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提交 82bd82c1 编写于 作者: C chengduoZH
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follow comments and refine code

上级 00e596ed
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Showing with 88 addition and 75 deletion
paddle/fluid/operators/concat_op.h
浏览文件 @ 82bd82c1
......@@ -33,6 +33,7 @@ class ConcatKernel : public framework::OpKernel<T> {
auto place = ctx.GetPlace();
out->mutable_data<T>(place);
// TODO(zcd): Sometimes direct copies will be faster
std::vector<framework::Tensor> inputs(ins.size());
for (size_t j = 0; j < ins.size(); ++j) {
inputs[j] = *ins[j];
......@@ -51,6 +52,7 @@ class ConcatGradKernel : public framework::OpKernel<T> {
auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
// TODO(zcd): Sometimes direct copies will be faster
std::vector<framework::Tensor> outputs(outs.size());
for (size_t j = 0; j < outs.size(); ++j) {
outs[j]->mutable_data<T>(ctx.GetPlace());
......
paddle/fluid/operators/math/concat.cc
浏览文件 @ 82bd82c1
......@@ -19,7 +19,8 @@ namespace operators {
namespace math {
/*
* All tensors' dimension should be the same.
* All tensors' dimension should be the same and the values of
* each dimension are the same, except the axis dimension.
*/
template <typename T>
class ConcatFunctor<platform::CPUDeviceContext, T> {
......@@ -27,12 +28,9 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const std::vector<framework::Tensor>& input, const int axis,
framework::Tensor* output) {
// assume the the max size of input is less than 8 and see the performance
// save origin dim
// TODO(zcd): Add input data validity checking
int num = input.size();
std::vector<paddle::framework::DDim> origin_dim(num);
// get the matrix size
int rows = 1;
auto dim_0 = input[0].dims();
for (int i = 0; i < axis; ++i) {
......@@ -40,7 +38,6 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
}
int out_rows = rows, out_cols = 0;
// get input's cols
std::vector<int64_t> input_cols(input.size());
for (int i = 0; i < num; ++i) {
int t_cols = input[i].numel() / rows;
......@@ -64,18 +61,19 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
}
};
/*
* All tensors' dimension should be the same and the values of
* each dimension are the same, except the axis dimension.
*/
template <typename T>
class ConcatGradFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, const int axis,
std::vector<framework::Tensor>& outputs) {
// assume the the max size of input is less than 8 and see the performance
// save origin dim
// TODO(zcd): Add input data validity checking
int num = outputs.size();
std::vector<paddle::framework::DDim> origin_dim(num);
// get the matrix size
int input_rows = 1;
auto dim_0 = outputs[0].dims();
for (int i = 0; i < axis; ++i) {
......@@ -83,7 +81,6 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
}
int input_cols = 0;
// get outputs' cols
std::vector<int64_t> output_cols(outputs.size());
for (int i = 0; i < num; ++i) {
int t_cols = outputs[i].numel() / input_rows;
......
paddle/fluid/operators/math/concat.cu
浏览文件 @ 82bd82c1
......@@ -12,6 +12,7 @@ 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 "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/platform/cuda_helper.h"
......@@ -19,16 +20,6 @@ namespace paddle {
namespace operators {
namespace math {
// TODO(zcd): This can be replaced by tensor,
// if that, maybe we should add int8 to VarType::Type.
// Or replaced by tensorArray.
