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

import dataclasses
import math
import torch
from typing import Optional, Protocol, Tuple
from references.quantize_scale_calc import scale_from_amax_tensor
from transformer_engine.pytorch.fp8 import blockwise_fp8_block_len

@dataclasses.dataclass()
class QuantizeResult:
    data: torch.Tensor
    scale: torch.Tensor
    data_t: Optional[torch.Tensor]
    scale_t: Optional[torch.Tensor]


@dataclasses.dataclass()
class CuBLASScaleMunger:

    def munge_scale_shapes_for_backend(
        self,
        unmunged: QuantizeResult,
        tile_shape: Tuple[int, int],
    ) -> QuantizeResult:
        """
        cuBLAS GEMMs requires 1x128 quantized tensors to be have scales transposed
        so that for an (M, N) tensor, the scales are (RoundUpDiv(N, 128), RoundUp(M, 4))

        For 128x128 quantized tensors, the GEMM expects (M, PadToAlign(RoundUpDivide(N, 128), 4))
        format. If RoundUpDivide(N, 128) is not divisible by 4, a transformation is required
        """

        def _pad_inner_to_align(s: torch.Tensor, transpose: bool) -> torch.Tensor:
            if transpose:
                s = s.transpose(-1, -2).contiguous()
            M, K = s.shape
            if K % 4 == 0:
                return s
            k_pad = 4 - (K % 4)
            return torch.nn.functional.pad(s, (0, k_pad), mode="constant", value=0).contiguous()

        s = _pad_inner_to_align(unmunged.scale, transpose=tile_shape[0] == 1)
        if unmunged.scale_t is None:
            s_t = None
        else:
            s_t = _pad_inner_to_align(unmunged.scale_t, transpose=tile_shape[0] == 1)
        return QuantizeResult(unmunged.data, s, unmunged.data_t, s_t)

    @classmethod
    def demunge_scale_shape_from_backend(
        cls,
        qtensor_shape: Tuple[int, int],
        scales: torch.Tensor,
        tile_shape: Tuple[int, int],
    ) -> torch.Tensor:
        """
        Inverse operation of munge_scale_shapes_for_backend
        """
        if tile_shape[0] != 1:
            # 2D block quantized tensor may need padding stripped off
            derived_scale_k_shape = math.ceil(qtensor_shape[1] / tile_shape[1])
        else:
            derived_scale_k_shape = qtensor_shape[0]
        M, K = scales.shape
        if derived_scale_k_shape != K:
            scales = scales[:, :derived_scale_k_shape].contiguous()
        if tile_shape[0] == 1:
            return scales.transpose(-1, -2).contiguous()
        else:
            return scales


@dataclasses.dataclass()
class BlockwiseQuantizerReference:
    """
    A reference QuantizeOp for subchannel/block hybrid quantization.

    Defers to ref GEMMs and quantizization formatting based on the backend.
    """

    def __init__(self) -> None:
        self.scale_munger = CuBLASScaleMunger()

    @classmethod
    def _quantize_square_block_tiling(
        cls,
        x: torch.Tensor,
        quant_dtype: torch.dtype,
        tile_len: int,
        *,
        return_transpose: bool,
        pow_2_scales: bool,
        eps: float,
    ) -> QuantizeResult:
        M, K = x.shape

        pad_m_k = [0, 0]
        if K % tile_len != 0:
            pad_m_k[1] = tile_len - (K % tile_len)
        if M % tile_len != 0:
            pad_m_k[0] = tile_len - (M % tile_len)

        unpadded_m, unpadded_k = M, K
        if pad_m_k[0] != 0 or pad_m_k[1] != 0:
            x = torch.nn.functional.pad(
                x, (0, pad_m_k[1], 0, pad_m_k[0]), mode="constant", value=0
            ).contiguous()
            M, K = x.shape

