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#include <algorithm>
#include <cub/cub.cuh>
#include "caffe2/utils/cub_namespace.cuh"
#include "caffe2/core/context_gpu.h"
#include "caffe2/operators/sequence_ops.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
namespace caffe2 {
namespace {
template <typename T>
__global__ void AddPaddingKernel(
const T* in,
int block_size,
int lengths_size,
int outer_size,
const int32_t* lengths_prefix_sum,
const T* padding_start_ptr,
int start_padding_width_blocks,
const T* padding_end_ptr,
int end_padding_width_blocks,
T* out,
int32_t* lengths_out) {
int element_idx = blockIdx.x;
int prior_padding =
element_idx * (start_padding_width_blocks + end_padding_width_blocks);
int out_start_idx = element_idx == 0
? 0
: lengths_prefix_sum[element_idx - 1] + prior_padding;
int len_blocks;
int in_start_idx;
if (lengths_prefix_sum) {
len_blocks = lengths_prefix_sum[element_idx] -
(element_idx == 0 ? 0 : lengths_prefix_sum[element_idx - 1]);
in_start_idx = lengths_prefix_sum[element_idx] - len_blocks;
} else {
// Only one element, use the outer size
CUDA_KERNEL_ASSERT(lengths_size == 1);
len_blocks = outer_size;
in_start_idx = 0;
}
out_start_idx *= block_size;
in_start_idx *= block_size;
int len = len_blocks * block_size;
int start_padding_width = start_padding_width_blocks * block_size;
int end_padding_width = end_padding_width_blocks * block_size;
// start pad
T* out_ptr = out + out_start_idx;
for (int i = threadIdx.x; i < start_padding_width; i += blockDim.x) {
T fill = padding_start_ptr ? padding_start_ptr[i % block_size] : T(0);
out_ptr[i] = fill;
}
// payload
for (int i = threadIdx.x; i < len; i += blockDim.x) {
out_ptr[i + start_padding_width] = in[in_start_idx + i];
}
// end pad
for (int i = threadIdx.x; i < end_padding_width; i += blockDim.x) {
T fill = padding_end_ptr ? padding_end_ptr[i % block_size] : T(0);
out_ptr[i + start_padding_width + len] = fill;
}
// update the lengths
if (threadIdx.x == 0 && lengths_out != nullptr) {
lengths_out[element_idx] =
len_blocks + start_padding_width_blocks + end_padding_width_blocks;
}
}
template <typename T>
__global__ void RemovePaddingKernel(
const T* in,
int block_size,
int lengths_size,
int outer_size,
const int32_t* lengths_prefix_sum,
int start_padding_width_blocks,
int end_padding_width_blocks,
T* out,
int32_t* lengths_out) {
int element_idx = blockIdx.x;
int prior_padding =
element_idx * (start_padding_width_blocks + end_padding_width_blocks);
int out_start_idx = element_idx == 0
? 0
: lengths_prefix_sum[element_idx - 1] - prior_padding;
int len_blocks;
int in_start_idx;
if (lengths_prefix_sum) {
len_blocks = lengths_prefix_sum[element_idx] -
(element_idx == 0 ? 0 : lengths_prefix_sum[element_idx - 1]);
in_start_idx = lengths_prefix_sum[element_idx] - len_blocks;
} else {
// Only one element, use the outer size
CUDA_KERNEL_ASSERT(lengths_size == 1);
len_blocks = outer_size;
in_start_idx = 0;
}
out_start_idx *= block_size;
in_start_idx *= block_size;
int len = len_blocks * block_size;
int start_padding_width = start_padding_width_blocks * block_size;
// payload
T* out_ptr = out + out_start_idx;
for (int i = threadIdx.x; i < len; i += blockDim.x) {
out_ptr[in_start_idx + i] = in[i + start_padding_width];
