Skip to content

P
Paddle

PaddlePaddle / Paddle
1 年多 前同步成功

通知 2302
Star 20931
Fork 5422
P
Paddle
提交 0ae5ac16 编写于 作者: H hedaoyuan 提交者： GitHub
浏览文件
操作

Merge pull request #1080 from tianbingsz/paddle_func_context 

Context Projection Paddle Function-- follow comments
上级 c13540a6 e9794214
显示空白变更内容
内联 并排
Showing with 291 addition and 246 deletion
paddle/function/BufferArg.cpp
浏览文件 @ 0ae5ac16
......@@ -20,23 +20,27 @@ limitations under the License. */
namespace paddle {
const SequenceArg& BufferArg::sequence() const {
// CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA);
CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA);
return dynamic_cast<const SequenceArg&>(*this);
}
const SparseMatrixArg& BufferArg::sparse() const {
// CHECK_EQ(bufferType_, TENSOR_SPARSE);
CHECK_EQ(bufferType_, TENSOR_SPARSE);
return dynamic_cast<const SparseMatrixArg&>(*this);
}
SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType)
: BufferArg(sparse, argType),
row_(reinterpret_cast<void*>(sparse.getRows()), VALUE_TYPE_INT32),
col_(reinterpret_cast<void*>(sparse.getCols()), VALUE_TYPE_INT32) {}
col_(reinterpret_cast<void*>(sparse.getCols()), VALUE_TYPE_INT32) {
bufferType_ = TENSOR_SPARSE;
}
SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType)
: BufferArg(sparse, argType),
row_(reinterpret_cast<void*>(sparse.getRows()), VALUE_TYPE_INT32),
col_(reinterpret_cast<void*>(sparse.getCols()), VALUE_TYPE_INT32) {}
col_(reinterpret_cast<void*>(sparse.getCols()), VALUE_TYPE_INT32) {
bufferType_ = TENSOR_SPARSE;
}
} // namespace paddle
paddle/function/BufferArg.h
浏览文件 @ 0ae5ac16
......@@ -23,10 +23,11 @@ limitations under the License. */
namespace paddle {
enum BufferType {
TENSOR_NORMAL = 0,
TENSOR_SEQUENCE_ID = 1,
TENSOR_SEQUENCE_DATA = 2,
TENSOR_SPARSE = 3
TENSOR_UNKNOWN = 0,
TENSOR_NORMAL = 1,
TENSOR_SEQUENCE_ID = 2,
TENSOR_SEQUENCE_DATA = 3,
TENSOR_SPARSE = 4
};
enum SparseDataType {
......@@ -86,6 +87,7 @@ public:
valueType_(DataType<real>::value),
shape_(2),
argType_(argType) {
bufferType_ = TENSOR_NORMAL;
shape_.setDim(0, matrix.getHeight());
shape_.setDim(1, matrix.getWidth());
}
......@@ -98,6 +100,7 @@ public:
valueType_(DataType<real>::value),
shape_(shape),
argType_(argType) {
bufferType_ = TENSOR_NORMAL;
CHECK_EQ(matrix.getElementCnt(), shape.getElements());
}
......@@ -107,6 +110,7 @@ public:
valueType_(DataType<real>::value),
shape_(1),
argType_(argType) {
bufferType_ = TENSOR_NORMAL;
shape_.setDim(0, vector.getSize());
}
......@@ -116,6 +120,7 @@ public:
valueType_(VALUE_TYPE_INT32),
shape_(1),
argType_(argType) {
bufferType_ = TENSOR_NORMAL;
shape_.setDim(0, vector.getSize());
}
......@@ -150,6 +155,8 @@ public:
ValueType valueType() const { return valueType_; }
BufferType bufferType() const { return bufferType_; }
const TensorShape& shape() const { return shape_; }
bool isSparse() const { return (TENSOR_SPARSE == bufferType_); }
bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; }
const SequenceArg& sequence() const;
const SparseMatrixArg& sparse() const;
......@@ -158,8 +165,8 @@ protected:
void* buf_;
ValueType valueType_;
TensorShape shape_;
BufferType bufferType_;
ArgType argType_ = UNSPECIFIED;
BufferType bufferType_{TENSOR_UNKNOWN};
ArgType argType_{UNSPECIFIED};
// leading dimensions. The size is dims_.size()
// Dims lds_;
};
......@@ -174,11 +181,13 @@ public:
const TensorShape& shape,
ArgType argType = UNSPECIFIED)
: BufferArg(buf, VALUE_TYPE_INT32, shape, argType) {
bufferType_ = TENSOR_SEQUENCE_ID;
CHECK_EQ(shape_.ndims(), (size_t)1);
numSeqs_ = shape_[0] - 1;
}
