chore: import upstream snapshot with attribution

test/cpp/imperative/test_prepare_op.cc

@@ -0,0 +1,260 @@
+// Copyright (c) 2019 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.
+
+//
+// Created by Jiabin on 2019-08-19.
+//
+
+#include <paddle/fluid/framework/op_registry.h>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/op_info.h"
+#include "paddle/fluid/imperative/prepared_operator.h"
+#include "paddle/fluid/imperative/type_defs.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_DECLARE_KERNEL(split, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(relu, CPU, ALL_LAYOUT);
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_DECLARE_KERNEL(relu, GPU, ALL_LAYOUT);
+#endif
+
+namespace paddle {
+namespace imperative {
+
+extern void TestHandleComplexGradToRealGradEager(
+    const NameVarMap<egr::EagerVariable>& outs);
+
+static framework::VariableNameMap CreateVarNameMap(
+    const framework::OpInfo& op_info,
+    const std::string& op_type,
+    const NameVarBaseMap& varbase_map,
+    bool is_input) {
+  if (op_info.proto_ == nullptr) {
+    return {};
+  }
+
+  framework::VariableNameMap result;
+
+  for (auto& var :
+       is_input ? op_info.Proto().inputs() : op_info.Proto().outputs()) {
+    auto it = varbase_map.find(var.name());
+    if (it == varbase_map.end()) {
+      PADDLE_ENFORCE_EQ(
+          var.dispensable(),
+          true,
+          common::errors::NotFound("Variable %s is not dispensable and "
+                                   "there are no such var in inputs",
+                                   var.name()));
+      result[var.name()] = {};
+    } else {
+      auto& var_vector = it->second;
+      std::vector<std::string> args;
+      args.reserve(var_vector.size());
+      for (auto& var_base : var_vector) {
+        args.emplace_back(var_base->Name());
+      }
+      result[var.name()] = std::move(args);
+    }
+  }
+  return result;
+}
+
+using vb_vector = std::vector<std::shared_ptr<imperative::VarBase>>;
+
+using var_pair = std::pair<std::string, vb_vector>;
+
+TEST(test_prepare_op, test_prepare_op) {
+  std::shared_ptr<imperative::VarBase> vin(
+      new imperative::VarBase(false, "vin"));
+  std::shared_ptr<imperative::VarBase> vout(
+      new imperative::VarBase(false, "vout"));
+  framework::OpDesc desc;
+  phi::CPUPlace place;
+  vin->MutableVar()->GetMutable<phi::DenseTensor>()->mutable_data<float>(place);
+  var_pair x_pair = var_pair("X", vb_vector(1, vin));
+  var_pair out_pair = var_pair("Out", vb_vector(1, vout));
+  imperative::NameVarBaseMap ins = {x_pair};
+  imperative::NameVarBaseMap outs = {out_pair};
+  framework::AttributeMap split_attr_map;
+  const auto& info = framework::OpInfoMap::Instance().Get("split");
+  if (info.Checker()) info.Checker()->Check(&split_attr_map);
+  framework::VariableNameMap var_in_map =
+      CreateVarNameMap(info, "split", ins, true);
+  framework::VariableNameMap var_out_map =
+      CreateVarNameMap(info, "split", outs, false);
+  auto op = framework::OpRegistry::CreateOp(
+      "split", var_in_map, var_out_map, split_attr_map);
+  ASSERT_NO_FATAL_FAILURE(PreparedOp preparedOp = PreparedOp::Prepare(
+                              ins,
+                              outs,
+                              dynamic_cast<framework::OperatorWithKernel&>(*op),
+                              place,
+                              split_attr_map,
+                              {}));
+}
+
+const phi::DenseTensor* GetTensorFromVar(const framework::Variable& var);
+
+TEST(test_prepare_op, test_get_tensor_from_var) {
+  std::shared_ptr<imperative::VarBase> vout_error(
+      new imperative::VarBase(false, "vout_error"));
+  vout_error->MutableVar()->GetMutable<phi::SelectedRows>();
+  auto* ts = GetTensorFromVar(*vout_error->MutableVar());
+  ASSERT_TRUE(ts != nullptr);
+}
+
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+TEST(test_prepare_op, test_prepare_data) {
+  std::shared_ptr<imperative::VarBase> vin(
+      new imperative::VarBase(false, "vin"));
+  std::shared_ptr<imperative::VarBase> vout(
+      new imperative::VarBase(false, "vout"));
+
+  framework::OpDesc desc;
+  phi::CPUPlace cpu_place;
+  phi::GPUPlace gpu_place(0);
+  std::vector<float> src_data(10, 2.0);
+  std::vector<int64_t> dims = {2, 5};
+
+  // prepare an cpu only input
+  auto* vin_tensor = vin->MutableVar()->GetMutable<phi::DenseTensor>();
+  vin_tensor->Resize(common::make_ddim(dims));
