From c5c8051657611025eeaf8bc095da09a81fb76a1d Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 4 Jan 2017 21:17:56 +0800 Subject: [PATCH] add BufferArg --- paddle/function/BufferArg.cpp | 43 +++++ paddle/function/BufferArg.h | 260 ++++++++++++++++++++++++++++++ paddle/function/BufferArgTest.cpp | 128 +++++++++++++++ paddle/function/TensorType.h | 5 + 4 files changed, 436 insertions(+) create mode 100644 paddle/function/BufferArg.cpp create mode 100644 paddle/function/BufferArg.h create mode 100644 paddle/function/BufferArgTest.cpp diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp new file mode 100644 index 00000000000..08031917b21 --- /dev/null +++ b/paddle/function/BufferArg.cpp @@ -0,0 +1,43 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +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 + +#include "BufferArg.h" + +namespace paddle { + +const SequenceArg& BufferArg::sequence() const { + // CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA); + return dynamic_cast(*this); +} + +const SparseMatrixArg& BufferArg::sparse() const { + // CHECK_EQ(bufferType_, TENSOR_SPARSE); + return dynamic_cast(*this); +} + +void BufferArgs::addArg(const Matrix& arg, const TensorShape& shape) { + args_.push_back(std::make_shared(arg, shape)); +} + +void BufferArgs::addArg(const CpuSparseMatrix& arg) { + args_.push_back(std::make_shared(arg)); +} + +void BufferArgs::addArg(const GpuSparseMatrix& arg) { + args_.push_back(std::make_shared(arg)); +} + +} // namespace paddle diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h new file mode 100644 index 00000000000..9fcda7a878a --- /dev/null +++ b/paddle/function/BufferArg.h @@ -0,0 +1,260 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +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 + +#include "TensorShape.h" +#include "TensorType.h" +#include "paddle/math/CpuSparseMatrix.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/SparseMatrix.h" + +namespace paddle { + +enum BufferType { + TENSOR_NORMAL = 0, + TENSOR_SEQUENCE_ID = 1, + TENSOR_SEQUENCE_DATA = 2, + TENSOR_SPARSE = 3 +}; + +enum SparseDataType { + SPARSE_NO_VALUE = 0, // do not need value pointer, all values are 1 + SPARSE_FLOAT_VALUE = 1 +}; + +enum SparseDataFormat { SPARSE_CSR_FORMAT = 0, SPARSE_CSC_FORMAT = 1 }; + +/** + * BufferArg used as the argument type for Function. + */ +class BufferArg; +class SequenceArg; +class SparseMatrixArg; +typedef std::shared_ptr BufferArgPtr; + +class BufferArgs { +public: + BufferArgs() {} + size_t size() const { return args_.size(); } + + // add argument into BufferArgss + template + void addArg(const Tensor& arg) { + args_.push_back(std::make_shared(arg)); + } + + void addArg(const Matrix& arg, const TensorShape& shape); + + void addArg(const CpuSparseMatrix& arg); + void addArg(const GpuSparseMatrix& arg); + + // get argument + const BufferArg& operator[](size_t num) const { + CHECK_LT(num, args_.size()); + return *args_[num]; + } + +private: + std::vector args_; +}; + +// an array of arbitrary dimensions +class BufferArg { +public: + BufferArg(void* buf, ValueType valueType, const TensorShape& shape) + : buf_(buf), valueType_(valueType), shape_(shape) {} + + BufferArg(void* buf, ValueType valueType) + : buf_(buf), valueType_(valueType) {} + + BufferArg(const Matrix& matrix) + : buf_((void*)matrix.getData()), + valueType_(DataType::value), + shape_(2) { + shape_.setDim(0, matrix.getHeight()); + shape_.setDim(1, matrix.getWidth()); + } + + BufferArg(const Matrix& matrix, const TensorShape& shape) + : buf_((void*)matrix.getData()), + valueType_(DataType::value), + shape_(shape) { + CHECK_EQ(matrix.getElementCnt(), shape.getElements()); + } + + BufferArg(const Vector& vector) + : buf_((void*)vector.getData()), + valueType_(DataType::value), + shape_(1) { + shape_.setDim(0, vector.getSize()); + } + + BufferArg(const