SequenceIdArg(const IVector& vector) : BufferArg(vector) {
bufferType_ = TENSOR_SEQUENCE_ID;
numSeqs_ = shape_[0] - 1;
}
......@@ -190,7 +199,7 @@ private:
size_t numSeqs_;
};
// sequence data
// sequence data {seqId(vec), buf(matrix)}
class SequenceArg : public BufferArg {
public:
SequenceArg(void* buf,
......@@ -199,17 +208,22 @@ public:
const SequenceIdArg& startPositions,
ArgType argType = UNSPECIFIED)
: BufferArg(buf, valueType, shape, argType),
startPositions_(startPositions) {}
startPositions_(startPositions) {
bufferType_ = TENSOR_SEQUENCE_DATA;
}
SequenceArg(const Matrix& matrix,
const IVector& vector,
ArgType argType = UNSPECIFIED)
: BufferArg(matrix, argType), startPositions_(vector) {}
: BufferArg(matrix, argType), startPositions_(vector) {
bufferType_ = TENSOR_SEQUENCE_DATA;
}
~SequenceArg() {}
void* getIdBuf() const { return startPositions_.data(); }
size_t numSeqs() const { return startPositions_.numSeqs(); }
const SequenceIdArg& getSequenceIds() const { return startPositions_; }
private:
SequenceIdArg startPositions_;
......@@ -235,6 +249,7 @@ public:
nnz_(nnz),
format_(format),
type_(type) {
bufferType_ = TENSOR_SPARSE;
CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE));
CHECK_EQ(shape_.ndims(), (size_t)2);
CHECK_EQ(row_.shape().ndims(), (size_t)1);
......
paddle/function/CMakeLists.txt
浏览文件 @ 0ae5ac16
......@@ -24,7 +24,7 @@ if(WITH_TESTING)
add_simple_unittest(TensorTypeTest)
add_simple_unittest(BufferArgTest)
add_simple_unittest(FunctionTest)
# add_simple_unittest(ContextProjectionOpTest)
add_simple_unittest(ContextProjectionOpTest)
endif()
endif()
......
paddle/function/ContextProjectionOp.cpp
浏览文件 @ 0ae5ac16
......@@ -17,7 +17,10 @@ limitations under the License. */
#include "paddle/math/Vector.h"
namespace paddle {
/**
* Context Projection Forward with CPU Matrix Device.
*
*/
template <>
void ContextProjectionForward<DEVICE_TYPE_CPU>(CpuMatrix& out_mat,
const CpuMatrix& input_mat,
......@@ -70,10 +73,30 @@ void ContextProjectionForward<DEVICE_TYPE_CPU>(CpuMatrix& out_mat,
}
/**
* \param inputs[0] input value.
* \param inputs[1] input weight.
* \param inputs[2] input sequence.
* \param outputs[0] output value.
* Paddle Function for Context Projection Forward.
* Calculate the output layer value sequence after context projection.
*
* What is Context Projection for a sequence?
* For example, assumed input (x) has 4 words and the dimension of each word
* representation is 2. If we use zero to pad instead of learned weight to pad,
* and the context_lenth is 3, the output (y) is:
*
* @code
* x = [a1, a2;
* b1, b2;
* c1, c2;
* d1, d2]
* y = [0, 0, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, d1, d2;
* c1, c2, d1, d2, 0, 0]
* @endcode
*
* \param outputs[0].matrix output layer value, n * (d * l)
* \param outputs[0].vector start position sequence, n * 1
* \param inputs[0].matrix input layer value, n * d
* \param inputs[0].vector start position sequence, n * 1
* \param inputs[1].matrix input layer weight, pad * d
*/
template <DeviceType Device>
class ContextProjectionForwardFunc : public FunctionBase {
......@@ -85,28 +108,38 @@ public:
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ((size_t)3, inputs.size());
CHECK(1 == inputs.size() || 2 == inputs.size());
CHECK_EQ((size_t)1, outputs.size());
CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg())
<< "SequenceArg required here";
const auto val_seqs = dynamic_cast<const SequenceArg&>(inputs[0]);
auto out_seq = dynamic_cast<const SequenceArg&>(outputs[0]);
CHECK(outputs[0].data() && inputs[0].data() && inputs[2].data());
CHECK_EQ(outputs[0].shape().ndims(), (size_t)2);
