# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Fusible operation for in-place add."""

from __future__ import annotations
from collections.abc import Iterable
from typing import Any, Optional

import torch

from transformer_engine.pytorch.ops.op import (
    BasicOperation,
    OperationContext,
)

from transformer_engine.pytorch.tensor import Quantizer


class AddInPlace(BasicOperation):
    """Add in-place

    This operation requires an extra tensor input to the operation
    fuser. The main input is added in-place to the extra input, and a
    view of the extra input is output.

    This operation is considered an advanced feature and most users
    are discouraged from using it. In-place operations break some
    autograd assumptions and they can result in subtle, esoteric bugs.

    Compare to `MakeExtraOutput`, which does a similar operation in
    the backward pass.

    """

    # Operation expects buffer for output tensor
    num_extra_inputs: int = 1

    def op_forward(self, *args, **kwargs) -> None:
        raise RuntimeError(
            "{self.__class__.__name__} operation has "
            f"{self.num_extra_inputs} extra tensor inputs "
            f"and {self.num_extra_outputs} extra tensor outputs. "
            "It overrides `fuser_forward` instead of `op_forward`."
        )

    def op_backward(self, *args, **kwargs) -> None:
        raise RuntimeError(
            "{self.__class__.__name__} operation has "
            f"{self.num_extra_inputs} extra tensor inputs "
            f"and {self.num_extra_outputs} extra tensor outputs. "
            "It overrides `fuser_backward` instead of `op_backward`."
        )

    def fuser_forward(
        self,
        basic_op_ctxs: list[OperationContext],
        input_: torch.Tensor,
        *,
        basic_op_extra_inputs: list[tuple[torch.Tensor, ...]],
        prev_op_grad_input_quantizer: Optional[Quantizer],
        next_op_input_quantizer: Optional[Quantizer],
        is_first_op: bool,
        basic_op_kwargs: list[dict[str, Any]],
    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
        output = basic_op_extra_inputs[0][0].detach()
        output += input_
        return output, [()]

    def fuser_backward(
        self,
        basic_op_ctxs: list[OperationContext],
        grad_output: torch.Tensor,
        *,
        basic_op_grad_extra_outputs: list[tuple[torch.Tensor, ...]],
    ) -> tuple[
        torch.Tensor,
        Iterable[Iterable[Optional[torch.Tensor]]],
        Iterable[Iterable[Optional[torch.Tensor]]],
    ]:
        return grad_output, [], [(grad_output,)]
