapping(const ArgumentMappingContext& ctx) {
-  bool reduce_all = paddle::any_cast<bool>(ctx.Attr("reduce_all"));
   if (ctx.IsDenseTensorInput("X")) {
-    if (!reduce_all) {
-      return KernelSignature("mean", {"X"}, {"dim", "keep_dim"}, {"Out"});
+    bool reduce_all = paddle::any_cast<bool>(ctx.Attr("reduce_all"));
+    // When ctx is InferShapeArgumentMappingContext, the reduce_all is used in
+    // InferShape, so we must return the "mean_raw" KernelSignature.
+    // And the InferMeta function(i.e. MeanRawInferMeta) is accordance with the
+    // "mean_raw" KernelSignature
+    if (ctx.IsForInferShape() || reduce_all) {
+      return KernelSignature(
+          "mean_raw", {"X"}, {"dim", "keep_dim", "reduce_all"}, {"Out"});
     }
-    return KernelSignature(
-        "mean_raw", {"X"}, {"dim", "keep_dim", "reduce_all"}, {"Out"});
+    return KernelSignature("mean", {"X"}, {"dim", "keep_dim"}, {"Out"});
   }
   return KernelSignature("unregistered", {}, {}, {});
 }

paddle/phi/ops/compat/selu_sig.cc

+
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature SeluGradGradOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature("selu_grad",
+                         {"Out", GradVarName("Out")},
+                         {"scale", "alpha"},
+                         {GradVarName("X")});
+}
+}  // namespace phi
+PD_REGISTER_ARG_MAPPING_FN(selu_grad, phi::SeluGradGradOpArgumentMapping);

paddle/phi/tests/ops/test_op_signature.h

@@ -80,6 +80,8 @@ class TestArgumentMappingContext : public phi::ArgumentMappingContext {
     return selected_rows_outputs.count(name) > 0;
   }
 
+  bool IsForInferShape() const override { return false; }
+
  private:
   const std::unordered_set<std::string> dense_tensor_inputs;
   const std::unordered_set<std::string> selected_rows_inputs;

paddle/scripts/paddle_build.sh

@@ -1266,7 +1266,7 @@ function card_test() {
     elif [ "${WITH_ASCEND_CL}" == "ON" ];then
         CUDA_DEVICE_COUNT=1
     elif [ "${WITH_ROCM}" == "ON" ];then
-        CUDA_DEVICE_COUNT=4
+        CUDA_DEVICE_COUNT=$(rocm-smi -i | grep GPU | wc -l)
     else
         CUDA_DEVICE_COUNT=$(nvidia-smi -L | wc -l)
     fi

python/paddle/fluid/contrib/slim/tests/CMakeLists.txt

@@ -25,6 +25,12 @@ function(inference_analysis_python_api_int8_test_mkldnn target model_dir data_pa
     _inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path} ${filename} True)
 endfunction()
 
+function(download_data install_dir url data_file check_sum)
+    if (NOT EXISTS ${install_dir}/${data_file})
+	    inference_download_and_uncompress(${install_dir} ${url} ${data_file} ${check_sum})
+    endif()
+endfunction()
+
 function(download_quant_data install_dir data_file check_sum)
     if (NOT EXISTS ${install_dir}/${data_file})
 	    inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file} ${check_sum})
@@ -290,8 +296,9 @@ if(LINUX AND WITH_MKLDNN)
 	### PTQ INT8
 
 	# PTQ int8 lstm model
-	set(LSTM_DATA_ARCHIVE "unittest_model_data/quant_lstm_input_data.tar.gz")
-	download_quant_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_DATA_ARCHIVE} add84c754e9b792fea1fbd728d134ab7)
+	set(LSTM_DATA_FILE "quant_lstm_input_data.tar.gz")
+	set(LSTM_URL "${INFERENCE_URL}/int8/unittest_model_data")
+	download_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_URL} ${LSTM_DATA_FILE} add84c754e9b792fea1fbd728d134ab7)
 	set(QUANT2_FP32_LSTM_MODEL_ARCHIVE "lstm_fp32_model.tar.gz")
 	download_lstm_model(${QUANT2_INT8_LSTM_SAVE_PATH} ${QUANT2_FP32_LSTM_MODEL_ARCHIVE} eecd9f44d69a84acc1cf2235c4b8b743)
 	inference_quant2_int8_lstm_model_test(test_quant2_int8_lstm_mkldnn ${QUANT2_INT8_LSTM_SAVE_PATH}/lstm_fp32_model ${QUANT2_LSTM_MODEL_DIR}/lstm_quant ${QUANT2_INT8_LSTM_SAVE_PATH}/quant_lstm_input_data)

python/paddle/fluid/tests/unittests/ipu/CMakeLists.txt

+if(WITH_IPU)
+    file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+    string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+    foreach(TEST_OP ${TEST_OPS})
+        py_test_modules(${TEST_OP} MODULES ${TEST_OP})
+    endforeach(TEST_OP)
+endif()

