# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Python interface for transpose extensions"""
from typing import Optional, Tuple, Union
import torch
import transformer_engine_extensions as tex
from ..constants import TE_DType


__all__ = ['fp8_cast_transpose_fused',
           'fp8_cast_transpose_bgrad_fused',
           'fp8_cast_transpose_bgrad_dgelu_fused',
           'fp8_transpose_bgrad_fused']


def fp8_cast_transpose_fused(
    inp: torch.Tensor,
    fp8_meta_tensor: tex.FP8TensorMeta,
    fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
    otype: tex.DType,
    cast_out: Optional[torch.Tensor] = None,
    transpose_out: Optional[torch.Tensor] = None,
    noop_flag: Optional[torch.Tensor] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor], None]:
    """Cast + Transpose with FP8 output"""

    return_outputs = False
    if transpose_out is None:
        transpose_out = torch.empty(
            inp.shape[1], inp.shape[0], device="cuda", dtype=torch.uint8
        )
        return_outputs = True
    if cast_out is None:
        cast_out = torch.empty_like(inp, dtype=torch.uint8)
        return_outputs = True

    if noop_flag is None:
        noop_flag = torch.Tensor()

    tex.fused_cast_transpose_noop(
        inp,
        noop_flag,
        fp8_meta_tensor.scale[fp8_tensor],
        fp8_meta_tensor.amax_history[0][fp8_tensor],
        fp8_meta_tensor.scale_inv[fp8_tensor],
        cast_out,
        transpose_out,
        otype,
    )

    if return_outputs:
        return cast_out, transpose_out
    return None


def fp8_cast_transpose_bgrad_fused(
    inp: torch.Tensor,
    fp8_meta_tensor: tex.FP8TensorMeta,
    fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
    otype: tex.DType,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Cast + Transpose + BGRAD with FP8 output"""
    return tex.fused_cast_transpose_bgrad(
        inp,
        fp8_meta_tensor.scale[fp8_tensor],
        fp8_meta_tensor.amax_history[0][fp8_tensor],
        fp8_meta_tensor.scale_inv[fp8_tensor],
        otype,
    )


def fp8_transpose_bgrad_fused(
    inp: torch.Tensor,
    fp8_meta_tensor: tex.FP8TensorMeta,
    fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
    otype: tex.DType,
    grad_bias_type: torch.dtype,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Transpose + BGRAD with FP8 output"""
    return tex.fused_fp8_transpose_bgrad(
        inp,
        fp8_meta_tensor.scale[fp8_tensor],
        fp8_meta_tensor.amax_history[0][fp8_tensor],
        fp8_meta_tensor.scale_inv[fp8_tensor],
        otype,
        TE_DType[grad_bias_type],
    )


def fp8_cast_transpose_bgrad_dgelu_fused(
    grad_output: torch.Tensor,
    gelu_input: torch.Tensor,
    fp8_meta_tensor: tex.FP8TensorMeta,
    fp8_tensor: Union[tex.FP8FwdTensors, tex.FP8BwdTensors],
    otype: tex.DType,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Cast + Transpose + BGRAD + DGELU with FP8 output"""
    return tex.fused_cast_transpose_bgrad_dgelu(
        grad_output,
        gelu_input,
        fp8_meta_tensor.scale[fp8_tensor],
        fp8_meta_tensor.amax_history[0][fp8_tensor],
        fp8_meta_tensor.scale_inv[fp8_tensor],
        otype,
    )
