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Fast and generic implementation using OpenMP and CUDA #45

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Fast and generic implementation using OpenMP and CUDA #45

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221b32b
First commit
shikishima-TasakiLab Jun 12, 2021
c32b399
CPU-only support
shikishima-TasakiLab Jun 13, 2021
41a5fb0
Update README.md
shikishima-TasakiLab Jun 13, 2021
1140e73
Remove "AutoNonVariableTypeMode".
shikishima-TasakiLab Jun 27, 2021
684769a
Merge branch 'main' of https://github.com/shikishima-TasakiLab/Involu…
shikishima-TasakiLab Jun 27, 2021
4b036d3
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab Jun 29, 2021
ce9100f
Compatible with PyTorch 1.7.0 or later
shikishima-TasakiLab Jul 4, 2021
4dc6c8f
Update README.md
shikishima-TasakiLab Jul 4, 2021
57a6d6a
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab Jul 4, 2021
0c190bc
Fixed the value of CUDA_MAX_THREADS
shikishima-TasakiLab Jul 10, 2021
353c22a
Fixed auto-casting to float32 when using AMP.
shikishima-TasakiLab Jul 10, 2021
265c309
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab Jul 10, 2021
2319dea
Support fatbin.
shikishima-TasakiLab Jul 13, 2021
7734b83
Merge remote-tracking branch 'upstream/main' into main
shikishima-TasakiLab Jul 13, 2021
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27 changes: 0 additions & 27 deletions include/autocast.h
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2 changes: 1 addition & 1 deletion include/involution2d_cuda.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
namespace involution {
namespace cuda {

#define CUDA_MAX_THREADS 512u
#define CUDA_MAX_THREADS 1024u

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@d-li14
In your CuPy implementation, the maximum number of CUDA threads was set to 1024. However, when I experimented, my CuPy reimplementation did not work with 1024, so I set it to 512.

My CUDA implementation does work with 1024. However, when I experimented, I set it to 512 and forgot to change it back to 1024.

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@shikishima-TasakiLab
Thanks, but I have tried to change the maximum CUDA threads, and it seems the result is still similar.

#define CUDA_KERNEL_LOOP(i, n) \
for (int64_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x)
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23 changes: 21 additions & 2 deletions include/involution2d_wrapper.h
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
#pragma once

#include <ATen/core/dispatch/Dispatcher.h>
#include <ATen/autocast_mode.h>
#include <torch/csrc/autograd/custom_function.h>

#include "autocast.h"
#include "involution2d_cpu.h"

#ifdef USE_CUDA
Expand Down Expand Up @@ -34,7 +34,8 @@ at::Tensor involution2d_autocast(
const std::vector<int64_t>& dilation
) {
c10::impl::ExcludeDispatchKeyGuard no_autocast(c10::DispatchKey::Autocast);
return involution2d(autocast::_cast(at::kFloat, input), autocast::_cast(at::kFloat, weight), stride, padding, dilation)
auto exec_type = at::autocast::promote_type(at::kFloat, input, weight);
return involution2d(at::autocast::cached_cast(exec_type, input), at::autocast::cached_cast(exec_type, weight), stride, padding, dilation)
.to(input.scalar_type());
}

Expand Down Expand Up @@ -208,6 +209,24 @@ at::Tensor involution2d_autograd(
return Involution2dFunctionCUDA::apply(input, weight, kernel_size, stride, padding, dilation, groups)[0];
}

at::Tensor involution2d_autocast(
const torch::autograd::Variable& input,
const torch::autograd::Variable& weight,
const std::vector<int64_t>& kernel_size,
const std::vector<int64_t>& stride,
const std::vector<int64_t>& padding,
const std::vector<int64_t>& dilation,
const int64_t groups
) {
c10::impl::ExcludeDispatchKeyGuard no_autocast(c10::DispatchKey::Autocast);
auto exec_type = at::autocast::promote_type(at::kFloat, input, weight);
return involution2d_autograd(
at::autocast::cached_cast(exec_type, input),
at::autocast::cached_cast(exec_type, weight),
kernel_size, stride, padding, dilation, groups
);
}

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@csvance
Fixed CUDA implementation input to be full precision using Autocast.

} // namespace cuda

#endif
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8 changes: 4 additions & 4 deletions src/pytorch_wrapper.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -24,10 +24,6 @@ TORCH_LIBRARY_IMPL(involution, CUDA, m) {
}
#endif

// TORCH_LIBRARY_IMPL(involution, Autocast, m) {
// m.impl("involution2d", involution2d_autocast);
// }

TORCH_LIBRARY_IMPL(involution, AutogradCPU, m) {
m.impl("involution2d", involution::cpu::involution2d_autograd);
}
Expand All @@ -36,4 +32,8 @@ TORCH_LIBRARY_IMPL(involution, AutogradCPU, m) {
TORCH_LIBRARY_IMPL(involution, AutogradCUDA, m) {
m.impl("involution2d", involution::cuda::involution2d_autograd);
}

TORCH_LIBRARY_IMPL(involution, Autocast, m) {
m.impl("involution2d", involution::cuda::involution2d_autocast);
}
#endif