CHECK_EQ(inputs[0].shape().ndims(), (size_t)2);
CHECK(out_seq.data() && val_seqs.data() &&
val_seqs.getSequenceIds().data());
CHECK_EQ(out_seq.shape().ndims(), (size_t)2);
CHECK_EQ(val_seqs.shape().ndims(), (size_t)2);
CHECK_EQ(val_seqs.getSequenceIds().shape().ndims(), (size_t)1);
if (2 == inputs.size()) {
CHECK_EQ(inputs[1].shape().ndims(), (size_t)2);
CHECK_EQ(inputs[2].shape().ndims(), (size_t)1);
}
/// dim of output = dim of input * context_length
CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_);
/// dim of input == dim of weight
CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]);
CHECK_EQ(out_seq.shape()[1], val_seqs.shape()[1] * context_length_);
/// input and output has the same batch_size
CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]);
CHECK_EQ(val_seqs.shape()[0], out_seq.shape()[0]);
/// dim of input == dim of weight
if (2 == inputs.size()) {
CHECK_EQ(val_seqs.shape()[1], inputs[1].shape()[1]);
}
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
auto out_mat = outputs[0].matrix<Device>();
auto in_mat = inputs[0].matrix<Device>();
auto w_mat = !inputs[1].data()
? typename Tensor<real, Device>::Matrix(nullptr, 0, 0)
: inputs[1].matrix<Device>();
auto seq_vec = inputs[2].vector<int, Device>();
CHECK_EQ(out_seq.getArgType(), ADD_TO);
auto out_mat = out_seq.matrix<Device>();
const auto in_mat = val_seqs.matrix<Device>();
const auto w_mat =
(2 == inputs.size())
? inputs[1].matrix<Device>()
: typename Tensor<real, Device>::Matrix(nullptr, 0, 0);
const auto seq_vec = val_seqs.getSequenceIds().vector<int, Device>();
ContextProjectionForward<Device>(out_mat,
in_mat,
w_mat,
......@@ -122,8 +155,12 @@ private:
size_t begin_pad_;
};
/**
* Context Projection Backward with CPU Matrix Device.
*
*/
template <>
void ContextProjectionBackward<DEVICE_TYPE_CPU>(CpuMatrix& out_grad_mat,
void ContextProjectionBackward<DEVICE_TYPE_CPU>(const CpuMatrix& out_grad_mat,
CpuMatrix& in_grad_mat,
CpuMatrix& w_grad_mat,
const CpuIVector& seq_vec,
......@@ -146,7 +183,8 @@ void ContextProjectionBackward<DEVICE_TYPE_CPU>(CpuMatrix& out_grad_mat,
int64_t pad_size =
std::min(starts[i] - begin, starts[i + 1] - starts[i]);
if (is_padding && w_grad_mat) {
MatrixPtr mat = out_grad_mat.subMatrix(starts[i], pad_size);
MatrixPtr mat = const_cast<CpuMatrix&>(out_grad_mat)
.subMatrix(starts[i], pad_size);
MatrixPtr sub = w_grad_mat.subMatrix(j, pad_size);
sub->addAtOffset(*mat, j * input_dim);
}
......@@ -157,8 +195,8 @@ void ContextProjectionBackward<DEVICE_TYPE_CPU>(CpuMatrix& out_grad_mat,
int64_t pad_size =
std::min(end - starts[i + 1], starts[i + 1] - starts[i]);
if (is_padding && w_grad_mat) {
MatrixPtr mat =
out_grad_mat.subMatrix(starts[i + 1] - pad_size, pad_size);
MatrixPtr mat = const_cast<CpuMatrix&>(out_grad_mat)
.subMatrix(starts[i + 1] - pad_size, pad_size);
MatrixPtr sub = w_grad_mat.subMatrix(
begin_pad + context_start + j - pad_size, pad_size);
sub->addAtOffset(*mat, j * input_dim);
......@@ -169,17 +207,22 @@ void ContextProjectionBackward<DEVICE_TYPE_CPU>(CpuMatrix& out_grad_mat,
if (end <= begin) continue;
if (!in_grad_mat) continue;
MatrixPtr src = in_grad_mat.subMatrix(begin, end - begin);
MatrixPtr dst = out_grad_mat.subMatrix(dst_begin, dst_end - dst_begin);
MatrixPtr dst = const_cast<CpuMatrix&>(out_grad_mat)
.subMatrix(dst_begin, dst_end - dst_begin);
src->addAtOffset(*dst, j * input_dim);
}
}
}
/**
* \param inputs[0] input grad.
* \param inputs[1] weight grad.
* \param inputs[2] input sequence.
* \param outputs[0] output value.
* Context Projection Backward Function.