        x_tiled = x.reshape(M // tile_len, tile_len, K // tile_len, tile_len)
        amax_grid = (
            torch.abs(x_tiled.transpose(-3, -2))
            .reshape(M // tile_len, K // tile_len, tile_len**2)
            .amax(dim=-1)
        ).float()
        if quant_dtype == torch.int8:
            dtype_max = torch.iinfo(quant_dtype).max
        else:
            dtype_max = torch.finfo(quant_dtype).max

        scale, scale_inv, _ = scale_from_amax_tensor(
            x_dtype=x.dtype,
            amax=amax_grid,
            quant_dtype=quant_dtype,
            pow_2_scales=pow_2_scales,
            eps=eps,
        )
        qx = x_tiled * scale.reshape(M // tile_len, 1, K // tile_len, 1)
        qx = torch.clamp(qx, min=-dtype_max, max=dtype_max)
        if quant_dtype == torch.int8:
            positive_mask = qx >= 0
            negative_mask = ~positive_mask
            pos_part = torch.where(positive_mask, torch.floor(qx + 0.5), 0)
            neg_part = torch.where(negative_mask, torch.ceil(qx - 0.5), 0)
            qx = pos_part + neg_part
        qx = qx.to(dtype=quant_dtype)
        qx = qx.reshape(M, K)
        if unpadded_k != K or unpadded_m != M:
            qx = qx[:unpadded_m, :unpadded_k].contiguous()
        if return_transpose:
            # Valid because of square block sizes
            qx_t = qx.transpose(-1, -2).contiguous()
            scale_inv_t = scale_inv.transpose(-1, -2).contiguous()
        else:
            qx_t = None
            scale_inv_t = None

        return QuantizeResult(data=qx, scale=scale_inv, data_t=qx_t, scale_t=scale_inv_t)

    @classmethod
    def _quantize_vectorwise_reference(
        cls,
        x: torch.Tensor,
        quant_dtype: torch.dtype,
        tile_len: int,
        *,
        pow_2_scales: bool,
        eps: float,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        M, K = x.shape
        if quant_dtype == torch.int8:
            dtype_max = torch.iinfo(quant_dtype).max
        else:
            dtype_max = torch.finfo(quant_dtype).max
        x_tiled = x.reshape(M, K // tile_len, tile_len)
        amax_grid = torch.abs(x_tiled).amax(dim=-1).float()
        scale, scale_inv, _ = scale_from_amax_tensor(
            x_dtype=x.dtype,
            amax=amax_grid,
            quant_dtype=quant_dtype,
            pow_2_scales=pow_2_scales,
            eps=eps,
        )
        qx = x_tiled * scale.reshape(M, K // tile_len, 1)
        qx = torch.clamp(qx, min=-dtype_max, max=dtype_max)
        if quant_dtype == torch.int8:
            positive_mask = qx >= 0
            negative_mask = ~positive_mask
            pos_part = torch.where(positive_mask, torch.floor(qx + 0.5), 0)
            neg_part = torch.where(negative_mask, torch.ceil(qx - 0.5), 0)
            qx = pos_part + neg_part
        qx = qx.to(dtype=quant_dtype)
        qx = qx.reshape(M, K)
        return qx, scale_inv

    @classmethod
    def _quantize_vector_tiling(
        cls,
        x: torch.Tensor,
        quant_dtype: torch.dtype,
        tile_len: int,
        *,
        return_transpose: bool,
        pow_2_scales: bool,
        eps: float,
    ) -> QuantizeResult:
        M, K = x.shape

        if K % tile_len == 0:
            qref_input = x
        else:
            pad_amount = tile_len - (K % tile_len)
            pad = (0, pad_amount)
            qref_input = torch.nn.functional.pad(x, pad, mode="constant", value=0)
        qout_padded, scale_inv = cls._quantize_vectorwise_reference(
            qref_input,
            quant_dtype,
            tile_len=tile_len,
            pow_2_scales=pow_2_scales,
            eps=eps,
        )
        if K % tile_len == 0:
            qout = qout_padded
        else:
            qout = qout_padded[:, :K].contiguous()

