210 lines
9.6 KiB
Python
210 lines
9.6 KiB
Python
# 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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"""Custom PyTorch ops for efficient bias and activation."""
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import os
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import numpy as np
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import torch
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import dnnlib
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from .. import custom_ops
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from .. import misc
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#----------------------------------------------------------------------------
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activation_funcs = {
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'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False),
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'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),
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'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),
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'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True),
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'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True),
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'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),
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'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),
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'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),
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'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),
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}
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#----------------------------------------------------------------------------
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_plugin = None
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_null_tensor = torch.empty([0])
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def _init():
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global _plugin
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if _plugin is None:
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_plugin = custom_ops.get_plugin(
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module_name='bias_act_plugin',
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sources=['bias_act.cpp', 'bias_act.cu'],
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headers=['bias_act.h'],
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source_dir=os.path.dirname(__file__),
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extra_cuda_cflags=['--use_fast_math'],
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)
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return True
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#----------------------------------------------------------------------------
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def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
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r"""Fused bias and activation function.
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Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
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and scales the result by `gain`. Each of the steps is optional. In most cases,
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the fused op is considerably more efficient than performing the same calculation
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using standard PyTorch ops. It supports first and second order gradients,
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but not third order gradients.
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Args:
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x: Input activation tensor. Can be of any shape.
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b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
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as `x`. The shape must be known, and it must match the dimension of `x`
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corresponding to `dim`.
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dim: The dimension in `x` corresponding to the elements of `b`.
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The value of `dim` is ignored if `b` is not specified.
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act: Name of the activation function to evaluate, or `"linear"` to disable.
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Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
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See `activation_funcs` for a full list. `None` is not allowed.
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alpha: Shape parameter for the activation function, or `None` to use the default.
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gain: Scaling factor for the output tensor, or `None` to use default.
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See `activation_funcs` for the default scaling of each activation function.
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If unsure, consider specifying 1.
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clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
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the clamping (default).
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impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
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Returns:
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Tensor of the same shape and datatype as `x`.
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"""
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assert isinstance(x, torch.Tensor)
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assert impl in ['ref', 'cuda']
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if impl == 'cuda' and x.device.type == 'cuda' and _init():
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return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
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return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
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#----------------------------------------------------------------------------
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@misc.profiled_function
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def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
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"""Slow reference implementation of `bias_act()` using standard TensorFlow ops.
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"""
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assert isinstance(x, torch.Tensor)
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assert clamp is None or clamp >= 0
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spec = activation_funcs[act]
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alpha = float(alpha if alpha is not None else spec.def_alpha)
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gain = float(gain if gain is not None else spec.def_gain)
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clamp = float(clamp if clamp is not None else -1)
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# Add bias.
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if b is not None:
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assert isinstance(b, torch.Tensor) and b.ndim == 1
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assert 0 <= dim < x.ndim
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assert b.shape[0] == x.shape[dim]
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x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
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# Evaluate activation function.
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alpha = float(alpha)
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x = spec.func(x, alpha=alpha)
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# Scale by gain.
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gain = float(gain)
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if gain != 1:
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x = x * gain
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# Clamp.
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if clamp >= 0:
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x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
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return x
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#----------------------------------------------------------------------------
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_bias_act_cuda_cache = dict()
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def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
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"""Fast CUDA implementation of `bias_act()` using custom ops.
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"""
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# Parse arguments.
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assert clamp is None or clamp >= 0
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spec = activation_funcs[act]
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alpha = float(alpha if alpha is not None else spec.def_alpha)
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gain = float(gain if gain is not None else spec.def_gain)
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clamp = float(clamp if clamp is not None else -1)
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# Lookup from cache.
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key = (dim, act, alpha, gain, clamp)
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if key in _bias_act_cuda_cache:
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return _bias_act_cuda_cache[key]
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# Forward op.
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class BiasActCuda(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, b): # pylint: disable=arguments-differ
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ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format
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x = x.contiguous(memory_format=ctx.memory_format)
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b = b.contiguous() if b is not None else _null_tensor
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y = x
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if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
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y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
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ctx.save_for_backward(
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x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
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b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
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y if 'y' in spec.ref else _null_tensor)
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return y
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@staticmethod
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def backward(ctx, dy): # pylint: disable=arguments-differ
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dy = dy.contiguous(memory_format=ctx.memory_format)
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x, b, y = ctx.saved_tensors
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dx = None
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db = None
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if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
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dx = dy
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if act != 'linear' or gain != 1 or clamp >= 0:
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dx = BiasActCudaGrad.apply(dy, x, b, y)
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if ctx.needs_input_grad[1]:
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db = dx.sum([i for i in range(dx.ndim) if i != dim])
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return dx, db
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# Backward op.
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class BiasActCudaGrad(torch.autograd.Function):
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@staticmethod
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def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
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ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format
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dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
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ctx.save_for_backward(
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dy if spec.has_2nd_grad else _null_tensor,
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x, b, y)
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return dx
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@staticmethod
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def backward(ctx, d_dx): # pylint: disable=arguments-differ
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d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
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dy, x, b, y = ctx.saved_tensors
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d_dy = None
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d_x = None
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d_b = None
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d_y = None
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if ctx.needs_input_grad[0]:
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d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
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if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
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d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
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if spec.has_2nd_grad and ctx.needs_input_grad[2]:
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d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
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return d_dy, d_x, d_b, d_y
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# Add to cache.
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_bias_act_cuda_cache[key] = BiasActCuda
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return BiasActCuda
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#----------------------------------------------------------------------------
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