* Update the weight gradient and input layer gradient with backprop
*
* \param inputs[0].matrix output layer grad, n * (d * l)
* \param inputs[0].vector start position sequence, n * 1
* \param outputs[0].matrix input layer grad, n * d
* \param outputs[0].vector start position sequence, n * 1
* \param outputs[1] weight grad, pad * d
*/
template <DeviceType Device>
class ContextProjectionBackwardFunc : public FunctionBase {
......@@ -193,32 +236,36 @@ public:
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ((size_t)3, inputs.size());
CHECK_EQ((size_t)1, outputs.size());
CHECK(outputs[0].data() && inputs[2].data());
CHECK_EQ(outputs[0].shape().ndims(), (size_t)2);
CHECK_EQ(inputs[0].shape().ndims(), (size_t)2);
CHECK_EQ(inputs[1].shape().ndims(), (size_t)2);
CHECK_EQ(inputs[2].shape().ndims(), (size_t)1);
/// dim of input == dim of weight
CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]);
/// input and output has the same batch_size
CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]);
/// dim of output = dim of input * context_length
CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_);
CHECK_EQ((size_t)1, inputs.size());
CHECK_EQ((size_t)2, outputs.size());
CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg())
<< "SequenceArg required here";
const auto in_seq = dynamic_cast<const SequenceArg&>(inputs[0]);
auto out_seq = dynamic_cast<const SequenceArg&>(outputs[0]);
CHECK(in_seq.data() && in_seq.getSequenceIds().data());
CHECK_EQ(in_seq.shape().ndims(), (size_t)2);
CHECK_EQ(in_seq.getSequenceIds().shape().ndims(), (size_t)1);
CHECK_EQ(out_seq.shape().ndims(), (size_t)2);
CHECK_EQ(out_seq.getSequenceIds().shape().ndims(), (size_t)1);
CHECK_EQ(outputs[1].shape().ndims(), (size_t)2);
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
/// dim of input grad == dim of weight
CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]);
/// input and output grad has the same batch_size
CHECK_EQ(out_seq.shape()[0], in_seq.shape()[0]);
/// dim of output grad = dim of input grad * context_length
CHECK_EQ(in_seq.shape()[1], out_seq.shape()[1] * context_length_);
CHECK_EQ(out_seq.getArgType(), ADD_TO);
CHECK_EQ(outputs[1].getArgType(), ADD_TO);
auto out_grad_mat = outputs[0].matrix<Device>();
const auto seq_vec = in_seq.getSequenceIds().vector<int, Device>();
const auto out_grad_mat = in_seq.matrix<Device>();
auto in_grad_mat =
!inputs[0].data() ? typename Tensor<real, Device>::Matrix(nullptr, 0, 0)
: inputs[0].matrix<Device>();
auto w_grad_mat = !inputs[1].data()
!out_seq.data() ? typename Tensor<real, Device>::Matrix(nullptr, 0, 0)
: out_seq.matrix<Device>();
auto w_grad_mat = !outputs[1].data()
? typename Tensor<real, Device>::Matrix(nullptr, 0, 0)
: inputs[1].matrix<Device>();
auto seq_vec = inputs[2].vector<int, Device>();
: outputs[1].matrix<Device>();
ContextProjectionBackward<Device>(out_grad_mat,
in_grad_mat,
w_grad_mat,
......@@ -238,11 +285,16 @@ private:
size_t total_pad_;
};
#if 0
/**
* \param inputs[0] input grad.
* \param inputs[1] input sequence.
* \param outputs[0] output grad.
* Context Projection Backward Data Function
* Update input layer grad
* input: sequence of output layer grad
* output: sequence of input layer grad
*
* \param outputs[0].matrix input layer grad, n * d
* \param outputs[0].vector start position sequence, n * 1
* \param inputs[0].matrix output layer grad, n * (d * l)
* \param inputs[0].vector start positon sequence, n * 1
*/
template <DeviceType Device>
class ContextProjectionBackwardDataFunc : public FunctionBase {
......@@ -252,32 +304,30 @@ public:
context_start_ = config.get<int>("context_start");
}
void calc(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) override {
CHECK_EQ(2, static_cast<int>(inputs.size()));
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1, static_cast<int>(inputs.size()));
CHECK_EQ(1, static_cast<int>(outputs.size()));
CHECK_EQ(0, static_cast<int>(inouts.size()));
CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData());
CHECK_EQ(static_cast<int>(outputs[0].dims_.size()), 2);
CHECK_EQ(static_cast<int>(inputs[0].dims_.size()), 2);
CHECK_EQ(static_cast<int>(inputs[1].dims_.size()), 1);
CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_);
CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg())
<< "SequenceArg required here";
const auto in_seq = dynamic_cast<const SequenceArg&>(inputs[0]);
const auto out_seq = dynamic_cast<const SequenceArg&>(outputs[0]);
CHECK(in_seq.data() && out_seq.data() && in_seq.getSequenceIds().data());
CHECK_EQ(static_cast<int>(out_seq.shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.getSequenceIds().shape().ndims()), 1);
/// output layer grad dim == input layer grad dim * context_length_
CHECK_EQ(in_seq.shape().ndims(), out_seq.shape().ndims() * context_length_);
/// input and output has the same batch_size
CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]);
CHECK_EQ(in_seq.shape()[0], out_seq.shape()[0]);
CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]);
const auto in_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]);
typename SequenceT<Device>::type seq_vec(
inputs[1].dims_[0], reinterpret_cast<int*>(inputs[1].getData()));
const auto out_grad_mat = in_seq.matrix<Device>();
const auto seq_vec = in_seq.getSequenceIds().vector<int, Device>();
auto in_grad_mat = out_seq.matrix<Device>();
ContextProjectionBackwardData<Device>(out_grad_mat.get(),
in_grad_mat.get(),
seq_vec,
context_length_,
context_start_);
ContextProjectionBackwardData<Device>(
out_grad_mat, in_grad_mat, seq_vec, context_length_, context_start_);
}
private:
......@@ -286,9 +336,14 @@ private:
};
/**
* \param inputs[0] weight grad.