IVector& vector) + : buf_((void*)vector.getData()), valueType_(VALUE_TYPE_INT32), shape_(1) { + shape_.setDim(0, vector.getSize()); + } + + template + typename Tensor::Matrix matrix() const { + CHECK(buf_); + CHECK(valueType_ == DataType::value); + // CHECK(deviceType_ == DType); + CHECK_EQ(2, shape_.ndims()); + return typename Tensor::Matrix( + reinterpret_cast(buf_), shape_[0], shape_[1]); + } + + template + typename Tensor::Vector vector() const { + CHECK(buf_); + CHECK(valueType_ == DataType::value); + // CHECK(deviceType_ == DType); + CHECK_EQ(1, shape_.ndims()); + return typename Tensor::Vector( + shape_[0], reinterpret_cast(buf_)); + } + + virtual ~BufferArg() {} + + template + T* data() const { + return reinterpret_cast(buf_); + } + + void* data() const { return buf_; } + ValueType valueType() const { return valueType_; } + BufferType bufferType() const { return bufferType_; } + const TensorShape& shape() const { return shape_; } + + const SequenceArg& sequence() const; + const SparseMatrixArg& sparse() const; + +protected: + void* buf_; + ValueType valueType_; + TensorShape shape_; + BufferType bufferType_; + // leading dimensions. The size is dims_.size() + // Dims lds_; +}; + +// sequence start positions in a mini-batch of sequences +// shape_.ndims() == 1 +// valueType_ = int32 +// if a < b than value_.buf_[a] < value_.buf_[b] +class SequenceIdArg : public BufferArg { +public: + SequenceIdArg(void* buf, const TensorShape& shape) + : BufferArg(buf, VALUE_TYPE_INT32, shape) { + CHECK_EQ(shape_.ndims(), 1); + numSeqs_ = shape_[0] - 1; + } + + SequenceIdArg(const IVector& vector) : BufferArg(vector) { + numSeqs_ = shape_[0] - 1; + } + + ~SequenceIdArg() {} + + size_t numSeqs() const { return numSeqs_; } + +private: + size_t numSeqs_; +}; + +// sequence data +class SequenceArg : public BufferArg { +public: + SequenceArg(void* buf, + ValueType valueType, + const TensorShape& shape, + const SequenceIdArg& startPositions) + : BufferArg(buf, valueType, shape), startPositions_(startPositions) {} + + SequenceArg(const Matrix& matrix, const IVector& vector) + : BufferArg(matrix), startPositions_(vector) {} + + ~SequenceArg() {} + + void* getIdBuf() const { return startPositions_.data(); } + size_t numSeqs() const { return startPositions_.numSeqs(); } + +private: + SequenceIdArg startPositions_; +}; + +// sparse matrix +// valueType_ == float or double +// shape_.ndims() == 2 +class SparseMatrixArg : public BufferArg { +public: + SparseMatrixArg(void* buf, + ValueType valueType, + const TensorShape& shape, + const BufferArg& row, + const BufferArg& col, + size_t nnz, + SparseDataFormat format, + SparseDataType type) + : BufferArg(buf, valueType, shape), + row_(row), + col_(col), + nnz_(nnz), + format_(format), + type_(type) { + CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); + CHECK_EQ(shape_.ndims(), 2); + CHECK_EQ(row_.shape().ndims(), 1); + CHECK_EQ(col_.shape().ndims(), 1); + if (format == SPARSE_CSR_FORMAT) { + CHECK_EQ(nnz, col.shape()[0]); + } else if (format == SPARSE_CSC_FORMAT) { + CHECK_EQ(nnz, row.shape()[0]); + } + } + + SparseMatrixArg(const CpuSparseMatrix& sparse) + : BufferArg(sparse), + row_((void*)sparse.getRows(), VALUE_TYPE_INT32), + col_((void*)sparse.getCols(), VALUE_TYPE_INT32) {} + + SparseMatrixArg(const GpuSparseMatrix& sparse) + : BufferArg(sparse), + row_((void*)sparse.getRows(), VALUE_TYPE_INT32), + col_((void*)sparse.getCols(), VALUE_TYPE_INT32) {} + + ~SparseMatrixArg() {} + + void* getRowBuf() const { return row_.data(); } + + void* getColBuf() const { return col_.data(); } + + size_t nnz() const { return nnz_; } + + SparseDataFormat dataFormat() const { return format_; } + + SparseDataType dataType() const { return type_; } + +private: + BufferArg row_; + BufferArg col_; + size_t nnz_; + SparseDataFormat format_; + SparseDataType type_; +}; + +} // namespace paddle diff --git a/paddle/function/BufferArgTest.cpp b/paddle/function/BufferArgTest.cpp new file mode 100644 