[NFC] polish pre-commit run --files colossalai/kernel/cuda_native/csrc/scaled_upper_triang_masked_softmax_cuda.cu code style (#943)

pull/997/head
HaoyuQin 2022-05-13 17:29:56 +08:00 committed by binmakeswell
parent 5bbefeb06a
commit c0f373db5d
1 changed files with 28 additions and 38 deletions

View File

@ -2,12 +2,13 @@
* with minor changes. */
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cuda_profiler_api.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <torch/extension.h>
#include "scaled_upper_triang_masked_softmax.h"
#include "type_shim.h"
@ -15,18 +16,15 @@ namespace multihead_attn {
namespace fused_softmax {
namespace scaled_upper_triang_masked_softmax {
torch::Tensor fwd_cuda(
torch::Tensor const& input,
float scale_factor)
{
torch::Tensor fwd_cuda(torch::Tensor const& input, float scale_factor) {
// input is a 3d tensor with dimensions [attn_batches, seq_len, seq_len]
const int attn_batches = input.size(0);
const int seq_len = input.size(1);
TORCH_INTERNAL_ASSERT(seq_len <= 2048);
// Output
// Output
auto act_options = input.options().requires_grad(false);
torch::Tensor softmax_results =
torch::Tensor softmax_results =
torch::empty({attn_batches, seq_len, seq_len}, act_options);
// Softmax Intermediate Result Ptr
@ -36,50 +34,42 @@ torch::Tensor fwd_cuda(
DISPATCH_HALF_AND_BFLOAT(
input.scalar_type(),
"dispatch_scaled_upper_triang_masked_softmax_forward",
dispatch_scaled_upper_triang_masked_softmax_forward<scalar_t, scalar_t, float>(
reinterpret_cast<scalar_t*>(softmax_results_ptr),
reinterpret_cast<const scalar_t*>(input_ptr),
scale_factor,
seq_len,
seq_len,
attn_batches);
);
dispatch_scaled_upper_triang_masked_softmax_forward<scalar_t, scalar_t,
float>(
reinterpret_cast<scalar_t*>(softmax_results_ptr),
reinterpret_cast<const scalar_t*>(input_ptr), scale_factor, seq_len,
seq_len, attn_batches););
return softmax_results;
}
torch::Tensor bwd_cuda(
torch::Tensor const& output_grads_,
torch::Tensor const& softmax_results_,
float scale_factor) {
torch::Tensor bwd_cuda(torch::Tensor const& output_grads_,
torch::Tensor const& softmax_results_,
float scale_factor) {
auto output_grads = output_grads_.contiguous();
auto softmax_results = softmax_results_.contiguous();
//output grads is a 3d tensor with dimensions [attn_batches, seq_len, seq_len]
// output grads is a 3d tensor with dimensions [attn_batches, seq_len,
// seq_len]
const int attn_batches = output_grads.size(0);
const int seq_len = output_grads.size(1);
TORCH_INTERNAL_ASSERT(output_grads.size(1) == output_grads.size(2));
void* output_grads_ptr = static_cast<void*>(output_grads.data_ptr());
//Softmax Grad
// Softmax Grad
DISPATCH_HALF_AND_BFLOAT(
output_grads_.scalar_type(),
"dispatch_scaled_upper_triang_masked_softmax_backward",
dispatch_scaled_upper_triang_masked_softmax_backward<scalar_t, scalar_t, float>(
reinterpret_cast<scalar_t*>(output_grads_ptr),
reinterpret_cast<scalar_t*>(output_grads_ptr),
reinterpret_cast<scalar_t const*>(softmax_results.data_ptr()),
scale_factor,
seq_len,
seq_len,
attn_batches);
);
//backward pass is completely in-place
dispatch_scaled_upper_triang_masked_softmax_backward<scalar_t, scalar_t,
float>(
reinterpret_cast<scalar_t*>(output_grads_ptr),
reinterpret_cast<scalar_t*>(output_grads_ptr),
reinterpret_cast<scalar_t const*>(softmax_results.data_ptr()),
scale_factor, seq_len, seq_len, attn_batches););
// backward pass is completely in-place
return output_grads;
}
}
}
}
} // namespace scaled_upper_triang_masked_softmax
} // namespace fused_softmax
} // namespace multihead_attn