add BufferArg

paddle/function/BufferArg.cpp

+/* 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 <glog/logging.h>
+
+#include "BufferArg.h"
+
+namespace paddle {
+
+const SequenceArg& BufferArg::sequence() const {
+  // CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA);
+  return dynamic_cast<const SequenceArg&>(*this);
+}
+
+const SparseMatrixArg& BufferArg::sparse() const {
+  // CHECK_EQ(bufferType_, TENSOR_SPARSE);
+  return dynamic_cast<const SparseMatrixArg&>(*this);
+}
+
+void BufferArgs::addArg(const Matrix& arg, const TensorShape& shape) {
+  args_.push_back(std::make_shared<BufferArg>(arg, shape));
+}
+
+void BufferArgs::addArg(const CpuSparseMatrix& arg) {
+  args_.push_back(std::make_shared<SparseMatrixArg>(arg));
+}
+
+void BufferArgs::addArg(const GpuSparseMatrix& arg) {
+  args_.push_back(std::make_shared<SparseMatrixArg>(arg));
+}
+
+}  // namespace paddle

paddle/function/BufferArg.h

+/* 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 <glog/logging.h>
+
+#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<BufferArg> BufferArgPtr;
+
+class BufferArgs {
+public:
+  BufferArgs() {}
+  size_t size() const { return args_.size(); }
+
+  // add argument into BufferArgss
+  template <typename Tensor>
+  void addArg(const Tensor& arg) {
+    args_.push_back(std::make_shared<BufferArg>(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<BufferArgPtr> 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<real>::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<real>::value),
+        shape_(shape) {
+    CHECK_EQ(matrix.getElementCnt(), shape.getElements());
+  }
+
+  BufferArg(const Vector& vector)
+      : buf_((void*)vector.getData()),
+        valueType_(DataType<real>::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 <DeviceType DType>
+  typename Tensor<real, DType>::Matrix matrix() const {
+    CHECK(buf_);
+    CHECK(valueType_ == DataType<real>::value);
+    // CHECK(deviceType_ == DType);
+    CHECK_EQ(2, shape_.ndims());
+    return typename Tensor<real, DType>::Matrix(
+        reinterpret_cast<real*>(buf_), shape_[0], shape_[1]);
+  }
+
+  template <typename VType, DeviceType DType>
+  typename Tensor<VType, DType>::Vector vector() const {
+    CHECK(buf_);
+    CHECK(valueType_ == DataType<VType>::value);
+    // CHECK(deviceType_ == DType);
+    CHECK_EQ(1, shape_.ndims());
+    return typename Tensor<VType, DType>::Vector(
+        shape_[0], reinterpret_cast<VType*>(buf_));
+  }
+
+  virtual ~BufferArg() {}
+
+  template <typename T>
+  T* data() const {
+    return reinterpret_cast<T*>(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

paddle/function/BufferArgTest.cpp

+/* 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 <gtest/gtest.h>
+#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<DEVICE_TYPE_CPU>().getHeight(),
+              matrix->getHeight());
+    EXPECT_EQ(inputs[0].matrix<DEVICE_TYPE_CPU>().getWidth(),
+              matrix->getWidth());
+    EXPECT_EQ(inputs[0].matrix<DEVICE_TYPE_CPU>().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<real, DEVICE_TYPE_CPU>();
+    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 <DeviceType DType>
+void FunctionApi(typename Tensor<real, DType>::Matrix& output,
+                 const typename Tensor<real, DType>::Matrix& input);
+
+template <>
+void FunctionApi<DEVICE_TYPE_CPU>(CpuMatrix& output, const CpuMatrix& input) {
+  EXPECT_EQ(output.getHeight(), 100);
+  EXPECT_EQ(output.getWidth(), 200);
+}
+
+template <>
+void FunctionApi<DEVICE_TYPE_GPU>(GpuMatrix& output, const GpuMatrix& input) {
+  EXPECT_EQ(output.getHeight(), 10);
+  EXPECT_EQ(output.getWidth(), 20);
+}
+
+template <DeviceType DType>
+void Function(const BufferArgs& arguments) {
+  auto input = arguments[0].matrix<DType>();
+  auto output = arguments[1].matrix<DType>();
+  FunctionApi<DType>(output, input);
+}
+
+TEST(BufferTest, Function) {
+  CpuMatrix cpuInput = CpuMatrix(100, 200);
+  CpuMatrix cpuOutput = CpuMatrix(100, 200);
+  BufferArgs cpuArgments;
+  cpuArgments.addArg(cpuInput);
+  cpuArgments.addArg(cpuOutput);
+  Function<DEVICE_TYPE_CPU>(cpuArgments);
+
+  GpuMatrix gpuInput = GpuMatrix(10, 20);
+  GpuMatrix gpuOutput = GpuMatrix(10, 20);
+  BufferArgs gpuArgments;
+  gpuArgments.addArg(gpuInput);
+  gpuArgments.addArg(gpuOutput);
+  Function<DEVICE_TYPE_GPU>(gpuArgments);
+}
+
+}  // namespace paddle

paddle/function/TensorType.h

@@ -57,6 +57,11 @@ struct DataType<double> {
   static const ValueType value = VALUE_TYPE_DOUBLE;
 };
 
+template <>
+struct DataType<int> {
+  static const ValueType value = VALUE_TYPE_INT32;
+};
+
 namespace detail {
 
 template <typename VType, DeviceType Device>