static constexpr int MaxSize = 8;
template <typename T>
struct CUDADeviceArray {
T data[MaxSize];
int size;
};
template <typename T>
__device__ T upper_bound(const T* first, T count, T val) {
const T* orig = first;
......@@ -49,25 +40,24 @@ __device__ T upper_bound(const T* first, T count, T val) {
}
template <typename T>
__global__ void KernelConcat(const CUDADeviceArray<const T*> inputs,
const CUDADeviceArray<int> input_cols,
__global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
const int output_rows, const int output_cols,
T* output) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
int segment = upper_bound<int>(input_cols.data, input_cols.size, tid_x) - 1;
int segment = upper_bound<int>(input_cols, col_size, tid_x) - 1;
int curr_offset = input_cols.data[segment];
int curr_offset = input_cols[segment];
int curr_segment = segment;
for (; tid_x < output_cols; tid_x += blockDim.x * gridDim.x) {
T curr_col_offset;
while ((curr_col_offset = input_cols.data[curr_segment + 1]) <= tid_x) {
while ((curr_col_offset = input_cols[curr_segment + 1]) <= tid_x) {
curr_offset = curr_col_offset;
++curr_segment;
}
int local_col = tid_x - curr_offset;
int segment_width = curr_col_offset - curr_offset;
const T* input_ptr = inputs.data[curr_segment];
T* input_ptr = inputs[curr_segment];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y)
output[tid_y * output_cols + tid_x] =
......@@ -76,41 +66,41 @@ __global__ void KernelConcat(const CUDADeviceArray<const T*> inputs,
}
template <typename T>
__global__ void KernelConcat(const CUDADeviceArray<const T*> inputs,
const int input_col, const int output_rows,
const int output_cols, T* output) {
__global__ void KernelConcat(T** inputs, const int input_col,
const int output_rows, const int output_cols,
T* output) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
float inv_input_col = 1.0 / input_col;
for (; tid_x < output_cols; tid_x += blockDim.x * gridDim.x) {
int split = tid_x * inv_input_col;
int in_offset = tid_x - split * input_col;
const T* input_ptr = inputs.data[split];
T* input_ptr = inputs[split];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y)
for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y) {
output[tid_y * output_cols + tid_x] =
input_ptr[tid_y * input_col + in_offset];
}
}
}
template <typename T>
__global__ void KernelConcatGrad(const T* input, const int input_row,
const int input_col,
CUDADeviceArray<int> output_cols,
CUDADeviceArray<T*> outputs) {
const int input_col, const int* output_cols,
int col_size, T** outputs) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
int segment = upper_bound<int>(output_cols.data, output_cols.size, tid_x) - 1;
int curr_offset = output_cols.data[segment];
int segment = upper_bound<int>(output_cols, col_size, tid_x) - 1;
int curr_offset = output_cols[segment];
int curr_segment = segment;
for (; tid_x < input_col; tid_x += blockDim.x * gridDim.x) {
T curr_col_offset;
while ((curr_col_offset = output_cols.data[curr_segment + 1]) <= tid_x) {
while ((curr_col_offset = output_cols[curr_segment + 1]) <= tid_x) {
curr_offset = curr_col_offset;
++curr_segment;
}
int local_col = tid_x - curr_offset;
int segment_width = curr_col_offset - curr_offset;
T* output_ptr = outputs.data[curr_segment];
T* output_ptr = outputs[curr_segment];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < input_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * segment_width + local_col] =
......@@ -121,13 +111,13 @@ __global__ void KernelConcatGrad(const T* input, const int input_row,
template <typename T>
__global__ void KernelConcatGrad(const T* input, const int input_row,
const int input_col, const int output_cols,
CUDADeviceArray<T*> outputs) {
T** outputs) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
float inv_input_col = 1.0 / input_col;
for (; tid_x < input_col; tid_x += blockDim.x * gridDim.x) {
int split = tid_x * inv_input_col;
int in_offset = tid_x - split * input_col;
T* output_ptr = outputs.data[split];
T* output_ptr = outputs[split];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < input_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * output_cols + in_offset] =
......@@ -136,7 +126,8 @@ __global__ void KernelConcatGrad(const T* input, const int input_row,
}
/*
* All tensors' dimension should be the same.
* All tensors' dimension should be the same and the values of
* each dimension are the same, except the axis dimension.
*/
template <typename T>
class ConcatFunctor<platform::CUDADeviceContext, T> {
......@@ -144,12 +135,8 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& context,
const std::vector<framework::Tensor>& input, const int axis,
framework::Tensor* output) {
// assume the the max size of input is less than 8 and see the performance
// save origin dim
// TODO(zcd): Add input data validity checking
int num = input.size();
PADDLE_ENFORCE_LT(num, MaxSize, "input number should be less than %d",
MaxSize);
// get the matrix size
int rows = 1;
auto dim_0 = input[0].dims();
for (int i = 0; i < axis; ++i) {
......@@ -157,25 +144,27 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
}
int cols = input[0].numel() / rows;
int out_rows = rows, out_cols = 0;
bool sameShape = true;
CUDADeviceArray<const T*> inputs_data;
CUDADeviceArray<int> inputs_cols;
inputs_data.size = num;
inputs_cols.size = num + 1;
inputs_cols.data[0] = 0;
// reshape to matrix
// check input shape is valid
paddle::framework::Vector<int16_t> inputs_data(num * sizeof(T*) / 2);
paddle::framework::Vector<int> inputs_cols(num + 1);
inputs_cols[0] = 0;
T** inputs_ptr = reinterpret_cast<T**>(inputs_data.data());
bool sameShape = true;
for (int i = 0; i < num; ++i) {
int t_cols = input[i].numel() / rows;
if (sameShape) {
if (t_cols != cols) sameShape = false;
}
out_cols += t_cols;
inputs_cols.data[i + 1] = out_cols;
inputs_data.data[i] = input[i].data<T>();
inputs_cols[i + 1] = out_cols;
inputs_ptr[i] = const_cast<T*>(input[i].data<T>());
}
T** ins_gpu =
reinterpret_cast<T**>(inputs_data.CUDAMutableData(context.GetPlace()));
const int* ins_col_gpu = inputs_cols.CUDAData(context.GetPlace());
// computation
// set the thread block and grid according to CurrentDeviceId
const int kThreadsPerBlock = 1024;
......@@ -198,27 +187,27 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
if (sameShape) {
KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
inputs_data, cols, out_rows, out_cols, output->data<T>());
ins_gpu, cols, out_rows, out_cols, output->data<T>());
} else {
KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
inputs_data, inputs_cols, out_rows, out_cols, output->data<T>());
ins_gpu, ins_col_gpu, static_cast<int>(inputs_cols.size()), out_rows,
out_cols, output->data<T>());
}
}
};
/*
* All tensors' dimension should be the same and the values of
* each dimension are the same, except the axis dimension.