* \param inputs[1] input sequence.
* \param outputs[0] output grad.
* Context Projection Backward Weight Function
* Update weight grad by backprop
* input: sequence of output layer grad
* output: weight grad
*
* \param outputs[0] weight grad, pad * d
* \param inputs[0].matrix output layer grad, n * (d * l)
* \param inputs[0].vecotr start positon sequence, n * 1
*/
template <DeviceType Device>
class ContextProjectionBackwardWeightFunc : public FunctionBase {
......@@ -300,28 +355,25 @@ public:
total_pad_ = config.get<size_t>("total_pad");
}
void calc(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) override {
CHECK_EQ(2, static_cast<int>(inputs.size()));
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1, static_cast<int>(inputs.size()));
CHECK_EQ(1, static_cast<int>(outputs.size()));
CHECK_EQ(0, static_cast<int>(inouts.size()));
CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData());
CHECK_EQ(static_cast<int>(outputs[0].dims_.size()), 2);
CHECK_EQ(static_cast<int>(inputs[0].dims_.size()), 2);
CHECK_EQ(static_cast<int>(inputs[1].dims_.size()), 1);
CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_);
auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]);
auto w_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]);
typename SequenceT<Device>::type seq_vec(
inputs[1].dims_[0], reinterpret_cast<int*>(inputs[1].getData()));
CHECK(inputs[0].isSequenceArg()) << "SequenceArg required here";
const auto in_seq = dynamic_cast<const SequenceArg&>(inputs[0]);
CHECK(in_seq.data() && in_seq.getSequenceIds().data() && outputs[0].data());
CHECK_EQ(static_cast<int>(outputs[0].shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.shape().ndims()), 2);
CHECK_EQ(static_cast<int>(in_seq.getSequenceIds().shape().ndims()), 1);
CHECK_EQ(in_seq.shape()[0], outputs[0].shape()[0]);
/// output layer grad dim == weight dim * context_length_
CHECK_EQ(in_seq.shape()[1], outputs[0].shape()[1] * context_length_);
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
ContextProjectionBackwardWeight<Device>(out_grad_mat.get(),
w_grad_mat.get(),
const auto seq_vec = in_seq.getSequenceIds().vector<int, Device>();
const auto out_grad_mat = in_seq.matrix<Device>();
auto w_grad_mat = outputs[0].matrix<Device>();
ContextProjectionBackwardWeight<Device>(out_grad_mat,
w_grad_mat,
seq_vec,
context_length_,
context_start_,
......@@ -335,7 +387,6 @@ private:
size_t begin_pad_;
size_t total_pad_;
};
#endif
REGISTER_TYPED_FUNC(ContextProjectionForward,
CPU,
......@@ -350,7 +401,6 @@ REGISTER_TYPED_FUNC(ContextProjectionForward,
REGISTER_TYPED_FUNC(ContextProjectionBackward,
GPU,
ContextProjectionBackwardFunc);
#if 0
REGISTER_TYPED_FUNC(ContextProjectionBackwardData,
GPU,
ContextProjectionBackwardDataFunc);
......@@ -358,5 +408,4 @@ REGISTER_TYPED_FUNC(ContextProjectionBackwardWeight,
GPU,
ContextProjectionBackwardWeightFunc);
#endif
#endif
} // namespace paddle
paddle/function/ContextProjectionOp.h
浏览文件 @ 0ae5ac16
......@@ -21,7 +21,7 @@ namespace paddle {
/**
* \brief Context Projection Forward.
*
* \param[out] outputs output data.
* \param[in/out] outputs output data.
* \param[in] input input data.
* \param[in] weight input weight.
* \param[in] sequence input data.
