
"""Custom replacement for `torch.nn.functional.conv2d` that supports
arbitrarily high order gradients with zero performance penalty."""

import warnings
import contextlib
import torch
from distutils.version import LooseVersion

# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
# pylint: disable=protected-access

#----------------------------------------------------------------------------

enabled = False                     # Enable the custom op by setting this to true.
weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
old_version = LooseVersion(torch.__version__) < LooseVersion('1.11.0')

@contextlib.contextmanager
def no_weight_gradients():
    global weight_gradients_disabled
    old = weight_gradients_disabled
    weight_gradients_disabled = True
    yield
    weight_gradients_disabled = old

#----------------------------------------------------------------------------

def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
    if _should_use_custom_op(input):
        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)

def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
    if _should_use_custom_op(input):
        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)
    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)

#----------------------------------------------------------------------------

def _should_use_custom_op(input):
    assert isinstance(input, torch.Tensor)
    if (not enabled) or (not torch.backends.cudnn.enabled):
        return False
    if input.device.type != 'cuda':
        return False
    if LooseVersion(torch.__version__) >= LooseVersion('1.7.0'):
        return True
    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
    return False

def _tuple_of_ints(xs, ndim):
    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
    assert len(xs) == ndim
    assert all(isinstance(x, int) for x in xs)
    return xs

#----------------------------------------------------------------------------

_conv2d_gradfix_cache = dict()

def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
    # Parse arguments.
    ndim = 2
    weight_shape = tuple(weight_shape)
    stride = _tuple_of_ints(stride, ndim)
    padding = _tuple_of_ints(padding, ndim)
    output_padding = _tuple_of_ints(output_padding, ndim)
    dilation = _tuple_of_ints(dilation, ndim)

    # Lookup from cache.
    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
    if key in _conv2d_gradfix_cache:
        return _conv2d_gradfix_cache[key]

    # Validate arguments.
    assert groups >= 1
    assert len(weight_shape) == ndim + 2
    assert all(stride[i] >= 1 for i in range(ndim))
    assert all(padding[i] >= 0 for i in range(ndim))
    assert all(dilation[i] >= 0 for i in range(ndim))
    if not transpose:
        assert all(output_padding[i] == 0 for i in range(ndim))
    else: # transpose
        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))

    # Helpers.
    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
    def calc_output_padding(input_shape, output_shape):
        if transpose:
            return [0, 0]
        return [
            input_shape[i + 2]
            - (output_shape[i + 2] - 1) * stride[i]
            - (1 - 2 * padding[i])
            - dilation[i] * (weight_shape[i + 2] - 1)
            for i in range(ndim)
        ]

    # Forward & backward.
    class Conv2d(torch.autograd.Function):
        @staticmethod
        def forward(ctx, input, weight, bias):
            assert weight.shape == weight_shape
            if not transpose:
                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
            else: # transpose
                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
            ctx.save_for_backward(input, weight, bias)
            return output

        @staticmethod
        def backward(ctx, grad_output):
            input, weight, bias = ctx.saved_tensors
            grad_input = None
            grad_weight = None
            grad_bias = None

            if ctx.needs_input_grad[0]:
                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
                assert grad_input.shape == input.shape

            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
                grad_weight = Conv2dGradWeight.apply(grad_output, input, bias)
                assert grad_weight.shape == weight_shape

            if ctx.needs_input_grad[2]:
                grad_bias = grad_output.sum([0, 2, 3])

            return grad_input, grad_weight, grad_bias

    # Gradient with respect to the weights.
    class Conv2dGradWeight(torch.autograd.Function):
        @staticmethod
        def forward(ctx, grad_output, input, bias):
            if old_version:
                op = torch._C._jit_get_operation(
                    'aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
                flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic,
                         torch.backends.cudnn.allow_tf32]
                grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
            else:
                bias_shape = bias.shape if (bias is not None) else None
                empty_weight = torch.empty(weight_shape, dtype=input.dtype, layout=input.layout, device=input.device)
                grad_weight = torch.ops.aten.convolution_backward(grad_output, input, empty_weight, bias_sizes=bias_shape, stride=stride, padding=padding, dilation=dilation, transposed=transpose, output_padding=output_padding, groups=groups, output_mask=[0,1,0])[1]
                assert grad_weight.shape == weight_shape
                ctx.save_for_backward(grad_output, input)
            return grad_weight

        @staticmethod
        def backward(ctx, grad2_grad_weight):
            grad_output, input = ctx.saved_tensors
            grad2_grad_output = None
            grad2_input = None

            if ctx.needs_input_grad[0]:
                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
                assert grad2_grad_output.shape == grad_output.shape

            if ctx.needs_input_grad[1]:
                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
                assert grad2_input.shape == input.shape

            return grad2_grad_output, grad2_input

    _conv2d_gradfix_cache[key] = Conv2d
    return Conv2d

#----------------------------------------------------------------------------
