File size: 6,353 Bytes
2595c46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
#undef CUB_WRAPPED_NAMESPACE
#define CUB_WRAPPED_NAMESPACE megablocks
#include "new_replicate.h"
#include <cstdint>
#include <cub/cub.cuh>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAStream.h>
#define CUDA_CALL(code) \
do { \
cudaError_t status = code; \
std::string err = cudaGetErrorString(status); \
TORCH_CHECK(status == cudaSuccess, err); \
} while (0)
namespace megablocks {
namespace replicate {
template <typename T, int kThreadsPerBlock>
__global__ void __launch_bounds__(kThreadsPerBlock)
ReplicateForwardKernel(T * __restrict__ x,
int * __restrict__ bins,
T * __restrict__ out,
int columns) {
// Offset to this threadblocks batch.
//
// x is [batch_size, num_bins]
// out is [batch_size, columns]
// bins is [num_bins]
int batch_idx = blockIdx.y;
int num_bins = gridDim.x;
x += batch_idx * num_bins;
out += batch_idx * columns;
// Load the start/end for this bin.
int bin_idx = blockIdx.x;
int start = 0;
if (bin_idx > 0) start = __ldg(bins + bin_idx - 1);
int end = __ldg(bins + bin_idx);
// Load the value to replicate.
T value = __ldg((T*)x + bin_idx);
// Offset to this threadblocks bin and this threads
// offset within the bin.
int bin_offset = blockIdx.z * kThreadsPerBlock + threadIdx.x;
out += start + bin_offset;
// Replicate the value to the output.
//
// TODO(tgale): Vectorize these stores.
int num_elements = end - start;
const int kElementsPerLoop = gridDim.z * kThreadsPerBlock;
T *out_ptr = (T*)out;
for (; bin_offset < num_elements; num_elements -= kElementsPerLoop) {
*out_ptr = value;
out_ptr += kElementsPerLoop;
}
}
template <typename T>
cudaError_t ReplicateForward(T *x,
int batch_size,
int num_bins,
int *bins,
T *out,
int columns,
cudaStream_t stream) {
const int kThreadsPerBlock = 64;
dim3 block_dim(kThreadsPerBlock, 1, 1);
int group_size = std::ceil((float)columns / (num_bins * kThreadsPerBlock));
dim3 grid_dim(num_bins, batch_size, group_size);
ReplicateForwardKernel<T, kThreadsPerBlock><<<
grid_dim, block_dim, 0, stream>>>(x, bins, out, columns);
return cudaGetLastError();
}
void cub_segmented_reduce(torch::Tensor grad,
torch::Tensor bins,
torch::Tensor out,
cudaStream_t stream) {
// Append a zero to the bin boundaries for CUB.
torch::Tensor offsets = torch::empty(bins.numel() + 1, bins.options());
CUDA_CALL(cudaMemsetAsync(offsets.data_ptr<int>(),
0,
offsets.numel() * sizeof(int),
stream));
CUDA_CALL(cudaMemcpyAsync(offsets.data_ptr<int>() + 1,
bins.data_ptr<int>(),
bins.numel() * sizeof(int),
cudaMemcpyDeviceToDevice,
stream));
// Get temporary buffer size.
size_t scratchpad_bytes = 0;
CUDA_CALL(cub::DeviceSegmentedReduce::Sum(nullptr,
scratchpad_bytes,
grad.data_ptr<c10::Half>(),
out.data_ptr<c10::Half>(),
bins.numel(),
offsets.data_ptr<int>(),
offsets.data_ptr<int>() + 1,
stream));
// Allocate scratchpad.
auto options = torch::TensorOptions()
.dtype(torch::kInt8)
.device(grad.device());
torch::Tensor scratchpad = torch::empty(scratchpad_bytes, options);
// Run the kernel for each batch item.
for (int i = 0; i < grad.size(0); ++i) {
int num_bins = out.size(1);
int num_values = grad.size(1);
CUDA_CALL(cub::DeviceSegmentedReduce::Sum(scratchpad.data_ptr<int8_t>(),
scratchpad_bytes,
grad.data_ptr<c10::Half>() + i * num_values,
out.data_ptr<c10::Half>() + i * num_bins,
bins.numel(),
offsets.data_ptr<int>(),
offsets.data_ptr<int>() + 1,
stream));
}
}
} // namespace replicate
void replicate_forward(torch::Tensor x,
torch::Tensor bins,
torch::Tensor out) {
// Validate the inputs.
TORCH_CHECK(x.is_cuda());
TORCH_CHECK(x.ndimension() == 2);
TORCH_CHECK(x.scalar_type() == torch::kFloat16 ||
x.scalar_type() == torch::kInt16 ||
x.scalar_type() == torch::kInt32);
TORCH_CHECK(bins.is_cuda());
TORCH_CHECK(bins.ndimension() == 1);
TORCH_CHECK(bins.scalar_type() == torch::kInt);
TORCH_CHECK(out.is_cuda());
TORCH_CHECK(out.ndimension() == 2);
TORCH_CHECK(out.scalar_type() == x.scalar_type());
// Batch dimensions should match for input/output.
TORCH_CHECK(x.size(0) == out.size(0));
// One input for each bin (in each batch).
TORCH_CHECK(x.size(1) == bins.size(0));
// Exit early if there is no work to do.
if (out.numel() == 0) return;
switch (x.scalar_type()) {
case torch::kFloat16:
CUDA_CALL(replicate::ReplicateForward(x.data_ptr<c10::Half>(),
x.size(0),
x.size(1),
bins.data_ptr<int>(),
out.data_ptr<c10::Half>(),
out.size(1),
c10::cuda::getCurrentCUDAStream()));
return;
case torch::kInt32:
CUDA_CALL(replicate::ReplicateForward(x.data_ptr<int>(),
x.size(0),
x.size(1),
bins.data_ptr<int>(),
out.data_ptr<int>(),
out.size(1),
c10::cuda::getCurrentCUDAStream()));
return;
}
TORCH_CHECK(x.scalar_type() == torch::kInt16);
CUDA_CALL(replicate::ReplicateForward(x.data_ptr<short>(),
x.size(0),
x.size(1),
bins.data_ptr<int>(),
out.data_ptr<short>(),
out.size(1),
c10::cuda::getCurrentCUDAStream()));
}
void replicate_backward(torch::Tensor grad,
torch::Tensor bins,
torch::Tensor out) {
// Validate the inputs.
TORCH_CHECK(grad.is_cuda());
TORCH_CHECK(grad.ndimension() == 2);
TORCH_CHECK(grad.scalar_type() == torch::kFloat16);
TORCH_CHECK(bins.is_cuda());
TORCH_CHECK(bins.ndimension() == 1);
TORCH_CHECK(bins.scalar_type() == torch::kInt);
TORCH_CHECK(out.is_cuda());
TORCH_CHECK(out.ndimension() == 2);
TORCH_CHECK(out.scalar_type() == torch::kFloat16);
// Batch dimensions should match for input/output.
TORCH_CHECK(grad.size(0) == out.size(0));
// One output for each bin (in each batch).
TORCH_CHECK(out.size(1) == bins.size(0));
replicate::cub_segmented_reduce(grad, bins, out, c10::cuda::getCurrentCUDAStream());
}
} // namespace megablocks
#undef CUDA_CALL
#undef CUB_WRAPPED_NAMESPACE |