}
// update the lengths
if (threadIdx.x == 0 && lengths_out != nullptr) {
lengths_out[element_idx] =
len_blocks - (start_padding_width_blocks + end_padding_width_blocks);
}
}
template <bool Inclusive = true>
void lengths_prefix_sum(
const int32_t* lengths,
int32_t num_items,
Tensor* prefix_buffer,
Tensor* prefix_sum,
CUDAContext* context) {
// Retrieve buffer size
size_t temp_storage_bytes = 0;
prefix_sum->Resize(num_items);
if (Inclusive) {
cub::DeviceScan::InclusiveSum(
NULL,
temp_storage_bytes,
lengths,
prefix_sum->template mutable_data<int32_t>(),
num_items,
context->cuda_stream());
} else {
cub::DeviceScan::ExclusiveSum(
NULL,
temp_storage_bytes,
lengths,
prefix_sum->template mutable_data<int32_t>(),
num_items,
context->cuda_stream());
}
// Allocate temporary storage
auto buffer_size = (temp_storage_bytes + sizeof(int32_t)) / sizeof(int32_t);
prefix_buffer->Resize(buffer_size);
void* d_temp_storage =
static_cast<void*>(prefix_buffer->template mutable_data<int32_t>());
if (Inclusive) {
cub::DeviceScan::InclusiveSum(
d_temp_storage,
temp_storage_bytes,
lengths,
prefix_sum->template mutable_data<int32_t>(),
num_items,
context->cuda_stream());
} else {
cub::DeviceScan::ExclusiveSum(
d_temp_storage,
temp_storage_bytes,
lengths,
prefix_sum->template mutable_data<int32_t>(),
num_items,
context->cuda_stream());
}
}
} // namespace
template <>
template <typename T>
bool AddPaddingOp<CUDAContext>::MakePadding(
const T* in_ptr,
T* out_ptr,
const int32_t* lengths_ptr,
int32_t lengths_size,
int32_t outer_size,
const T* padding_start_ptr,
const T* padding_end_ptr,
int64_t block_size) {
// Step 1: compute prefix sum over the lengths -- unless
// there were no lengths given, i.e there is only one segment
const int32_t* lengths_prefix_sum_ptr = nullptr;
if (lengths_ptr != nullptr) {
lengths_prefix_sum(
lengths_ptr,
lengths_size,
&lengths_prefix_sum_buffer_,
&lengths_prefix_sum_,
&context_);
lengths_prefix_sum_ptr = lengths_prefix_sum_.data<int32_t>();
}
int32_t* lengths_out_ptr = nullptr;
if (OutputSize() > 1) {
auto* lengths_out = Output(1, {lengths_size}, at::dtype<int32_t>());
lengths_out_ptr = lengths_out->template mutable_data<int32_t>();
}
if (lengths_size == 0) {
return true;
}
// Compute the padding using the accumulated lengths
AddPaddingKernel<T>
<<<lengths_size, CAFFE_CUDA_NUM_THREADS, 0, context_.cuda_stream()>>>(
in_ptr,
block_size,
lengths_size,
outer_size,
lengths_prefix_sum_ptr,
padding_start_ptr,
startPaddingWidth_,
padding_end_ptr,
endPaddingWidth_,
out_ptr,
lengths_out_ptr);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
REGISTER_CUDA_OPERATOR(AddPadding, AddPaddingOp<CUDAContext>);
template <>
template <typename T>
bool RemovePaddingOp<CUDAContext>::DoRunWithType() {
const auto& in = Input(0);
CAFFE_ENFORCE_GE(in.dim(), 1);
const int32_t outer_size = in.sizes()[0];
const auto block_size = std::accumulate(
in.sizes().begin() + 1, in.sizes().end(), 1, std::multiplies<int64_t>());
// if no lengths is provided, assume it is a single full-span entry
const int32_t* lengths_ptr = nullptr;
int32_t lengths_size = 1;
if (InputSize() > 1) {
const auto& lengths = Input(1);
lengths_ptr = lengths.data<int32_t>();
lengths_size = lengths.numel();
