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401 lines
14 KiB
401 lines
14 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "Function.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/math/SparseMatrix.h"
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#include "paddle/math/tests/TensorCheck.h"
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#include "paddle/testing/TestUtil.h"
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namespace paddle {
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typedef std::shared_ptr<BufferArg> BufferArgPtr;
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namespace test {
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template <DeviceType DType>
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struct Allocator;
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template <>
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struct Allocator<DEVICE_TYPE_CPU> {
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using type = CpuMemoryHandle;
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};
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template <>
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struct Allocator<DEVICE_TYPE_GPU> {
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using type = GpuMemoryHandle;
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};
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// Copy argument1 to argument2
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template <DeviceType DType1, DeviceType DType2>
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class CopyArgument {
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public:
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void operator()(const BufferArg& arg1, BufferArg& arg2) {
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CHECK_EQ(arg1.valueType(), arg2.valueType());
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CHECK_LE(arg1.shape().getElements(), arg2.shape().getElements());
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if (arg1.valueType() == VALUE_TYPE_INT32) {
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IVectorPtr vector1 =
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IVector::create((int*)arg1.data(),
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arg1.shape().getElements(),
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DType1 == DEVICE_TYPE_CPU ? false : true);
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IVectorPtr vector2 =
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IVector::create((int*)arg2.data(),
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arg2.shape().getElements(),
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DType2 == DEVICE_TYPE_CPU ? false : true);
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vector2->copyFrom(*vector1);
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} else {
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VectorPtr vector1 =
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Vector::create((real*)arg1.data(),
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arg1.shape().getElements(),
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DType1 == DEVICE_TYPE_CPU ? false : true);
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VectorPtr vector2 =
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Vector::create((real*)arg2.data(),
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arg2.shape().getElements(),
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DType2 == DEVICE_TYPE_CPU ? false : true);
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vector2->copyFrom(*vector1);
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}
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}
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};
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} // namespace test
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/**
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* \brief A class for comparing two Functions of different implementations.
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* For example, can be used to compare the CPU and GPU implementation
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* of the function is consistent.
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*
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* Use case:
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* // Initializes a test object, the corresponding cpu and gpu Function
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* // are constructed according to FunctionName and FuncConfig.
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* CpuGpuFuncCompare test(FunctionName, FuncConfig);
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* // Prepare inputs and outputs arguments.
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* // Here the input and output can not contain real data,
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* // only contains the argument type and shape.
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* test.addInputs(input1);
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* test.addInputs(input2);
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* test.addOutputs(output1);
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* test.addOutputs(output2);
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* // Run.
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* // Will according to the type and shape of arguments(inputs_/outputs_),
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* // automatic initialization cpu and gpu function required arguments
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* // (cpuInputs_/cpuOutputs_/gpuInputs_/gpuOutputs_).
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* // Call the CPU and GPU Function calculation results.
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* // Compares CPU and GPU calculation results for consistency.
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* test.run();
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*/
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template <DeviceType DType1, DeviceType DType2>
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class Compare2Function {
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public:
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typedef typename test::Allocator<DType1>::type Allocator1;
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typedef typename test::Allocator<DType2>::type Allocator2;
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typedef typename Tensor<real, DType1>::Vector Vector1;
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typedef typename Tensor<real, DType2>::Vector Vector2;
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typedef typename Tensor<real, DType1>::SparseMatrix SparseMatrix1;
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typedef typename Tensor<real, DType2>::SparseMatrix SparseMatrix2;
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Compare2Function(const std::string& name1,
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const std::string& name2,
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const FuncConfig& config)
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: function1_(FunctionBase::funcRegistrar_.createByType(name1)),
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function2_(FunctionBase::funcRegistrar_.createByType(name2)) {
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function1_->init(config);
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function2_->init(config);
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}
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~Compare2Function() {}
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// input need only contains shape, do not contains data.