*/
template <typename T>
class ConcatGradFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, const int axis,
std::vector<framework::Tensor>& outputs) {
// assume the the max size of input is less than 8 and see the performance
// save origin dim
// TODO(zcd): Add input data validity checking
int num = outputs.size();
PADDLE_ENFORCE_LT(num, MaxSize, "input number should be less than %d",
MaxSize);
// get the matrix size
int input_row = 1;
auto dim_0 = outputs[0].dims();
for (int i = 0; i < axis; ++i) {
......@@ -229,11 +218,10 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
int input_col = 0;
bool sameShape = true;
CUDADeviceArray<T*> outputs_data;
CUDADeviceArray<int> outputs_cols;
outputs_data.size = num;
outputs_cols.size = num + 1;
outputs_cols.data[0] = 0;
paddle::framework::Vector<int16_t> outputs_data(num * sizeof(T*) / 2);
paddle::framework::Vector<int> outputs_cols(num + 1);
outputs_cols[0] = 0;
T** outputs_ptr = reinterpret_cast<T**>(outputs_data.data());
for (int i = 0; i < num; ++i) {
int t_col = outputs[i].numel() / input_row;
......@@ -241,12 +229,16 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
if (t_col != output_col_0) sameShape = false;
}
input_col += t_col;
outputs_cols.data[i + 1] = input_col;
outputs_data.data[i] = outputs[i].data<T>();
outputs_cols[i + 1] = input_col;
outputs_ptr[i] = outputs[i].data<T>();
}
T** outs_gpu =
reinterpret_cast<T**>(outputs_data.CUDAMutableData(context.GetPlace()));
const int* outs_col_gpu = outputs_cols.CUDAData(context.GetPlace());
// computation
const int kThreadsPerBlock = 256;
const int kThreadsPerBlock = 1024;
int block_cols = std::min(input_col, kThreadsPerBlock);
int block_rows = std::max(kThreadsPerBlock / block_cols, 1);
dim3 block_size = dim3(block_cols, block_rows, 1);
......@@ -257,10 +249,11 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
if (sameShape) {
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
input.data<T>(), input_row, input_col, output_col_0, outputs_data);
input.data<T>(), input_row, input_col, output_col_0, outs_gpu);
} else {
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
input.data<T>(), input_row, input_col, outputs_cols, outputs_data);
input.data<T>(), input_row, input_col, outs_col_gpu,
static_cast<int>(outputs_cols.size()), outs_gpu);
}
}
};
......
paddle/fluid/operators/math/concat.h
浏览文件 @ 82bd82c1
......@@ -20,7 +20,16 @@ namespace operators {
namespace math {
/*
* \brief Concatenate the input tensors along the dimension axis.
* TODO(zcd): maybe it needs to be more detailed.
* Examples:
* Input[0] = [[1,2],[3,4]]
* Input[1] = [[5,6]]
* axis = 0
*
* Output = [[1,2],
* [3,4],
* [5,6]]
*/
template <typename DeviceContext, typename T>
class ConcatFunctor {
......@@ -30,6 +39,18 @@ class ConcatFunctor {
framework::Tensor* output);
};
/*
* \brief Split the input tensors along the dimension axis into outputs.
* TODO(zcd): maybe it needs to be more detailed.
* Examples:
* Input = [[1,2],
* [3,4],
* [5,6]]
* axis = 0
*
* Output[0] = [[1,2],[3,4]]
* Output[1] = [[5,6]]
*/
template <typename DeviceContext, typename T>
class ConcatGradFunctor {
public:
......
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