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| *.pyc | ||
| __pycache__/ | ||
| */__pycache__/ | ||
| alias_free_cuda/build/ | ||
| exp/ | ||
| tmp/ |
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| # Copyright (c) 2024 NVIDIA CORPORATION. | ||
| # Licensed under the MIT license. | ||
|
|
||
| import torch | ||
| import torch.nn as nn | ||
| from alias_free_torch.resample import UpSample1d, DownSample1d | ||
| # load fused CUDA kernel: this enables importing anti_alias_activation_cuda | ||
| from alias_free_cuda import load | ||
| load.load() | ||
|
|
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| class FusedAntiAliasActivation(torch.autograd.Function): | ||
| """ | ||
| Assumes filter size 12, replication padding on upsampling, and logscale alpha/beta parameters as inputs | ||
| """ | ||
| @staticmethod | ||
| def forward(ctx, inputs, ftr, alpha, beta): | ||
| import anti_alias_activation_cuda | ||
| activation_results = anti_alias_activation_cuda.forward(inputs, ftr, alpha, beta) | ||
| return activation_results | ||
|
|
||
| @staticmethod | ||
| def backward(ctx, output_grads): | ||
| # TODO: implement bwd pass | ||
| raise NotImplementedError | ||
| return output_grads, None, None | ||
|
|
||
| class Activation1d(nn.Module): | ||
| def __init__(self, | ||
| activation, | ||
| up_ratio: int = 2, | ||
| down_ratio: int = 2, | ||
| up_kernel_size: int = 12, | ||
| down_kernel_size: int = 12, | ||
| fused: bool = True | ||
| ): | ||
| super().__init__() | ||
| self.up_ratio = up_ratio | ||
| self.down_ratio = down_ratio | ||
| self.act = activation | ||
| self.upsample = UpSample1d(up_ratio, up_kernel_size) | ||
| self.downsample = DownSample1d(down_ratio, down_kernel_size) | ||
|
|
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| self.fused = fused # whether to use fused CUDA kernel or not | ||
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| def forward(self, x): | ||
| if not self.fused: | ||
| x = self.upsample(x) | ||
| x = self.act(x) | ||
| x = self.downsample(x) | ||
| return x | ||
| else: | ||
| if self.act.__class__.__name__ == "Snake": | ||
| beta = self.act.alpha.data # snake uses same params for alpha and beta | ||
| else: | ||
| beta = self.act.beta.data # snakebeta uses different params for alpha and beta | ||
| alpha = self.act.alpha.data | ||
| if not self.act.alpha_logscale: # exp baked into cuda kernel, cancel it out with a log | ||
| alpha = torch.log(alpha) | ||
| beta = torch.log(beta) | ||
| x = FusedAntiAliasActivation.apply(x, self.upsample.filter, alpha, beta) | ||
| x = self.downsample(x) | ||
| return x |
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| /* coding=utf-8 | ||
| * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
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| #include <cuda_fp16.h> | ||
| #include <torch/extension.h> | ||
| #include <vector> | ||
|
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| namespace anti_alias_activation { | ||
|
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| torch::Tensor fwd_cuda(torch::Tensor const& input, | ||
| torch::Tensor const& filter, | ||
| torch::Tensor const& alpha, | ||
| torch::Tensor const& beta | ||
| ); | ||
|
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| torch::Tensor fwd(torch::Tensor const& input, | ||
| torch::Tensor const& filter, | ||
| torch::Tensor const& alpha, | ||
| torch::Tensor const& beta | ||
| ) { | ||
| AT_ASSERTM(input.dim() == 3, "expected 3D tensor"); | ||
| //AT_ASSERTM((input.scalar_type() == at::ScalarType::Half) || | ||
| // (input.scalar_type() == at::ScalarType::BFloat16), | ||
| // "Only fp16 and bf16 are supported"); | ||
|
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| return fwd_cuda(input, filter, alpha, beta); | ||
| } | ||
|
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| } // end namespace anti_alias_activation | ||
|
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| PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { | ||
| m.def("forward", | ||
| &anti_alias_activation::fwd, | ||
| "Anti Alias Activation -- Forward."); | ||
| } |
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