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242 lines
8.8 KiB
242 lines
8.8 KiB
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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// Created by Jiabin on 2019-08-19.
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//
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#include <paddle/fluid/framework/op_registry.h>
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#include <memory>
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#include <string>
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#include <vector>
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/op_info.h"
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#include "paddle/fluid/imperative/prepared_operator.h"
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#include "paddle/fluid/imperative/type_defs.h"
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namespace imperative = paddle::imperative;
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namespace platform = paddle::platform;
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namespace framework = paddle::framework;
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namespace paddle {
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namespace imperative {
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static framework::RuntimeContext PrepareRuntimeContext(
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const NameVarBaseMap& ins, const NameVarBaseMap& outs) {
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framework::VariableValueMap inputs, outputs;
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for (auto& in_pair : ins) {
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auto& in_ctx = inputs[in_pair.first];
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in_ctx.reserve(in_pair.second.size());
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for (auto& in_var : in_pair.second) {
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in_ctx.emplace_back(in_var->MutableVar());
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}
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}
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for (auto& out_pair : outs) {
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auto& out_ctx = outputs[out_pair.first];
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out_ctx.reserve(out_pair.second.size());
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for (auto& out_var : out_pair.second) {
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out_ctx.emplace_back(out_var->MutableVar());
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}
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}
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return framework::RuntimeContext(std::move(inputs), std::move(outputs));
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}
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static framework::VariableNameMap CreateVarNameMap(
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const framework::OpInfo& op_info, const std::string& op_type,
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const NameVarBaseMap& varbase_map, bool is_input) {
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if (op_info.proto_ == nullptr) {
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return {};
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}
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framework::VariableNameMap result;
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for (auto& var :
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is_input ? op_info.Proto().inputs() : op_info.Proto().outputs()) {
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auto it = varbase_map.find(var.name());
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if (it == varbase_map.end()) {
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PADDLE_ENFORCE_EQ(
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var.dispensable(), true,
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platform::errors::NotFound("Variable %s is not dispensable and "
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"there are no such var in inputs",
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var.name()));
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result[var.name()] = {};
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} else {
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auto& var_vector = it->second;
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std::vector<std::string> args;
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args.reserve(var_vector.size());
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for (auto& var_base : var_vector) {
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args.emplace_back(var_base->Name());
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}
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result[var.name()] = std::move(args);
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}
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}
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return result;
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}
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using vb_vector = std::vector<std::shared_ptr<imperative::VarBase>>;
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using var_pair = std::pair<std::string, vb_vector>;
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TEST(test_prepare_op, test_prepare_op) {
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std::shared_ptr<imperative::VarBase> vin(
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new imperative::VarBase(false, "vin"));
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std::shared_ptr<imperative::VarBase> vout(
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new imperative::VarBase(false, "vout"));
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framework::OpDesc desc;
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platform::CPUPlace place;
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vin->MutableVar()->GetMutable<framework::LoDTensor>()->mutable_data<float>(
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place);
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var_pair x_pair = var_pair("X", vb_vector(1, vin));
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var_pair out_pair = var_pair("Out", vb_vector(1, vout));
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imperative::NameVarBaseMap ins = {x_pair};
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imperative::NameVarBaseMap outs = {out_pair};
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framework::AttributeMap split_attr_map;
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const auto& info = framework::OpInfoMap::Instance().Get("split");
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if (info.Checker()) info.Checker()->Check(&split_attr_map);
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framework::VariableNameMap var_in_map =
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CreateVarNameMap(info, "split", ins, true);
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framework::VariableNameMap var_out_map =
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CreateVarNameMap(info, "split", outs, false);
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auto op = framework::OpRegistry::CreateOp("split", var_in_map, var_out_map,
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split_attr_map);
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framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);
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ASSERT_NO_FATAL_FAILURE(PreparedOp preparedOp = PreparedOp::Prepare(
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ins, outs,
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dynamic_cast<framework::OperatorWithKernel&>(*op),
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place, split_attr_map));
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}
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const framework::Tensor* GetTensorFromVar(const framework::Variable& var);
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TEST(test_prepare_op, test_get_tensor_from_var) {
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std::shared_ptr<imperative::VarBase> vout_error(
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new imperative::VarBase(false, "vout_error"));
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vout_error->MutableVar()->GetMutable<framework::SelectedRows>();
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auto* ts = GetTensorFromVar(*vout_error->MutableVar());
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ASSERT_TRUE(ts != nullptr);
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}
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#if defined(PADDLE_WITH_CUDA)
