""" CUTLASS based Fused MoE kernels."""
from typing import Any, Callable, Optional

import torch
from loguru import logger

import aphrodite.modeling.layers.fused_moe.modular_kernel as mk
from aphrodite import _custom_ops as ops
from aphrodite.modeling.layers.fused_moe.config import FusedMoEQuantConfig
from aphrodite.modeling.layers.fused_moe.prepare_finalize import (
    MoEPrepareAndFinalizeNoEP)
from aphrodite.modeling.layers.fused_moe.topk_weight_and_reduce import (
    TopKWeightAndReduceDelegate)
from aphrodite.modeling.layers.fused_moe.utils import (_fp8_perm,
                                                       _fp8_quantize,
                                                       _resize_cache,
                                                       extract_required_args)
from aphrodite.scalar_type import scalar_types
from aphrodite.common.logger import log_once


def run_cutlass_moe_fp8(
    output: torch.Tensor,
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_ids: torch.Tensor,
    activation_callable: Callable,
    global_num_experts: int,
    expert_map: Optional[torch.Tensor],
    w1_scale: Optional[torch.Tensor],
    w2_scale: Optional[torch.Tensor],
    a1q_scale: Optional[torch.Tensor],
    a2_scale: Optional[torch.Tensor],
    workspace13: torch.Tensor,
    workspace2: torch.Tensor,
    expert_num_tokens: Optional[torch.Tensor],
    out_dtype: torch.dtype,
    per_act_token: bool,
    per_out_ch: bool,
    use_batched_format: bool,
):
    a1q = hidden_states

    assert w1_scale is not None
    assert w2_scale is not None
    assert w1.dtype == torch.float8_e4m3fn
    assert w2.dtype == torch.float8_e4m3fn
    assert a1q.size(-1) == w1.size(2), "Hidden size mismatch w1"
    assert w1.size(1) == w2.size(2) * 2, "Hidden size mismatch w2"
    assert w1_scale.dim() == 1 or w1_scale.size(
        1) == 1 or w1_scale.shape[1] == w1.size(1), "W1 scale shape mismatch"
    assert w2_scale.dim() == 1 or w2_scale.size(
        1) == 1 or w2_scale.shape[1] == w2.size(1), "W2 scale shape mismatch"
    assert w1.size(0) == w2.size(0), "Expert number mismatch"
    assert a1q_scale is None or a1q_scale.dim() == 0 or a1q_scale.size(
        0) == 1 or a1q_scale.size(
            0) == a1q.shape[0], "Input scale shape mismatch"
    assert w1.size(0) == w2.size(0), "Weights expert number mismatch"
    assert w1.size(0) == w1_scale.size(0), "w1 scales expert number mismatch"
    assert w1.size(0) == w2_scale.size(0), "w2 scales expert number mismatch"
    assert a2_scale is None or a2_scale.dim() == 0 or a2_scale.size(
        0) == 1 or a2_scale.size(
            0) == a1q.shape[0], "Intermediate scale shape mismatch"
    assert out_dtype in [torch.half, torch.bfloat16], "Invalid output dtype"
    if expert_map is not None:
        assert expert_num_tokens is None

    # We have two modes: batched experts and non-batched experts.
    # In the non-batched mode, the input tokens are not padded: thus, the shape
    # of the input is [total_num_tokens, hidden_size]. The input and output
    # require shuffling by a_map and c_map such that the tokens assigned to
    # each expert are contiguous.
    # In the batched mode, the input tokens are padded per expert to ensure that
    # the batched dispatch and combine functions work correctly: thus, the shape
    # of the input is [num_experts, max_num_tokens_per_expert, hidden_size].
    # The batched input and output require no shuffling by a_map and c_map since
    # their tokens are already contiguous for each expert as a result of
    # the dispatch function.