index 00000000000..5d669b8137e --- /dev/null +++ b/paddle/function/BufferArgTest.cpp @@ -0,0 +1,128 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +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 "BufferArg.h" +#include +#include "paddle/math/MemoryHandle.h" + +namespace paddle { + +TEST(BufferTest, BufferArg) { + TensorShape shape({8, 10}); + CpuMemoryHandle memory(shape.getElements() * + sizeOfValuType(VALUE_TYPE_FLOAT)); + BufferArg buffer(memory.getBuf(), VALUE_TYPE_FLOAT, shape); + EXPECT_EQ(buffer.data(), memory.getBuf()); +} + +TEST(BufferTest, SequenceIdArg) { + TensorShape shape({10}); + CpuMemoryHandle memory(shape.getElements() * + sizeOfValuType(VALUE_TYPE_INT32)); + SequenceIdArg buffer(memory.getBuf(), shape); + EXPECT_EQ(buffer.data(), memory.getBuf()); + EXPECT_EQ(buffer.numSeqs(), 9); +} + +TEST(BufferTest, asArgument) { + MatrixPtr matrix = Matrix::create(100, 200); + VectorPtr vector = Vector::create(100, false); + CpuSparseMatrix sparse(200, 300, 50); + + // prepare arguments + BufferArgs argments; + argments.addArg(*matrix); + argments.addArg(*vector); + argments.addArg(sparse); + + // function + auto function = [=](const BufferArgs& inputs) { + EXPECT_EQ(inputs.size(), 3); + + // check inputs[0] + EXPECT_EQ(inputs[0].shape().ndims(), 2); + EXPECT_EQ(inputs[0].shape()[0], 100); + EXPECT_EQ(inputs[0].shape()[1], 200); + EXPECT_EQ(inputs[0].data(), matrix->getData()); + + EXPECT_EQ(inputs[0].matrix().getHeight(), + matrix->getHeight()); + EXPECT_EQ(inputs[0].matrix().getWidth(), + matrix->getWidth()); + EXPECT_EQ(inputs[0].matrix().getData(), matrix->getData()); + + // check inputs[1] + EXPECT_EQ(inputs[1].shape().ndims(), 1); + EXPECT_EQ(inputs[1].shape()[0], 100); + EXPECT_EQ(inputs[1].data(), vector->getData()); + CpuVector inVector = inputs[1].vector(); + EXPECT_EQ(inVector.getSize(), vector->getSize()); + EXPECT_EQ(inVector.getData(), vector->getData()); + + // check inputs[2] + EXPECT_EQ(inputs[2].shape().ndims(), 2); + EXPECT_EQ(inputs[2].shape()[0], 200); + EXPECT_EQ(inputs[2].shape()[1], 300); + EXPECT_EQ(inputs[2].data(), sparse.getData()); + // CHECK_EQ(inputs[2].sparse().nnz(), 50); + // CHECK_EQ(inputs[2].sparse().dataFormat(), SPARSE_CSR_FORMAT); + // CHECK_EQ(inputs[2].sparse().dataType(), SPARSE_FLOAT_VALUE); + EXPECT_EQ(inputs[2].sparse().getRowBuf(), sparse.getRows()); + EXPECT_EQ(inputs[2].sparse().getColBuf(), sparse.getCols()); + }; + + // call function + function(argments); +} + +template +void FunctionApi(typename Tensor::Matrix& output, + const typename Tensor::Matrix& input); + +template <> +void FunctionApi(CpuMatrix& output, const CpuMatrix& input) { + EXPECT_EQ(output.getHeight(), 100); + EXPECT_EQ(output.getWidth(), 200); +} + +template <> +void FunctionApi(GpuMatrix& output, const GpuMatrix& input) { + EXPECT_EQ(output.getHeight(), 10); + EXPECT_EQ(output.getWidth(), 20); +} + +template +void Function(const BufferArgs& arguments) { + auto input = arguments[0].matrix(); + auto output = arguments[1].matrix(); + FunctionApi(output, input); +} + +TEST(BufferTest, Function) { + CpuMatrix cpuInput = CpuMatrix(100, 200); + CpuMatrix cpuOutput = CpuMatrix(100, 200); + BufferArgs cpuArgments; + cpuArgments.addArg(cpuInput); + cpuArgments.addArg(cpuOutput); + Function(cpuArgments); + + GpuMatrix gpuInput = GpuMatrix(10, 20); + GpuMatrix gpuOutput = GpuMatrix(10, 20); + BufferArgs gpuArgments; + gpuArgments.addArg(gpuInput); + gpuArgments.addArg(gpuOutput); + Function(gpuArgments); +} + +} // namespace paddle diff --git a/paddle/function/TensorType.h b/paddle/function/TensorType.h index 800f71a5b97..98942cff9e2 100644 --- a/paddle/function/TensorType.h +++ b/paddle/function/TensorType.h @@ -57,6 +57,11 @@ struct DataType { static const ValueType value = VALUE_TYPE_DOUBLE; }; +template <> +struct DataType { + static const ValueType value = VALUE_TYPE_INT32; +}; + namespace detail { template -- GitLab