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TEST(test_prepare_op, test_prepare_data) {
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std::shared_ptr<imperative::VarBase> vin(
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new imperative::VarBase(false, "vin"));
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std::shared_ptr<imperative::VarBase> vout(
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new imperative::VarBase(false, "vout"));
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framework::OpDesc desc;
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platform::CPUPlace cpu_place;
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platform::CUDAPlace gpu_place(0);
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std::vector<float> src_data(10, 2.0);
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std::vector<int64_t> dims = {2, 5};
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// prepare an cpu only input
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auto* vin_tensor = vin->MutableVar()->GetMutable<framework::LoDTensor>();
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vin_tensor->Resize(framework::make_ddim(dims));
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auto* vin_mutable_tensor = vin_tensor->mutable_data<float>(cpu_place);
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paddle::memory::Copy(cpu_place, vin_mutable_tensor, cpu_place,
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src_data.data(), sizeof(float) * src_data.size());
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var_pair x_pair = var_pair("X", vb_vector(1, vin));
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var_pair out_pair = var_pair("Out", vb_vector(1, vout));
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imperative::NameVarBaseMap ins = {x_pair};
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imperative::NameVarBaseMap outs = {out_pair};
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const std::string op_type = "relu";
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framework::AttributeMap attr_map;
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const auto& info = framework::OpInfoMap::Instance().Get(op_type);
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if (info.Checker()) info.Checker()->Check(&attr_map);
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framework::VariableNameMap var_in_map =
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CreateVarNameMap(info, op_type, ins, true);
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framework::VariableNameMap var_out_map =
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CreateVarNameMap(info, op_type, outs, false);
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auto op = framework::OpRegistry::CreateOp(op_type, var_in_map, var_out_map,
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attr_map);
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framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);
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// test if it can be transformed to GPU place
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PreparedOp prepared_op = PreparedOp::Prepare(
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ins, outs, dynamic_cast<framework::OperatorWithKernel&>(*op), gpu_place,
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attr_map);
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for (const auto& name_pair : ins) {
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for (const auto& vb : name_pair.second) {
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ASSERT_TRUE(platform::is_same_place(
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vb->Var().Get<framework::LoDTensor>().place(), gpu_place));
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}
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}
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}
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#endif
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void TestPrepareDataSamePlace(framework::AttributeMap attr_map) {
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std::shared_ptr<imperative::VarBase> vin(
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new imperative::VarBase(false, "vin"));
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std::shared_ptr<imperative::VarBase> vout(
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new imperative::VarBase(false, "vout"));
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framework::OpDesc desc;
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platform::CPUPlace cpu_place;
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std::vector<float> src_data(10, 2.0);
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std::vector<int64_t> dims = {2, 5};
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// prepare an cpu only input
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auto* vin_tensor = vin->MutableVar()->GetMutable<framework::LoDTensor>();
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vin_tensor->Resize(framework::make_ddim(dims));
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auto* vin_mutable_tensor = vin_tensor->mutable_data<float>(cpu_place);
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paddle::memory::Copy(cpu_place, vin_mutable_tensor, cpu_place,
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src_data.data(), sizeof(float) * src_data.size());
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var_pair x_pair = var_pair("X", vb_vector(1, vin));
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var_pair out_pair = var_pair("Out", vb_vector(1, vout));
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imperative::NameVarBaseMap ins = {x_pair};
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imperative::NameVarBaseMap outs = {out_pair};
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const std::string op_type = "relu";
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const auto& info = framework::OpInfoMap::Instance().Get(op_type);
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if (info.Checker()) info.Checker()->Check(&attr_map);
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framework::VariableNameMap var_in_map =
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CreateVarNameMap(info, op_type, ins, true);
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framework::VariableNameMap var_out_map =
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CreateVarNameMap(info, op_type, outs, false);
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auto op = framework::OpRegistry::CreateOp(op_type, var_in_map, var_out_map,
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attr_map);
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framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);
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// test if it never transferred on GPU place
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PreparedOp prepared_op = PreparedOp::Prepare(
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ins, outs, dynamic_cast<framework::OperatorWithKernel&>(*op), cpu_place,
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attr_map);
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for (const auto& name_pair : ins) {
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for (const auto& vb : name_pair.second) {
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ASSERT_TRUE(platform::is_same_place(
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vb->Var().Get<framework::LoDTensor>().place(), cpu_place));
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}
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}
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}
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TEST(test_prepare_op, test_prepare_data_same_place) {
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TestPrepareDataSamePlace({});
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}
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#ifdef PADDLE_WITH_MKLDNN
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TEST(test_prepare_op, test_prepare_data_cpu_mkldnn) {
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TestPrepareDataSamePlace({{"use_mkldnn", true}});
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}
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#endif
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} // namespace imperative
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} // namespace paddle
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USE_OP(split);
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USE_OP(relu);
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#ifdef PADDLE_WITH_MKLDNN
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USE_OP_DEVICE_KERNEL(relu, MKLDNN);
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#endif
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