torch_utils/ops/fma.py

# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""

import torch

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


def fma(a, b, c):  # => a * b + c
    return _FusedMultiplyAdd.apply(a, b, c)


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


class _FusedMultiplyAdd(torch.autograd.Function):  # a * b + c
    @staticmethod
    def forward(ctx, a, b, c):  # pylint: disable=arguments-differ
        out = torch.addcmul(c, a, b)
        ctx.save_for_backward(a, b)
        ctx.c_shape = c.shape
        return out

    @staticmethod
    def backward(ctx, dout):  # pylint: disable=arguments-differ
        a, b = ctx.saved_tensors
        c_shape = ctx.c_shape
        da = None
        db = None
        dc = None

        if ctx.needs_input_grad[0]:
            da = _unbroadcast(dout * b, a.shape)

        if ctx.needs_input_grad[1]:
            db = _unbroadcast(dout * a, b.shape)

        if ctx.needs_input_grad[2]:
            dc = _unbroadcast(dout, c_shape)

        return da, db, dc


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


def _unbroadcast(x, shape):
    extra_dims = x.ndim - len(shape)
    assert extra_dims >= 0
    dim = [
        i for i in range(x.ndim)
        if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)
    ]
    if len(dim):
        x = x.sum(dim=dim, keepdim=True)
    if extra_dims:
        x = x.reshape(-1, *x.shape[extra_dims + 1:])
    assert x.shape == shape
    return x


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