......@@ -56,7 +56,7 @@ void ContextProjectionForward(
*/
template <DeviceType DType>
void ContextProjectionBackward(
typename Tensor<real, DType>::Matrix& out_grad,
const typename Tensor<real, DType>::Matrix& out_grad,
typename Tensor<real, DType>::Matrix& in_grad,
typename Tensor<real, DType>::Matrix& w_grad,
const typename Tensor<int, DType>::Vector& seq_vec,
......@@ -68,7 +68,7 @@ void ContextProjectionBackward(
template <DeviceType DType>
void ContextProjectionBackwardData(
typename Tensor<real, DType>::Matrix& out_grad,
const typename Tensor<real, DType>::Matrix& out_grad,
typename Tensor<real, DType>::Matrix& in_grad,
const typename Tensor<int, DType>::Vector& sequence,
size_t context_length,
......@@ -76,7 +76,7 @@ void ContextProjectionBackwardData(
template <DeviceType DType>
void ContextProjectionBackwardWeight(
typename Tensor<real, DType>::Matrix& out_grad,
const typename Tensor<real, DType>::Matrix& out_grad,
typename Tensor<real, DType>::Matrix& w_grad,
const typename Tensor<int, DType>::Vector& seq_vec,
size_t context_length,
......
paddle/function/ContextProjectionOpGpu.cu
浏览文件 @ 0ae5ac16
......@@ -138,10 +138,10 @@ void ContextProjectionForward<DEVICE_TYPE_GPU>(GpuMatrix& output,
begin_pad);
}
__global__ void KeContextProjectionBackwardData(real* out_grad,
__global__ void KeContextProjectionBackwardData(const real* out_grad,
const int* sequence,
real* in_grad,
int input_dim,
size_t input_dim,
int context_length,
int context_start) {
int idx = threadIdx.x;
......@@ -152,7 +152,8 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
real value = 0;
int instances = seq_end - seq_start + context_length - 1;
out_grad += seq_start * input_dim * context_length;
auto out = const_cast<real*>(out_grad);
out += seq_start * input_dim * context_length;
in_grad += seq_start * input_dim;
for (int k = 0; k <= input_dim / block_size; k++) {
if (idx < input_dim) {
......@@ -169,7 +170,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
int outx = (i - context_length) < 0 ? i : (context_length - 1);
int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1));
real* output_r =
out_grad + outy * input_dim * context_length + outx * input_dim;
out + outy * input_dim * context_length + outx * input_dim;
for (int j = outy; j < seq_end - seq_start; j++) {
value += output_r[idx];
if (j - outy == outx) break;
......@@ -194,7 +195,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
* @param[in] context_start context start.
*
*/
void hl_context_projection_backward_data(real* out_grad,
void hl_context_projection_backward_data(const real* out_grad,
const int* sequence,
real* input_grad,
size_t num_sequences,
......@@ -216,7 +217,7 @@ void hl_context_projection_backward_data(real* out_grad,
}
template <>
void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(GpuMatrix& out_grad,
void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
GpuMatrix& in_grad,
const GpuIVector& sequence,
size_t context_length,
......@@ -231,7 +232,7 @@ void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(GpuMatrix& out_grad,
}
template<int THREADS_X, int THREADS_Y>
__global__ void KeContextProjectionBackwardWeight(real* out_grad,
__global__ void KeContextProjectionBackwardWeight(const real* out_grad,
const int* sequence,
real* w_grad,
int num_sequences,
......@@ -254,7 +255,8 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad,
for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) {
int seq_start = sequence[seqId];
int seq_end = sequence[seqId+1];
output_r = out_grad + seq_start * w_dim * context_length;
output_r = const_cast<real*>(out_grad)
+ seq_start * w_dim * context_length;
if (context_start < 0) {
if (padId + context_start < 0) {
......@@ -318,7 +320,7 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad,
* beginning.
*
*/
void hl_context_projection_backward_weight(real* out_grad,
void hl_context_projection_backward_weight(const real* out_grad,
const int* sequence,
real* w_grad,
size_t num_sequences,
......@@ -346,7 +348,7 @@ void hl_context_projection_backward_weight(real* out_grad,
template <>
void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
GpuMatrix& out_grad,
const GpuMatrix& out_grad,
GpuMatrix& w_grad,
const GpuIVector& seq_vec,
size_t context_length,
......@@ -365,7 +367,7 @@ void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
}
template <>
void ContextProjectionBackward<DEVICE_TYPE_GPU>(GpuMatrix& out_grad,
void ContextProjectionBackward<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
GpuMatrix& in_grad,
GpuMatrix& w_grad,
const GpuIVector& sequence,
......