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

@@ -423,6 +423,14 @@ class TestMoveAxis(unittest.TestCase):
         self.assertEqual(np.array_equal(out.numpy(), expected), True)
         paddle.enable_static()
 
+    def test_moveaxis3(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(
+            [[1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j]])
+        out = x.moveaxis(0, 1)
+        self.assertEqual(out.shape, [2, 3])
+        paddle.enable_static()
+
     def test_error(self):
         x = paddle.randn([2, 3, 4, 5])
         # src must have the same number with dst

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

@@ -51,6 +51,10 @@ class TestVarBase(unittest.TestCase):
                     np.array_equal(x.numpy(), np.array([1.2], 'float16')))
                 self.assertEqual(x.dtype, core.VarDesc.VarType.FP16)
 
+                # set_default_dtype take effect on int
+                x = paddle.to_tensor(1, place=place)
+                self.assertTrue(x.dtype, core.VarDesc.VarType.INT64)
+
                 # set_default_dtype take effect on float
                 x = paddle.to_tensor(1.2, place=place, stop_gradient=False)
                 self.assertTrue(

python/paddle/tensor/creation.py

@@ -110,12 +110,6 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
             "'place' must be any of paddle.Place, paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace, paddle.NPUPlace, paddle.XPUPlace, paddle.CustomPlace"
         )
 
-    #Todo(zhouwei): Support allocate tensor on any other specified card
-    if isinstance(place, core.CUDAPlace) and isinstance(
-            _current_expected_place(), core.CUDAPlace) and place._get_device_id(
-            ) != _current_expected_place()._get_device_id():
-        place = _current_expected_place()
-
     if not isinstance(data, np.ndarray):
 
         def _handle_dtype(data, dtype):
@@ -139,7 +133,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
             data.stop_gradient = stop_gradient
             return data
         elif isinstance(data, (core.LoDTensor, core.Tensor)):
-            # Note(zhouwei25): should't expose it to users, just for internal use.
+            # should't expose it to users, just for internal use.
             # convert core.Tensor/core.LoDTensor to VarBase first
             # Currenly, there is no copy when places are same
             data = paddle.Tensor(data)
@@ -152,15 +146,20 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
             raise TypeError(
                 "Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor".
                 format(type(data)))
-        if not dtype and data.dtype in [
-                'float16', 'float32', 'float64', 'complex64', 'complex128'
-        ]:
-            default_type = paddle.get_default_dtype()
-            if np.iscomplexobj(data):
-                default_type = 'complex64' if default_type in [
-                    'float16', 'float32'
-                ] else 'complex128'
-            data = data.astype(default_type)
+        if not dtype:
+            if data.dtype in [
+                    'float16', 'float32', 'float64', 'complex64', 'complex128'
+            ]:
+                default_type = paddle.get_default_dtype()
+                if np.iscomplexobj(data):
+                    default_type = 'complex64' if default_type in [
+                        'float16', 'float32'
+                    ] else 'complex128'
+                data = data.astype(default_type)
+            # Windows default type is 'int32', while Linux/Mac is 'int64'. Unify they.
+            if data.dtype in ['int32']:
+                default_type = "int64"
+                data = data.astype(default_type)
 
     if dtype and convert_dtype(dtype) != data.dtype:
         data = data.astype(convert_dtype(dtype))

python/paddle/tensor/manipulation.py

@@ -2737,9 +2737,10 @@ def moveaxis(x, source, destination, name=None):
         out, _ = _C_ops.transpose2(x, 'axis', perm)
         return out
 
-    check_variable_and_dtype(
-        x, 'x', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
-        'moveaxis')
+    check_variable_and_dtype(x, 'x', [
+        'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64',
+        'complex128'
+    ], 'moveaxis')
 
     helper = LayerHelper('moveaxis', **locals())
     out = helper.create_variable_for_type_inference(x.dtype)

python/paddle/utils/code_gen/api.yaml

@@ -124,7 +124,7 @@
   args : (Tensor x, int64_t[] axis={}, bool keep_dim=false)
   output : Tensor
   infer_meta :
-    func : ReduceInferMeta
+    func : MeanInferMeta
   kernel :
     func : mean