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void addInputs(const BufferArg& input) {
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size_t size =
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input.shape().getElements() * sizeOfValuType(input.valueType());
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func1Memory_.emplace_back(std::make_shared<Allocator1>(size));
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func2Memory_.emplace_back(std::make_shared<Allocator2>(size));
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func1Inputs_.emplace_back(std::make_shared<BufferArg>(
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func1Memory_.back()->getBuf(), input.valueType(), input.shape()));
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func2Inputs_.emplace_back(std::make_shared<BufferArg>(
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func2Memory_.back()->getBuf(), input.valueType(), input.shape()));
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}
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// assume one copy of sequence is shared by different SequenceArgs
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void addSequence(const SequenceIdArg& input) {
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CHECK_EQ(input.shape().ndims(), 1UL);
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size_t batchSize = input.shape()[0];
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size_t numSeqs = batchSize / 10 + 1;
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size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32);
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func1Memory_.emplace_back(std::make_shared<Allocator1>(sizeId));
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func2Memory_.emplace_back(std::make_shared<Allocator2>(sizeId));
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seq1_ = std::make_shared<SequenceIdArg>(func1Memory_.back()->getBuf(),
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TensorShape{numSeqs + 1});
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seq2_ = std::make_shared<SequenceIdArg>(func2Memory_.back()->getBuf(),
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TensorShape{numSeqs + 1});
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/// init sequence Id
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initArg(*seq1_, batchSize);
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copyArg_(*seq1_, *seq2_);
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}
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void addInputs(const SequenceArg& input) {
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CHECK_EQ(input.shape().ndims(), 2UL);
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size_t batchSize = input.shape()[0];
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if (!seq1_ || !seq2_) { // sequence not exist
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addSequence(SequenceIdArg(TensorShape{batchSize}));
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}
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size_t size =
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input.shape().getElements() * sizeOfValuType(input.valueType());
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func1Memory_.emplace_back(std::make_shared<Allocator1>(size));
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func2Memory_.emplace_back(std::make_shared<Allocator2>(size));
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/// SequenceArg
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func1Inputs_.emplace_back(
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std::make_shared<SequenceArg>(func1Memory_.back()->getBuf(),
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input.valueType(),
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input.shape(),
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*seq1_));
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func2Inputs_.emplace_back(
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std::make_shared<SequenceArg>(func2Memory_.back()->getBuf(),
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input.valueType(),
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input.shape(),
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*seq2_));
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}
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// output need only contains shape, do not contains data.
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void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) {
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size_t size =
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output.shape().getElements() * sizeOfValuType(output.valueType());
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func1Memory_.emplace_back(std::make_shared<Allocator1>(size));
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func2Memory_.emplace_back(std::make_shared<Allocator2>(size));
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func1Outputs_.emplace_back(
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std::make_shared<BufferArg>(func1Memory_.back()->getBuf(),
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output.valueType(),
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output.shape(),
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argType));
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func2Outputs_.emplace_back(
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std::make_shared<BufferArg>(func2Memory_.back()->getBuf(),
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output.valueType(),
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output.shape(),
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argType));
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}
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/// add and init output sparse matrix
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void addOutputs(const SparseMatrixArg& output, ArgType argType = ASSIGN_TO) {
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sparse1_ = std::make_shared<SparseMatrix1>(
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output.shape()[0],
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output.shape()[1],
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output.nnz(),
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static_cast<SparseValueType>(output.dataType()),
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static_cast<SparseFormat>(output.dataFormat()));
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sparse2_ = std::make_shared<SparseMatrix2>(
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output.shape()[0],
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output.shape()[1],
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output.nnz(),
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static_cast<SparseValueType>(output.dataType()),
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static_cast<SparseFormat>(output.dataFormat()));
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/// init sparse matrix
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hl_stream_t stream(HPPL_STREAM_1);
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sparse1_->randomizeUniform();
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sparse2_->copyFrom(*sparse1_, stream);
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hl_stream_synchronize(stream);
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func1Outputs_.emplace_back(
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std::make_shared<SparseMatrixArg>(*sparse1_, argType));
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func2Outputs_.emplace_back(
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std::make_shared<SparseMatrixArg>(*sparse2_, argType));
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}
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void addOutputs(const SequenceArg& output, ArgType argType = ASSIGN_TO) {
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CHECK_EQ(output.shape().ndims(), 2UL);
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size_t batchSize = output.shape()[0];
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if (!seq1_ || !seq2_) { // sequence not exist
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addSequence(SequenceIdArg(TensorShape{batchSize}));
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}
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size_t size =
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output.shape().getElements() * sizeOfValuType(output.valueType());
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func1Memory_.emplace_back(std::make_shared<Allocator1>(size));
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func2Memory_.emplace_back(std::make_shared<Allocator2>(size));
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/// SequenceArg
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func1Outputs_.emplace_back(
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std::make_shared<SequenceArg>(func1Memory_.back()->getBuf(),
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output.valueType(),
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output.shape(),
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*seq1_,
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argType));
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func2Outputs_.emplace_back(
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std::make_shared<SequenceArg>(func2Memory_.back()->getBuf(),
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output.valueType(),
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output.shape(),
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*seq2_,
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argType));
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}
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void addInputs(const SparseMatrixArg& input) {
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sparse1_ = std::make_shared<SparseMatrix1>(
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input.shape()[0],
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input.shape()[1],
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input.nnz(),
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static_cast<SparseValueType>(input.dataType()),
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static_cast<SparseFormat>(input.dataFormat()));
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sparse2_ = std::make_shared<SparseMatrix2>(
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input.shape()[0],
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input.shape()[1],
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input.nnz(),
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static_cast<SparseValueType>(input.dataType()),
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static_cast<SparseFormat>(input.dataFormat()));
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/// init sparse matrix
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hl_stream_t stream(HPPL_STREAM_1);
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sparse1_->randomizeUniform();
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sparse2_->copyFrom(*sparse1_, stream);
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hl_stream_synchronize(stream);
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func1Inputs_.emplace_back(std::make_shared<SparseMatrixArg>(*sparse1_));
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func2Inputs_.emplace_back(std::make_shared<SparseMatrixArg>(*sparse2_));
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}
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void run() {
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// prepare cpu/gpu arguments
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initInputs();
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initOutputs();
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// function calculate
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auto callFunction = [](FunctionBase* function,
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std::vector<BufferArgPtr>& inputs,
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std::vector<BufferArgPtr>& outputs) {
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BufferArgs inArgs;
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BufferArgs outArgs;
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for (auto arg : inputs) {
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inArgs.addArg(*arg);
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}
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for (auto arg : outputs) {
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outArgs.addArg(*arg);
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}
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function->calc(inArgs, outArgs);
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};
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callFunction(function1_.get(), func1Inputs_, func1Outputs_);
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callFunction(function2_.get(), func2Inputs_, func2Outputs_);
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// check outputs
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compareOutputs();
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}
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std::shared_ptr<FunctionBase> getFunction1() const { return function1_; }
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std::shared_ptr<FunctionBase> getFunction2() const { return function2_; }
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protected:
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// only init cpu argument, gpu argument copy from cpu argument.