paddle/function/ContextProjectionOpTest.cpp
浏览文件 @ 0ae5ac16
......@@ -56,22 +56,25 @@ void testMatrixProjectionForward(int context_start,
cpu_out.randomizeUniform();
gpu_out.copyFrom(cpu_out);
compare.getCpuFunction()->calc(
{Tensor(cpu_in.getData(), Dims{batch_size, input_dim}),
Tensor(cpu_weight ? cpu_weight->getData() : nullptr,
Dims{pad, input_dim}),
Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
Dims{cpu_seq->getSize()})},
{Tensor(cpu_out.getData(), Dims{batch_size, input_dim * context_length})},
{});
compare.getGpuFunction()->calc(
{Tensor(gpu_in.getData(), Dims{batch_size, input_dim}),
Tensor(gpu_weight ? gpu_weight->getData() : nullptr,
Dims{pad, input_dim}),
Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
Dims{gpu_seq->getSize()})},
{Tensor(gpu_out.getData(), Dims{batch_size, input_dim * context_length})},
{});
BufferArgs cpu_inputs;
BufferArgs cpu_outputs;
cpu_inputs.addArg(cpu_in, *cpu_seq);
if (cpu_weight) {
cpu_inputs.addArg(*cpu_weight, *cpu_seq);
}
cpu_outputs.addArg(cpu_out, *cpu_seq, ADD_TO);
compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs);
BufferArgs gpu_inputs;
BufferArgs gpu_outputs;
gpu_inputs.addArg(gpu_in, *gpu_seq);
if (gpu_weight) {
gpu_inputs.addArg(*gpu_weight, *gpu_seq);
}
gpu_outputs.addArg(gpu_out, *gpu_seq, ADD_TO);
compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs);
autotest::TensorCheckEqual(cpu_out, gpu_out);
}
......@@ -117,25 +120,23 @@ void testMatrixProjectionBackward(int context_start,
gpu_w_grad->copyFrom(*cpu_w_grad);
}
compare.getCpuFunction()->calc(
{Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}),
Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr,
Dims{pad, input_dim}),
Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
Dims{cpu_seq->getSize()})},
{Tensor(cpu_out_grad.getData(),
Dims{batch_size, input_dim * context_length})},
{});
compare.getGpuFunction()->calc(
{Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}),
Tensor(gpu_w_grad ? gpu_w_grad->getData() : nullptr,
Dims{pad, input_dim}),
Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
Dims{gpu_seq->getSize()})},
{Tensor(gpu_out_grad.getData(),
Dims{batch_size, input_dim * context_length})},
{});
BufferArgs cpu_inputs;
BufferArgs cpu_outputs;
cpu_inputs.addArg(cpu_out_grad, *cpu_seq);
cpu_outputs.addArg(cpu_in_grad, *cpu_seq, ADD_TO);
cpu_outputs.addArg(
cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO);
compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs);
BufferArgs gpu_inputs;
BufferArgs gpu_outputs;
gpu_inputs.addArg(gpu_out_grad, *gpu_seq);
gpu_outputs.addArg(gpu_in_grad, *gpu_seq, ADD_TO);
gpu_outputs.addArg(
gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO);
compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs);
autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad);
if (is_padding) {
......
paddle/function/Function.cpp
浏览文件 @ 0ae5ac16
......@@ -90,6 +90,12 @@ void BufferArgs::addArg(const GpuSparseMatrix& arg, ArgType argType) {
args_.push_back(std::make_shared<SparseMatrixArg>(arg, argType));
}
void BufferArgs::addArg(const Matrix& matrix,
const IVector& vector,
ArgType argType) {
args_.push_back(std::make_shared<SequenceArg>(matrix, vector, argType));
}
ClassRegistrar<FunctionBase> FunctionBase::funcRegistrar_;
} // namespace paddle
paddle/function/Function.h
浏览文件 @ 0ae5ac16
......@@ -77,6 +77,10 @@ public:
void addArg(const CpuSparseMatrix& arg, ArgType argType = UNSPECIFIED);
void addArg(const GpuSparseMatrix& arg, ArgType argType = UNSPECIFIED);
void addArg(const Matrix& matrix,
const IVector& vector,
ArgType argType = UNSPECIFIED);
// get argument
const BufferArg& operator[](size_t num) const {
CHECK_LT(num, args_.size());
......
paddle/function/FunctionTest.h
浏览文件 @ 0ae5ac16
......@@ -27,66 +27,28 @@ public:
gpu->init(config);
}
void cmpWithArg(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) {
void cmpWithArg(const BufferArgs& inputs,
const BufferArgs& outputs,
const BufferArgs& inouts) {
// init cpu and gpu arguments
auto initArgs = [=](
Arguments& cpuArgs, Arguments& gpuArgs, const Arguments& inArgs) {
for (const auto arg : inArgs) {
size_t size = sizeof(real);
for (const auto dim : arg.dims_) {
size *= dim;
}
if (arg.getData()) {
// todo(tianbing), waste unnecessary mem here
cpuMemory.emplace_back(std::make_shared<CpuMemoryHandle>(size));
gpuMemory.emplace_back(std::make_shared<GpuMemoryHandle>(size));
cpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_));
gpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_));
// already init outside
} else {
cpuMemory.emplace_back(std::make_shared<CpuMemoryHandle>(size));
gpuMemory.emplace_back(std::make_shared<GpuMemoryHandle>(size));
cpuArgs.emplace_back(
Tensor((real*)cpuMemory.back()->getBuf(), arg.dims_));
gpuArgs.emplace_back(
Tensor((real*)gpuMemory.back()->getBuf(), arg.dims_));
// will use an api to refactor this code.