        if return_transpose:
            if M % tile_len == 0:
                qref_input = x.transpose(-1, -2).contiguous()
            else:
                amount_to_pad = tile_len - (M % tile_len)
                pad = (0, amount_to_pad)
                qref_input = torch.nn.functional.pad(
                    x.transpose(-1, -2), pad, mode="constant", value=0
                ).contiguous()
            qout_t_padded, scale_inv_t = cls._quantize_vectorwise_reference(
                qref_input,
                quant_dtype,
                tile_len=tile_len,
                pow_2_scales=pow_2_scales,
                eps=eps,
            )
            if M % tile_len == 0:
                qout_t = qout_t_padded
            else:
                qout_t = qout_t_padded[:, :M].contiguous()
        else:
            qout_t, scale_inv_t = None, None

        return QuantizeResult(data=qout, scale=scale_inv, data_t=qout_t, scale_t=scale_inv_t)

    def ref_dequantize_rowwise(
        self,
        q: torch.Tensor,
        quant_tile_shape: Tuple[int, int],
        s: torch.Tensor,
        dtype: torch.dtype,
    ) -> torch.Tensor:
        assert q.dim() == 2
        q_M, q_K = q.shape
        s = self.scale_munger.demunge_scale_shape_from_backend((q_M, q_K), s, quant_tile_shape)
        assert len(s.shape) == 2
        m_tiles, k_tiles = s.shape
        M, K = q.shape
        unpadded_m, unpadded_k = M, K
        if M % quant_tile_shape[0] != 0 or K % quant_tile_shape[1] != 0:
            m_pad_amount = (quant_tile_shape[0] - (M % quant_tile_shape[0])) % quant_tile_shape[0]
            k_pad_amount = (quant_tile_shape[1] - (K % quant_tile_shape[1])) % quant_tile_shape[1]
            q = torch.nn.functional.pad(
                q, (0, k_pad_amount, 0, m_pad_amount), mode="constant", value=0
            ).contiguous()
            M, K = q.shape
        q_tiled = q.reshape(m_tiles, quant_tile_shape[0], k_tiles, quant_tile_shape[1])
        result = q_tiled.to(dtype) * s.reshape(m_tiles, 1, k_tiles, 1)
        result = result.view(M, K).to(dtype)
        if M != unpadded_m or K != unpadded_k:
            result = result[:unpadded_m, :unpadded_k].contiguous()
        return result

    def quantize(
        self,
        x: torch.Tensor,
        quant_dtype: torch.dtype,
        return_transpose: bool = False,
        eps: float = 0.0,
        pow_2_scales: bool = False,
        quant_tile_shape: Tuple[int, int] = (blockwise_fp8_block_len, blockwise_fp8_block_len),
        munge_scale_shapes: bool = True,
    ) -> QuantizeResult:
        # sanity checks
        assert x.dim() == 2
        assert x.dtype in (
            torch.float,
            torch.float16,
            torch.bfloat16,
            torch.float32,
        ), "Unsupported input dtype."
        assert quant_dtype in (
            torch.float8_e4m3fn,
            torch.float8_e5m2,
            torch.int8,
        ), "Unsupported quant dtype."

        assert quant_tile_shape in ((1, blockwise_fp8_block_len), (blockwise_fp8_block_len, blockwise_fp8_block_len))
        if quant_tile_shape[0] == 1:
            # Quantize row-wise
            result = self._quantize_vector_tiling(
                x,
                quant_dtype,
                tile_len=quant_tile_shape[1],
                return_transpose=return_transpose,
                pow_2_scales=pow_2_scales,
                eps=eps,
            )
            if munge_scale_shapes:
                result = self.scale_munger.munge_scale_shapes_for_backend(
                    result,
                    quant_tile_shape,
                )
            return result
        else:
            # Quantize block-wise
            result = self._quantize_square_block_tiling(
                x,
                quant_dtype,
                tile_len=quant_tile_shape[0],
                return_transpose=return_transpose,
                pow_2_scales=pow_2_scales,
                eps=eps,
            )
            if munge_scale_shapes:
                result = self.scale_munger.munge_scale_shapes_for_backend(
                    result,
                    quant_tile_shape,
                )
            return result
