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#define CUB_IGNORE_DEPRECATED_API
#undef CUB_WRAPPED_NAMESPACE
#define CUB_WRAPPED_NAMESPACE megablocks
#include "new_cumsum.h"
#include "gpu_backend.h"
#include <cstdint>
namespace megablocks {
struct Inclusive {};
struct Exclusive {};
template <typename Type> struct Cumsum {
template<
typename InputIteratorT,
typename OutputIteratorT>
static void Run(void * d_temp_storage,
size_t & temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
megablocks::gpuStream_t stream = 0) {
GPU_CALL(cubns::DeviceScan::ExclusiveSum(d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
num_items,
stream));
}
};
template <> struct Cumsum<Inclusive> {
template<
typename InputIteratorT,
typename OutputIteratorT>
static void Run(void * d_temp_storage,
size_t & temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
megablocks::gpuStream_t stream = 0) {
GPU_CALL(cubns::DeviceScan::InclusiveSum(d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
num_items,
stream));
}
};
template <typename SumType, typename T>
void cub_cumsum(torch::Tensor x, int dim, torch::Tensor out) {
// Get temporary storage size.
size_t scratchpad_bytes = 0;
Cumsum<SumType>::Run(nullptr,
scratchpad_bytes,
x.data_ptr<T>(),
out.data_ptr<T>(),
x.size(1),
megablocks::get_current_stream());
// Allocate scratchpad.
//
// NOTE: We scale for the batch dimension so we can run in parallel.
auto options = torch::TensorOptions()
.dtype(torch::kInt8)
.device(x.device());
torch::Tensor scratchpad = torch::empty(scratchpad_bytes * x.size(0),
options);
// Run the kernel.
//
// NOTE: Using different streams for each issue does not appear to
// yield performance gains for our problem set. The overhead of
// event/stream synchronization appears to outweigh the benfits.
// We could write a true batched cumsum, but this would require
// significant code duplication from cub and we might move away
// from this formulation anyways.
for (int i = 0; i < x.size(0); ++i) {
void* scratchpad_ptr = (int8_t*)scratchpad.data_ptr() + scratchpad_bytes * i;
Cumsum<SumType>::Run(scratchpad_ptr,
scratchpad_bytes,
x.data_ptr<T>() + x.size(1) * i,
out.data_ptr<T>() + x.size(1) * i,
x.size(1),
megablocks::get_current_stream());
}
}
void exclusive_cumsum(torch::Tensor x, int dim, torch::Tensor out) {
// Validate the input matrix.
TORCH_CHECK(x.is_cuda());
TORCH_CHECK(x.ndimension() == 2);
TORCH_CHECK(x.scalar_type() == torch::kInt16 ||
x.scalar_type() == torch::kInt32 ||
x.scalar_type() == torch::kInt64);
TORCH_CHECK(out.is_cuda());
TORCH_CHECK(out.ndimension() == 2);
TORCH_CHECK(out.scalar_type() == x.scalar_type());
// NOTE: We currently only support contraction across the contiguous
// dimension in the matrix.
TORCH_CHECK(dim == 1);
if (x.scalar_type() == torch::kInt16) {
cub_cumsum<Exclusive, short>(x, dim, out);
return;
}
if (x.scalar_type() == torch::kInt32) {
cub_cumsum<Exclusive, int>(x, dim, out);
return;
}
TORCH_CHECK(x.scalar_type() == torch::kInt64);
cub_cumsum<Exclusive, long>(x, dim, out);
}
void inclusive_cumsum(torch::Tensor x, int dim, torch::Tensor out) {
// Validate the input matrix.
TORCH_CHECK(x.is_cuda());
TORCH_CHECK(x.ndimension() == 2);
TORCH_CHECK(x.scalar_type() == torch::kInt16 ||
x.scalar_type() == torch::kInt32 ||
x.scalar_type() == torch::kInt64);
TORCH_CHECK(out.is_cuda());
TORCH_CHECK(out.ndimension() == 2);
TORCH_CHECK(out.scalar_type() == x.scalar_type());
// NOTE: We currently only support contraction across the contiguous
// dimension in the matrix.
TORCH_CHECK(dim == 1);
if (x.scalar_type() == torch::kInt16) {
cub_cumsum<Inclusive, short>(x, dim, out);
return;
}
if (x.scalar_type() == torch::kInt32) {
cub_cumsum<Inclusive, int>(x, dim, out);
return;
}
TORCH_CHECK(x.scalar_type() == torch::kInt64);
cub_cumsum<Inclusive, long>(x, dim, out);
}
} // namespace megablocks
#undef CUB_WRAPPED_NAMESPACE
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