583f2bdd13
BOMs barf on UTF-8 decoding within PyTorch:
File "D:\Soft\Miniconda3\lib\site-packages\torch\utils_cpp_extension_versioner.py", line 16, in hash_source_files
hash_value = update_hash(hash_value, file.read())
UnicodeDecodeError: 'gbk' codec can't decode byte 0xbf in position 2: illegal multibyte sequence
Should fix #10, #14
107 lines
4.9 KiB
C++
107 lines
4.9 KiB
C++
// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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//
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// NVIDIA CORPORATION and its licensors retain all intellectual property
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// and proprietary rights in and to this software, related documentation
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// and any modifications thereto. Any use, reproduction, disclosure or
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// distribution of this software and related documentation without an express
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// license agreement from NVIDIA CORPORATION is strictly prohibited.
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#include <torch/extension.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include "upfirdn2d.h"
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//------------------------------------------------------------------------
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static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
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{
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// Validate arguments.
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TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
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TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
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TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
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TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
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TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
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TORCH_CHECK(x.numel() > 0, "x has zero size");
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TORCH_CHECK(f.numel() > 0, "f has zero size");
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TORCH_CHECK(x.dim() == 4, "x must be rank 4");
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TORCH_CHECK(f.dim() == 2, "f must be rank 2");
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TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large");
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TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
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TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
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TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
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// Create output tensor.
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const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
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int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
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int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
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TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
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torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
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TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
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TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large");
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// Initialize CUDA kernel parameters.
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upfirdn2d_kernel_params p;
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p.x = x.data_ptr();
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p.f = f.data_ptr<float>();
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p.y = y.data_ptr();
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p.up = make_int2(upx, upy);
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p.down = make_int2(downx, downy);
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p.pad0 = make_int2(padx0, pady0);
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p.flip = (flip) ? 1 : 0;
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p.gain = gain;
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p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
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p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
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p.filterSize = make_int2((int)f.size(1), (int)f.size(0));
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p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0));
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p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
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p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
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p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
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p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1;
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// Choose CUDA kernel.
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upfirdn2d_kernel_spec spec;
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AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
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{
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spec = choose_upfirdn2d_kernel<scalar_t>(p);
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});
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// Set looping options.
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p.loopMajor = (p.sizeMajor - 1) / 16384 + 1;
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p.loopMinor = spec.loopMinor;
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p.loopX = spec.loopX;
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p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1;
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p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1;
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// Compute grid size.
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dim3 blockSize, gridSize;
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if (spec.tileOutW < 0) // large
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{
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blockSize = dim3(4, 32, 1);
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gridSize = dim3(
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((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
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(p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
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p.launchMajor);
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}
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else // small
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{
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blockSize = dim3(256, 1, 1);
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gridSize = dim3(
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((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
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(p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
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p.launchMajor);
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}
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// Launch CUDA kernel.
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void* args[] = {&p};
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AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
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return y;
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}
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//------------------------------------------------------------------------
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
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{
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m.def("upfirdn2d", &upfirdn2d);
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}
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//------------------------------------------------------------------------
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