CpuVector cpuVector(size / sizeof(real),
(real*)cpuArgs.back().getData());
GpuVector gpuVector(size / sizeof(real),
(real*)gpuArgs.back().getData());
cpuVector.uniform(0.001, 1);
gpuVector.copyFrom(cpuVector);
}
}
BufferArgs& cpuArgs, BufferArgs& gpuArgs, const BufferArgs& inArgs) {
/// leave it empty to pass the compile of ContextProjectionTest
/// Daoyuan is working on FunctionTest
/// and I will further merge with it
};
initArgs(cpuInputs, gpuInputs, inputs);
initArgs(cpuOutputs, gpuOutputs, outputs);
initArgs(cpuInouts, gpuInouts, inouts);
// function calculate
cpu->calc(cpuInputs, cpuOutputs, cpuInouts);
gpu->calc(gpuInputs, gpuOutputs, gpuInouts);
cpu->calc(cpuInputs, cpuOutputs);
gpu->calc(gpuInputs, gpuOutputs);
// check outputs and inouts
auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) {
for (size_t i = 0; i < cpuArgs.size(); i++) {
auto cpu = cpuArgs[i];
auto gpu = gpuArgs[i];
size_t size = 1;
for (auto dim : cpu.dims_) {
size *= dim;
}
CpuVector cpuVector(size, (real*)cpu.getData());
GpuVector gpuVector(size, (real*)gpu.getData());
autotest::TensorCheckErr(cpuVector, gpuVector);
}
auto checkArgs = [=](const BufferArgs& cpuArgs, const BufferArgs& gpuArgs) {
/// leave it open
};
checkArgs(cpuOutputs, gpuOutputs);
checkArgs(cpuInouts, gpuInouts);
}
std::shared_ptr<FunctionBase> getCpuFunction() const { return cpu; }
......@@ -98,12 +60,12 @@ protected:
std::shared_ptr<FunctionBase> gpu;
std::vector<CpuMemHandlePtr> cpuMemory;
std::vector<GpuMemHandlePtr> gpuMemory;
Arguments cpuInputs;
Arguments cpuOutputs;
Arguments cpuInouts;
Arguments gpuInputs;
Arguments gpuOutputs;
Arguments gpuInouts;
BufferArgs cpuInputs;
BufferArgs cpuOutputs;
BufferArgs cpuInouts;
BufferArgs gpuInputs;
BufferArgs gpuOutputs;
BufferArgs gpuInouts;
};
} // namespace paddle
paddle/gserver/layers/ContextProjection.cpp
浏览文件 @ 0ae5ac16
......@@ -118,16 +118,15 @@ void ContextProjection::forward() {
/// first use state_, otherwise use weight_(padding false === w nullptr)
auto w_ptr =
state_ ? state_.get() : is_padding ? weight_->getW().get() : nullptr;
auto start_pos = in_->sequenceStartPositions;
const auto start_pos = in_->sequenceStartPositions->getVector(useGpu_);
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*in_->value);
inputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr,
w_ptr ? w_ptr->getHeight() : 0,
input_dim));
inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_));
outputs.addArg(*out_->value, ADD_TO);
inputs.addArg(*in_->value, *start_pos);
if (w_ptr) {
inputs.addArg(CpuMatrix(w_ptr->getData(), w_ptr->getHeight(), input_dim),
*start_pos);
}
outputs.addArg(*out_->value, *start_pos, ADD_TO);
forward_[0]->calc(inputs, outputs);
if (state_ && config_.context_start() < 0) {
......@@ -166,13 +165,16 @@ void ContextProjection::backward(const UpdateCallback& callback) {
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(CpuMatrix(
in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim));
inputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr,
inputs.addArg(*out_->grad, *in_->sequenceStartPositions->getVector(useGpu_));
outputs.addArg(
CpuMatrix(
in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim),
*in_->sequenceStartPositions->getVector(useGpu_),
ADD_TO);
outputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr,
w_ptr ? w_ptr->getHeight() : 0,
input_dim));
inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_));
outputs.addArg(*out_->grad, ADD_TO);
input_dim),
ADD_TO);
backward_[0]->calc(inputs, outputs);
if (config_.trainable_padding()) {
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或