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@ -23,10 +23,11 @@ limitations under the License. */
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namespace paddle {
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enum BufferType {
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TENSOR_NORMAL = 0,
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TENSOR_SEQUENCE_ID = 1,
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TENSOR_SEQUENCE_DATA = 2,
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TENSOR_SPARSE = 3
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TENSOR_UNKNOWN = 0,
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TENSOR_NORMAL = 1,
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TENSOR_SEQUENCE_ID = 2,
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TENSOR_SEQUENCE_DATA = 3,
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TENSOR_SPARSE = 4
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};
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enum SparseDataType {
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@ -39,7 +40,6 @@ enum SparseDataFormat { SPARSE_CSR_FORMAT = 0, SPARSE_CSC_FORMAT = 1 };
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class BufferArg;
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class SequenceArg;
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class SparseMatrixArg;
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typedef std::shared_ptr<BufferArg> BufferArgPtr;
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/**
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* \brief BufferArg used as the argument type of Function.
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@ -50,6 +50,11 @@ typedef std::shared_ptr<BufferArg> BufferArgPtr;
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* 3. SequenceArg for a Buffer of sequence data.
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* 4. SparseMatrixArg for a Buffer of sparse matrix.
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*
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* Buffer shape
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* For most buffers, the first dimension `shape()[0]` represents
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* the size of the mini-batch.
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*
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* Buffer argType
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* There is an ArgType property for the BufferArg used as Function Output.
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* Whether the result of the Function calculation is assigned to the
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* output Buffer or added to the output Buffer is determined by the
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@ -71,6 +76,14 @@ public:
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ArgType getArgType() const { return argType_; }
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public:
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BufferArg(ValueType valueType,
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const TensorShape& shape,
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ArgType argType = UNSPECIFIED)
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: buf_(nullptr),
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valueType_(valueType),
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shape_(shape),
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argType_(argType) {}
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BufferArg(void* buf,
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ValueType valueType,
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const TensorShape& shape,
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@ -86,6 +99,7 @@ public:
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valueType_(DataType<real>::value),
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shape_(2),
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argType_(argType) {
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bufferType_ = TENSOR_NORMAL;
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shape_.setDim(0, matrix.getHeight());
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shape_.setDim(1, matrix.getWidth());
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}
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@ -98,6 +112,7 @@ public:
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valueType_(DataType<real>::value),
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shape_(shape),
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argType_(argType) {
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bufferType_ = TENSOR_NORMAL;
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CHECK_EQ(matrix.getElementCnt(), shape.getElements());
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}
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@ -107,6 +122,7 @@ public:
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valueType_(DataType<real>::value),
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shape_(1),
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argType_(argType) {
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bufferType_ = TENSOR_NORMAL;
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shape_.setDim(0, vector.getSize());
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}
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@ -116,6 +132,7 @@ public:
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valueType_(VALUE_TYPE_INT32),
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shape_(1),
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argType_(argType) {
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bufferType_ = TENSOR_NORMAL;
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shape_.setDim(0, vector.getSize());
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}
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@ -150,6 +167,8 @@ public:
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ValueType valueType() const { return valueType_; }
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BufferType bufferType() const { return bufferType_; }
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const TensorShape& shape() const { return shape_; }
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bool isSparse() const { return (TENSOR_SPARSE == bufferType_); }
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bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; }
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const SequenceArg& sequence() const;
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const SparseMatrixArg& sparse() const;
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@ -158,8 +177,8 @@ protected:
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void* buf_;
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ValueType valueType_;
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TensorShape shape_;
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BufferType bufferType_;
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ArgType argType_ = UNSPECIFIED;
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BufferType bufferType_{TENSOR_UNKNOWN};
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ArgType argType_{UNSPECIFIED};
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// leading dimensions. The size is dims_.size()
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// Dims lds_;
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};
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@ -170,15 +189,24 @@ protected:
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// if a < b then value_.buf_[a] < value_.buf_[b]
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class SequenceIdArg : public BufferArg {
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public:
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SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED)
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: BufferArg(VALUE_TYPE_INT32, shape, argType) {
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CHECK_EQ(shape_.ndims(), (size_t)1);
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CHECK_GT(shape_[0], 1);
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numSeqs_ = shape_[0] - 1;
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}
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SequenceIdArg(void* buf,
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const TensorShape& shape,
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ArgType argType = UNSPECIFIED)
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: BufferArg(buf, VALUE_TYPE_INT32, shape, argType) {
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bufferType_ = TENSOR_SEQUENCE_ID;
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CHECK_EQ(shape_.ndims(), (size_t)1);
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numSeqs_ = shape_[0] - 1;
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}
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SequenceIdArg(const IVector& vector) : BufferArg(vector) {
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bufferType_ = TENSOR_SEQUENCE_ID;
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numSeqs_ = shape_[0] - 1;
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}
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@ -190,26 +218,41 @@ private:
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size_t numSeqs_;
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};
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// sequence data
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// sequences data
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// For mini-batch calculate,
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// one batch can contain more than one sequence of data.
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// SequenceArg can be used to represent sequences that contain multiple
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// unequal lengths.
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class SequenceArg : public BufferArg {
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public:
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SequenceArg(ValueType valueType,
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const TensorShape& shape,
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ArgType argType = UNSPECIFIED)
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: BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {}
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SequenceArg(void* buf,
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ValueType valueType,
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const TensorShape& shape,
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const SequenceIdArg& startPositions,
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ArgType argType = UNSPECIFIED)
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: BufferArg(buf, valueType, shape, argType),
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startPositions_(startPositions) {}
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startPositions_(startPositions) {
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bufferType_ = TENSOR_SEQUENCE_DATA;
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}
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SequenceArg(const Matrix& matrix,
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const IVector& vector,
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ArgType argType = UNSPECIFIED)
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: BufferArg(matrix, argType), startPositions_(vector) {}
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: BufferArg(matrix, argType), startPositions_(vector) {
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bufferType_ = TENSOR_SEQUENCE_DATA;
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}
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~SequenceArg() {}
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void* getIdBuf() const { return startPositions_.data(); }
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size_t numSeqs() const { return startPositions_.numSeqs(); }
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SequenceIdArg& getSequenceId() { return startPositions_; }
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const SequenceIdArg& getSequenceId() const { return startPositions_; }
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private:
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SequenceIdArg startPositions_;
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@ -235,6 +278,7 @@ public:
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nnz_(nnz),
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format_(format),
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type_(type) {
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bufferType_ = TENSOR_SPARSE;
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CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE));
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CHECK_EQ(shape_.ndims(), (size_t)2);
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CHECK_EQ(row_.shape().ndims(), (size_t)1);
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王益
9 years ago