+  auto* vin_mutable_tensor = vin_tensor->mutable_data<float>(cpu_place);
+  paddle::memory::Copy(cpu_place,
+                       vin_mutable_tensor,
+                       cpu_place,
+                       src_data.data(),
+                       sizeof(float) * src_data.size());
+
+  var_pair x_pair = var_pair("X", vb_vector(1, vin));
+  var_pair out_pair = var_pair("Out", vb_vector(1, vout));
+  imperative::NameVarBaseMap ins = {x_pair};
+  imperative::NameVarBaseMap outs = {out_pair};
+  const std::string op_type = "relu";
+  framework::AttributeMap attr_map;
+  const auto& info = framework::OpInfoMap::Instance().Get(op_type);
+  if (info.Checker()) info.Checker()->Check(&attr_map);
+  framework::VariableNameMap var_in_map =
+      CreateVarNameMap(info, op_type, ins, true);
+  framework::VariableNameMap var_out_map =
+      CreateVarNameMap(info, op_type, outs, false);
+  auto op = framework::OpRegistry::CreateOp(
+      op_type, var_in_map, var_out_map, attr_map);
+
+  // test if it can be transformed to GPU place
+  auto prepared_op =
+      PreparedOp::Prepare(ins,
+                          outs,
+                          dynamic_cast<framework::OperatorWithKernel&>(*op),
+                          gpu_place,
+                          attr_map,
+                          {});
+  PrepareData<imperative::VarBase>(
+      dynamic_cast<framework::OperatorWithKernel&>(*op),
+      ins,
+      prepared_op.kernel_key(),
+      gpu_place);
+  for (const auto& name_pair : ins) {
+    for (const auto& vb : name_pair.second) {
+      ASSERT_TRUE(phi::is_same_place(vb->Var().Get<phi::DenseTensor>().place(),
+                                     gpu_place));
+    }
+  }
+}
+#endif
+
+void TestPrepareDataSamePlace(framework::AttributeMap attr_map) {
+  std::shared_ptr<imperative::VarBase> vin(
+      new imperative::VarBase(false, "vin"));
+  std::shared_ptr<imperative::VarBase> vout(
+      new imperative::VarBase(false, "vout"));
+
+  framework::OpDesc desc;
+  phi::CPUPlace cpu_place;
+  std::vector<float> src_data(10, 2.0);
+  std::vector<int64_t> dims = {2, 5};
+
+  // prepare an cpu only input
+  auto* vin_tensor = vin->MutableVar()->GetMutable<phi::DenseTensor>();
+  vin_tensor->Resize(common::make_ddim(dims));
+  auto* vin_mutable_tensor = vin_tensor->mutable_data<float>(cpu_place);
+  paddle::memory::Copy(cpu_place,
+                       vin_mutable_tensor,
+                       cpu_place,
+                       src_data.data(),
+                       sizeof(float) * src_data.size());
+
+  var_pair x_pair = var_pair("X", vb_vector(1, vin));
+  var_pair out_pair = var_pair("Out", vb_vector(1, vout));
+  imperative::NameVarBaseMap ins = {x_pair};
+  imperative::NameVarBaseMap outs = {out_pair};
+  const std::string op_type = "relu";
+  const auto& info = framework::OpInfoMap::Instance().Get(op_type);
+  if (info.Checker()) info.Checker()->Check(&attr_map);
+  framework::VariableNameMap var_in_map =
+      CreateVarNameMap(info, op_type, ins, true);
+  framework::VariableNameMap var_out_map =
+      CreateVarNameMap(info, op_type, outs, false);
+
+  auto op = framework::OpRegistry::CreateOp(
+      op_type, var_in_map, var_out_map, attr_map);
+
+  // test if it never transferred on GPU place
+  auto prepared_op =
+      PreparedOp::Prepare(ins,
+                          outs,
+                          dynamic_cast<framework::OperatorWithKernel&>(*op),
+                          cpu_place,
+                          attr_map,
+                          {});
+  PrepareData<imperative::VarBase>(
+      dynamic_cast<framework::OperatorWithKernel&>(*op),
+      ins,
+      prepared_op.kernel_key(),
+      cpu_place);
+  for (const auto& name_pair : ins) {
+    for (const auto& vb : name_pair.second) {
+      ASSERT_TRUE(phi::is_same_place(vb->Var().Get<phi::DenseTensor>().place(),
+                                     cpu_place));
+    }
+  }
+}
+
+TEST(test_prepare_op, test_prepare_data_same_place) {
+  TestPrepareDataSamePlace({});
+}
+
+TEST(test_prepare_op, test_complex_eager) {
+  NameVarMap<egr::EagerVariable> outs = {};
+  TestHandleComplexGradToRealGradEager(outs);
+}
+
+#ifdef PADDLE_WITH_DNNL
+TEST(test_prepare_op, test_prepare_data_cpu_onednn) {
+  TestPrepareDataSamePlace({{"use_onednn", true}});
+}
+#endif
+}  // namespace imperative
+}  // namespace paddle
+
+USE_OP_ITSELF(split);
+USE_OP_ITSELF(relu);
+#ifdef PADDLE_WITH_DNNL
+PD_DECLARE_KERNEL(relu, OneDNN, ONEDNN);
+#endif