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| # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| """Custom PyTorch ops for efficient bias and activation.""" | |
| import os | |
| import warnings | |
| import numpy as np | |
| import torch | |
| import dnnlib | |
| import traceback | |
| from torch import nn | |
| from .. import custom_ops | |
| from .. import misc | |
| #---------------------------------------------------------------------------- | |
| activation_funcs = { | |
| 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), | |
| 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False), | |
| 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False), | |
| 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True), | |
| 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True), | |
| 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True), | |
| 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True), | |
| 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True), | |
| 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), | |
| } | |
| #---------------------------------------------------------------------------- | |
| _inited = False | |
| _plugin = None | |
| _null_tensor = torch.empty([0]) | |
| def _init(): | |
| global _inited, _plugin | |
| if not _inited: | |
| _inited = True | |
| sources = ['bias_act.cpp', 'bias_act.cu'] | |
| sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] | |
| try: | |
| _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) | |
| except: | |
| warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) | |
| return _plugin is not None | |
| #---------------------------------------------------------------------------- | |
| def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): | |
| r"""Fused bias and activation function. | |
| Adds bias `b` to activation tensor `x`, evaluates activation function `act`, | |
| and scales the result by `gain`. Each of the steps is optional. In most cases, | |
| the fused op is considerably more efficient than performing the same calculation | |
| using standard PyTorch ops. It supports first and second order gradients, | |
| but not third order gradients. | |
| Args: | |
| x: Input activation tensor. Can be of any shape. | |
| b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type | |
| as `x`. The shape must be known, and it must match the dimension of `x` | |
| corresponding to `dim`. | |
| dim: The dimension in `x` corresponding to the elements of `b`. | |
| The value of `dim` is ignored if `b` is not specified. | |
| act: Name of the activation function to evaluate, or `"linear"` to disable. | |
| Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. | |
| See `activation_funcs` for a full list. `None` is not allowed. | |
| alpha: Shape parameter for the activation function, or `None` to use the default. | |
| gain: Scaling factor for the output tensor, or `None` to use default. | |
| See `activation_funcs` for the default scaling of each activation function. | |
| If unsure, consider specifying 1. | |
| clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable | |
| the clamping (default). | |
| impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). | |
| Returns: | |
| Tensor of the same shape and datatype as `x`. | |
| """ | |
| assert isinstance(x, torch.Tensor) | |
| assert impl in ['ref', 'cuda'] | |
| if impl == 'cuda' and x.device.type == 'cuda' and _init(): | |
| return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) | |
| return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) | |
| class bias_act_relu(nn.Module): | |
| def __init__(self, bias, negative_slope=0.2, scale=2 ** 0.5): | |
| super().__init__() | |
| self.bias = nn.Parameter(torch.zeros(bias)) | |
| self.negative_slope = negative_slope | |
| self.scale = scale | |
| def forward(self, input): | |
| return bias_act(input, b=self.bias, alpha=self.negative_slope, gain=self.scale) | |
| #---------------------------------------------------------------------------- | |
| def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): | |
| """Slow reference implementation of `bias_act()` using standard TensorFlow ops. | |
| """ | |
| assert isinstance(x, torch.Tensor) | |
| assert clamp is None or clamp >= 0 | |
| spec = activation_funcs[act] | |
| alpha = float(alpha if alpha is not None else spec.def_alpha) | |
| gain = float(gain if gain is not None else spec.def_gain) | |
| clamp = float(clamp if clamp is not None else -1) | |
| # Add bias. | |
| if b is not None: | |
| assert isinstance(b, torch.Tensor) and b.ndim == 1 | |
| assert 0 <= dim < x.ndim | |
| assert b.shape[0] == x.shape[dim] | |
| x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) | |
| # Evaluate activation function. | |
| alpha = float(alpha) | |
| x = spec.func(x, alpha=alpha) | |
| # Scale by gain. | |
| gain = float(gain) | |
| if gain != 1: | |
| x = x * gain | |
| # Clamp. | |
| if clamp >= 0: | |
| x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type | |
| return x | |
| #---------------------------------------------------------------------------- | |
| _bias_act_cuda_cache = dict() | |
| def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): | |
| """Fast CUDA implementation of `bias_act()` using custom ops. | |
| """ | |
| # Parse arguments. | |
| assert clamp is None or clamp >= 0 | |
| spec = activation_funcs[act] | |
| alpha = float(alpha if alpha is not None else spec.def_alpha) | |
| gain = float(gain if gain is not None else spec.def_gain) | |
| clamp = float(clamp if clamp is not None else -1) | |
| # Lookup from cache. | |
| key = (dim, act, alpha, gain, clamp) | |
| if key in _bias_act_cuda_cache: | |
| return _bias_act_cuda_cache[key] | |
| # Forward op. | |
| class BiasActCuda(torch.autograd.Function): | |
| def forward(ctx, x, b): # pylint: disable=arguments-differ | |
| ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format | |
| x = x.contiguous(memory_format=ctx.memory_format) | |
| b = b.contiguous() if b is not None else _null_tensor | |
| y = x | |
| if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: | |
| y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) | |
| ctx.save_for_backward( | |
| x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, | |
| b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, | |
| y if 'y' in spec.ref else _null_tensor) | |
| return y | |
| def backward(ctx, dy): # pylint: disable=arguments-differ | |
| dy = dy.contiguous(memory_format=ctx.memory_format) | |
| x, b, y = ctx.saved_tensors | |
| dx = None | |
| db = None | |
| if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: | |
| dx = dy | |
| if act != 'linear' or gain != 1 or clamp >= 0: | |
| dx = BiasActCudaGrad.apply(dy, x, b, y) | |
| if ctx.needs_input_grad[1]: | |
| db = dx.sum([i for i in range(dx.ndim) if i != dim]) | |
| return dx, db | |
| # Backward op. | |
| class BiasActCudaGrad(torch.autograd.Function): | |
| def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ | |
| ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format | |
| dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) | |
| ctx.save_for_backward( | |
| dy if spec.has_2nd_grad else _null_tensor, | |
| x, b, y) | |
| return dx | |
| def backward(ctx, d_dx): # pylint: disable=arguments-differ | |
| d_dx = d_dx.contiguous(memory_format=ctx.memory_format) | |
| dy, x, b, y = ctx.saved_tensors | |
| d_dy = None | |
| d_x = None | |
| d_b = None | |
| d_y = None | |
| if ctx.needs_input_grad[0]: | |
| d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) | |
| if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): | |
| d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) | |
| if spec.has_2nd_grad and ctx.needs_input_grad[2]: | |
| d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) | |
| return d_dy, d_x, d_b, d_y | |
| # Add to cache. | |
| _bias_act_cuda_cache[key] = BiasActCuda | |
| return BiasActCuda | |
| #---------------------------------------------------------------------------- | |