199 lines
9.2 KiB
Python
199 lines
9.2 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 replacement for `torch.nn.functional.conv2d` that supports
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arbitrarily high order gradients with zero performance penalty."""
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import contextlib
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import torch
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# pylint: disable=redefined-builtin
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# pylint: disable=arguments-differ
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# pylint: disable=protected-access
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#----------------------------------------------------------------------------
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enabled = False # Enable the custom op by setting this to true.
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weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights.
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@contextlib.contextmanager
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def no_weight_gradients(disable=True):
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global weight_gradients_disabled
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old = weight_gradients_disabled
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if disable:
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weight_gradients_disabled = True
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yield
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weight_gradients_disabled = old
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#----------------------------------------------------------------------------
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def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
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if _should_use_custom_op(input):
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return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
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return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
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def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
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if _should_use_custom_op(input):
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return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
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return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
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#----------------------------------------------------------------------------
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def _should_use_custom_op(input):
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assert isinstance(input, torch.Tensor)
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if (not enabled) or (not torch.backends.cudnn.enabled):
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return False
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if input.device.type != 'cuda':
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return False
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return True
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def _tuple_of_ints(xs, ndim):
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xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
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assert len(xs) == ndim
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assert all(isinstance(x, int) for x in xs)
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return xs
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#----------------------------------------------------------------------------
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_conv2d_gradfix_cache = dict()
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_null_tensor = torch.empty([0])
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def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
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# Parse arguments.
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ndim = 2
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weight_shape = tuple(weight_shape)
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stride = _tuple_of_ints(stride, ndim)
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padding = _tuple_of_ints(padding, ndim)
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output_padding = _tuple_of_ints(output_padding, ndim)
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dilation = _tuple_of_ints(dilation, ndim)
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# Lookup from cache.
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key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
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if key in _conv2d_gradfix_cache:
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return _conv2d_gradfix_cache[key]
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# Validate arguments.
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assert groups >= 1
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assert len(weight_shape) == ndim + 2
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assert all(stride[i] >= 1 for i in range(ndim))
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assert all(padding[i] >= 0 for i in range(ndim))
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assert all(dilation[i] >= 0 for i in range(ndim))
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if not transpose:
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assert all(output_padding[i] == 0 for i in range(ndim))
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else: # transpose
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assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
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# Helpers.
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common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
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def calc_output_padding(input_shape, output_shape):
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if transpose:
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return [0, 0]
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return [
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input_shape[i + 2]
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- (output_shape[i + 2] - 1) * stride[i]
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- (1 - 2 * padding[i])
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- dilation[i] * (weight_shape[i + 2] - 1)
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for i in range(ndim)
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]
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# Forward & backward.
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class Conv2d(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, weight, bias):
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assert weight.shape == weight_shape
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ctx.save_for_backward(
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input if weight.requires_grad else _null_tensor,
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weight if input.requires_grad else _null_tensor,
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)
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ctx.input_shape = input.shape
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# Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
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if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0):
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a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1])
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b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1)
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c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2)
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c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1)
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c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
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return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
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# General case => cuDNN.
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if transpose:
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return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
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return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
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@staticmethod
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def backward(ctx, grad_output):
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input, weight = ctx.saved_tensors
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input_shape = ctx.input_shape
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grad_input = None
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grad_weight = None
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grad_bias = None
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if ctx.needs_input_grad[0]:
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p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape)
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op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
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grad_input = op.apply(grad_output, weight, None)
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assert grad_input.shape == input_shape
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if ctx.needs_input_grad[1] and not weight_gradients_disabled:
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grad_weight = Conv2dGradWeight.apply(grad_output, input)
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assert grad_weight.shape == weight_shape
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if ctx.needs_input_grad[2]:
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grad_bias = grad_output.sum([0, 2, 3])
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return grad_input, grad_weight, grad_bias
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# Gradient with respect to the weights.
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class Conv2dGradWeight(torch.autograd.Function):
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@staticmethod
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def forward(ctx, grad_output, input):
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ctx.save_for_backward(
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grad_output if input.requires_grad else _null_tensor,
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input if grad_output.requires_grad else _null_tensor,
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)
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ctx.grad_output_shape = grad_output.shape
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ctx.input_shape = input.shape
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# Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
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if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0):
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a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
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b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
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c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape)
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return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
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# General case => cuDNN.
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name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight'
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flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
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return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
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@staticmethod
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def backward(ctx, grad2_grad_weight):
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grad_output, input = ctx.saved_tensors
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grad_output_shape = ctx.grad_output_shape
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input_shape = ctx.input_shape
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grad2_grad_output = None
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grad2_input = None
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if ctx.needs_input_grad[0]:
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grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
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assert grad2_grad_output.shape == grad_output_shape
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if ctx.needs_input_grad[1]:
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p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape)
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op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
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grad2_input = op.apply(grad_output, grad2_grad_weight, None)
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assert grad2_input.shape == input_shape
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return grad2_grad_output, grad2_input
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_conv2d_gradfix_cache[key] = Conv2d
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return Conv2d
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#----------------------------------------------------------------------------
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