# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from math import prod
from typing import Optional, Union

import torch

from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    per_token_group_quant_fp8)
from vllm.model_executor.layers.quantization.utils.int8_utils import (
    per_token_group_quant_int8, per_token_quant_int8)
from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
    quant_dequant_mxfp4)
from vllm.platforms import current_platform
from vllm.utils import cdiv


def _resize_cache(x: torch.Tensor, v: tuple[int, ...]) -> torch.Tensor:
    """
    Shrink the given tensor and apply the given view to it.  This is
    used to resize the intermediate fused_moe caches.
    """
    assert prod(v) <= x.numel(
    ), f"{v} ({prod(v)}) <= {x.shape} ({x.numel()})"  # CUDAGRAPH unfriendly?
    return x.flatten()[:prod(v)].view(*v)


def _fp8_quantize(
    A: torch.Tensor,
    A_scale: Optional[torch.Tensor],
    per_act_token: bool,
    block_shape: Optional[list[int]] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Perform fp8 quantization on the inputs.  If a block_shape
    is provided, the output will be blocked.
    """
    if block_shape is None:
        A, A_scale = ops.scaled_fp8_quant(
            A, A_scale, use_per_token_if_dynamic=per_act_token)
    else:
        assert not per_act_token
        assert len(block_shape) == 2
        _, block_k = block_shape[0], block_shape[1]
        A, A_scale = per_token_group_quant_fp8(A, block_k)
        assert cdiv(A.size(-1), block_k) == A_scale.size(-1)

    return A, A_scale


def _int8_quantize(
    A: torch.Tensor,
    A_scale: Optional[torch.Tensor],
    per_act_token: bool,
    block_shape: Optional[list[int]] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Perform int8 quantization on the inputs.  If a block_shape
    is provided, the output will be blocked.
    """

    # If weights are per-channel (per_channel_quant=True), then
    # activations apply per-token quantization. Otherwise, assume
    # activation tensor-wise fp8/int8 quantization, dynamic or static
    if block_shape is None:
        assert per_act_token, \
            "int8 quantization only supports block or channel-wise"
        A, A_scale = per_token_quant_int8(A)
    else:
        assert not per_act_token
        assert len(block_shape) == 2
        _, block_k = block_shape[0], block_shape[1]
        A, A_scale = per_token_group_quant_int8(A, block_k)
        assert cdiv(A.size(-1), block_k) == A_scale.size(-1)

    return A, A_scale


def _mxfp4_quantize(
    A: torch.Tensor,
    A_scale: Optional[torch.Tensor],
    per_act_token_quant: bool,
    block_shape: Optional[list[int]] = None,
) -> tuple[torch.Tensor, None]:
    assert block_shape is None
    if not current_platform.supports_mx():
        A = quant_dequant_mxfp4(A)
    else:
        raise NotImplementedError()

    return A, None


def moe_kernel_quantize_input(
    A: torch.Tensor,
    A_scale: Optional[torch.Tensor],
    quant_dtype: Union[None, torch.dtype, str],
    per_act_token_quant: bool,
    block_shape: Optional[list[int]] = None,
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
    if quant_dtype == torch.float8_e4m3fn:
        return _fp8_quantize(A, A_scale, per_act_token_quant, block_shape)
    elif quant_dtype == torch.int8:
        return _int8_quantize(A, A_scale, per_act_token_quant, block_shape)
    elif quant_dtype == "mxfp4":
        return _mxfp4_quantize(A, A_scale, per_act_token_quant, block_shape)
    else:
        return A, A_scale


def _fp8_perm(m: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
    """
    A permutation routine that works on fp8 types.
    """
    if torch.is_floating_point(m) and m.dtype.itemsize == 1:
        return m.view(dtype=torch.uint8)[idx, ...].view(dtype=m.dtype)
    else:
        return m[idx, ...]


def normalize_scales_shape(
        scales: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
    if scales is not None:
        if scales.numel() == 1:
            scales = scales.view(1, 1)
        else:
            scales = scales.view(-1, scales.size(-1))
    return scales


def normalize_batched_scales_shape(
    scales: Optional[torch.Tensor],
    num_experts: int,
) -> Optional[torch.Tensor]:
    if scales is not None and scales.ndim < 3:
        if scales.numel() == 1:
            scales = scales.view(1)
            scales = torch.repeat_interleave(scales, num_experts,
                                             dim=0).view(num_experts, 1, 1)
        else:
            scales = scales.view(num_experts, -1, scales.size(-1))

    return scales


def _validate_scale_shape(
    a: torch.Tensor,
    a_scale: Optional[torch.Tensor],
    per_act_token_quant: bool,
    block_shape: Optional[list[int]],
) -> None:
    if a_scale is None:
        return

    if not per_act_token_quant and block_shape is None:
        assert a_scale.numel() == 1, f"{a_scale.shape}"
    elif per_act_token_quant:
        assert a_scale.shape[0] == a.shape[0] and a_scale.shape[1] == 1, (
            f"{a_scale.shape[0]} == {a.shape[0]} and {a_scale.shape[1]} == 1")
    else:
        assert block_shape is not None
        expected = (a.shape[0], cdiv(a.shape[1], block_shape[1]))
        assert a_scale.shape == expected, f"{a_scale.shape} == {expected}"