}
auto out_dims = in.sizes().vec();
out_dims[0] -= (startPaddingWidth_ + endPaddingWidth_) * lengths_size;
auto* out = Output(0, out_dims, at::dtype<T>());
const auto* in_ptr = in.template data<T>();
auto* out_ptr = out->template mutable_data<T>();
// Step 1: compute prefix sum over the (padded) lengths -- unless
// there were no lengths given, i.e there is only one segment
const int32_t* lengths_prefix_sum_ptr = nullptr;
if (lengths_ptr != nullptr) {
lengths_prefix_sum(
lengths_ptr,
lengths_size,
&lengths_prefix_sum_buffer_,
&lengths_prefix_sum_,
&context_);
lengths_prefix_sum_ptr = lengths_prefix_sum_.data<int32_t>();
}
int32_t* lengths_out_ptr = nullptr;
if (OutputSize() > 1) {
auto* lengths_out = Output(1, {lengths_size}, at::dtype<int32_t>());
lengths_out_ptr = lengths_out->template mutable_data<int32_t>();
}
if (lengths_size == 0) {
return true;
}
// Compute the padding using the accumulated lengths
RemovePaddingKernel<T>
<<<lengths_size, CAFFE_CUDA_NUM_THREADS, 0, context_.cuda_stream()>>>(
in_ptr,
block_size,
lengths_size,
outer_size,
lengths_prefix_sum_ptr,
startPaddingWidth_,
endPaddingWidth_,
out_ptr,
lengths_out_ptr);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template <typename T>
__global__ void gather_padding_kernel(
const int K,
const int N,
const int Y0Width,
const int Y1Width,
const T* X,
const int* I,
const int* L,
T* Y0,
T* Y1) {
typedef cub::BlockReduce<float, CAFFE_CUDA_NUM_THREADS> BlockReduce;
__shared__ typename BlockReduce::TempStorage y0_tmp;
__shared__ typename BlockReduce::TempStorage y1_tmp;
for (int i = blockIdx.x; i < N; i += gridDim.x) {
T sum_1 = T(0);
T sum_2 = T(0);
for (int j = threadIdx.x; j < K * Y0Width; j += blockDim.x) {
const int j1 = j / Y0Width;
const int j2 = j % Y0Width;
const int idx1 = N * (L[j1] + j2);
sum_1 += X[idx1 + i];
}
for (int j = threadIdx.x; j < K * Y1Width; j += blockDim.x) {
const int j1 = j / Y1Width;
const int j2 = j % Y1Width;
const int idx1 = N * L[j1];
const int idx2 = idx1 + N * (I[j1] - Y1Width + j2);
sum_2 += X[idx2 + i];
}
sum_1 = BlockReduce(y0_tmp).Reduce(sum_1, cub::Sum());
sum_2 = BlockReduce(y1_tmp).Reduce(sum_2, cub::Sum());
if (threadIdx.x == 0) {
Y0[i] = sum_1;
Y0 != Y1 ? Y1[i] = sum_2 : Y0[i] = sum_1 + sum_2;
}
__syncthreads();
}
}
template <>
template <typename T>
void GatherPaddingOp<CUDAContext>::GatherPadding(
const int outer_size,
const int lengths_size,
const int block_size,
const int pad_width,
const T* in_ptr,
const int* lengths_ptr,
T* padding_start_ptr,
T* padding_end_ptr) {
if (lengths_size > 0) {
lengths_prefix_sum<false>(
lengths_ptr,
lengths_size,
&lengths_prefix_sum_buffer_,
&lengths_prefix_sum_,
&context_);
gather_padding_kernel<T>
<<<std::min(block_size, CAFFE_MAXIMUM_NUM_BLOCKS),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
lengths_size,
block_size,
startPaddingWidth_,
endPaddingWidth_,
in_ptr,
lengths_ptr,
lengths_prefix_sum_.template data<int>(),
padding_start_ptr,
padding_end_ptr);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
}
REGISTER_CUDA_OPERATOR(RemovePadding, RemovePaddingOp<CUDAContext>);
REGISTER_CUDA_OPERATOR(GatherPadding, GatherPaddingOp<CUDAContext>);
} // namespace caffe2
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