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void initArg(BufferArg& arg) {
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Vector1 vector(arg.shape().getElements(), (real*)arg.data());
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vector.uniform(0.001, 1);
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}
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void initArg(SequenceArg& arg) {
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/// init only matrix
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Vector1 vector(arg.shape().getElements(), (real*)arg.data());
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vector.uniform(0.001, 1);
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}
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void initArg(SequenceIdArg& arg, size_t batchSize) {
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size_t numSeqs = arg.numSeqs();
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int* buf = reinterpret_cast<int*>(arg.data());
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int pos = 0;
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size_t maxLen = 2 * batchSize / numSeqs;
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for (int i = 0; i < (int)numSeqs; ++i) {
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int len = 1 + uniformRandom(std::min<int64_t>(
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maxLen, batchSize - pos - numSeqs + i));
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buf[i] = pos;
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pos += len;
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VLOG(1) << " len=" << len;
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}
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buf[numSeqs] = batchSize;
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}
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void initInputs() {
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for (size_t i = 0; i < func1Inputs_.size(); i++) {
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if (func1Inputs_[i]->isSparseArg()) {
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continue; /// sparse matrix already init
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}
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if (func1Inputs_[i]->isSequenceArg()) {
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initArg(dynamic_cast<SequenceArg&>(*func1Inputs_[i]));
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} else {
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initArg(*func1Inputs_[i]);
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}
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copyArg_(*func1Inputs_[i], *func2Inputs_[i]);
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}
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}
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void initOutputs() {
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for (size_t i = 0; i < func1Outputs_.size(); i++) {
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if (func1Outputs_[i]->isSparseArg()) {
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continue; /// sparse matrix already init
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}
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if (func1Outputs_[i]->isSequenceArg()) {
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initArg(dynamic_cast<SequenceArg&>(*func1Outputs_[i]));
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} else {
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initArg(*func1Outputs_[i]);
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}
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copyArg_(*func1Outputs_[i], *func2Outputs_[i]);
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}
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}
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void compareOutputs() {
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for (size_t i = 0; i < func1Outputs_.size(); i++) {
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// TODO, Need a BufferCheck used to compare the two buffers.
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const auto cpu = func1Outputs_[i];
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const auto gpu = func2Outputs_[i];
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CHECK_EQ(cpu->numElements(), gpu->numElements());
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Vector1 cpuVector(cpu->numElements(), (real*)cpu->data());
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Vector2 gpuVector(gpu->numElements(), (real*)gpu->data());
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autotest::TensorCheckErr(cpuVector, gpuVector);
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}
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}
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protected:
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std::shared_ptr<FunctionBase> function1_;
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std::shared_ptr<FunctionBase> function2_;
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std::vector<std::shared_ptr<Allocator1>> func1Memory_;
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std::vector<std::shared_ptr<Allocator2>> func2Memory_;
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std::vector<BufferArgPtr> func1Inputs_;
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std::vector<BufferArgPtr> func1Outputs_;
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std::vector<BufferArgPtr> func2Inputs_;
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std::vector<BufferArgPtr> func2Outputs_;
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std::shared_ptr<SparseMatrix1> sparse1_;
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std::shared_ptr<SparseMatrix2> sparse2_;
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std::shared_ptr<SequenceIdArg> seq1_;
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std::shared_ptr<SequenceIdArg> seq2_;
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test::CopyArgument<DType1, DType2> copyArg_;
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};
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class CpuGpuFuncCompare
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: public Compare2Function<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> {
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public:
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CpuGpuFuncCompare(const std::string& name, const FuncConfig& config)
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: Compare2Function(name + "-CPU", name + "-GPU", config) {}
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~CpuGpuFuncCompare() {}
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};
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} // namespace paddle
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