    M = a1q.size(0)  # non batched expert M
    padded_M = a1q.size(1)  # batched expert M
    _, K, N = w2.shape
    device = a1q.device

    assert w1.size(2) == K
    assert global_num_experts != -1
    assert a1q_scale is not None

    if expert_map is not None:
        "Translate info from expert_map to topk_ids"
        local_topk_ids = torch.where(expert_map[topk_ids] != -1,
                                     expert_map[topk_ids], -1)
    else:
        local_topk_ids = topk_ids

    topk = local_topk_ids.size(1)
    local_E = w1.size(0)

    if use_batched_format:
        assert expert_num_tokens is not None

        expert_offsets = torch.empty((local_E),
                                     dtype=torch.int32,
                                     device=device)
        problem_sizes1 = torch.empty((local_E, 3),
                                     dtype=torch.int32,
                                     device=device)
        problem_sizes2 = torch.empty((local_E, 3),
                                     dtype=torch.int32,
                                     device=device)

        ops.get_cutlass_pplx_moe_mm_data(expert_offsets, problem_sizes1,
                                         problem_sizes2, expert_num_tokens,
                                         local_E, padded_M, N, K)

        w1_scale = w1_scale.reshape(w1_scale.size(0), -1)
        w2_scale = w2_scale.reshape(w2_scale.size(0), -1)
        a1q = a1q.reshape(-1, a1q.size(2))
        a1q_scale = a1q_scale.reshape(-1, a1q_scale.size(2)).contiguous()

    else:
        expert_offsets = torch.empty((global_num_experts + 1),
                                     dtype=torch.int32,
                                     device=device)
        problem_sizes1 = torch.empty((global_num_experts, 3),
                                     dtype=torch.int32,
                                     device=device)
        problem_sizes2 = torch.empty((global_num_experts, 3),
                                     dtype=torch.int32,
                                     device=device)

        # With expert_map each Rank processes only a subset of experts. As
        # a result not all of a_map and c2 tensors are filled. We fill it
        # zeros for correctness.
        if expert_map is not None:
            a_map = torch.zeros((local_topk_ids.numel()),
                                dtype=torch.int32,
                                device=device)
        else:
            a_map = torch.empty((local_topk_ids.numel()),
                                dtype=torch.int32,
                                device=device)

        c_map = torch.empty((local_topk_ids.numel()),
                            dtype=torch.int32,
                            device=device)

        ops.get_cutlass_moe_mm_data(local_topk_ids, expert_offsets,
                                    problem_sizes1, problem_sizes2, a_map,
                                    c_map, global_num_experts, N, K)

        a1q = _fp8_perm(a1q, a_map)
        a1q_scale = a1q_scale[a_map] if per_act_token else a1q_scale
        expert_offsets = expert_offsets[:-1]

    ab_strides1 = torch.full((w1.size(0), ),
                             K,
                             device=device,
                             dtype=torch.int64)
    c_strides1 = torch.full((w1.size(0), ),
                            2 * N,
                            device=device,
                            dtype=torch.int64)
    ab_strides2 = torch.full((w1.size(0), ),
                             N,
                             device=device,
                             dtype=torch.int64)
    c_strides2 = torch.full((w1.size(0), ),
                            K,
                            device=device,
                            dtype=torch.int64)

    if use_batched_format:
        c1 = _resize_cache(workspace13, (local_E * padded_M, N * 2))
        c2 = _resize_cache(workspace2, (local_E * padded_M, N))
        c3 = _resize_cache(workspace13, (local_E * padded_M, K))
    else:
        c1 = _resize_cache(workspace13, (M * topk, N * 2))
        c2 = _resize_cache(workspace2, (M * topk, N))
        c3 = _resize_cache(workspace13, (M * topk, K))

    if not per_act_token and (expert_map is not None or use_batched_format):
        # this is necessary to avoid imprecise scale calculation caused by
        # random data in the unused workspace. The workspace is unused when
        # this rank handles only partial tokens, or when it is batched .
        c1.fill_(0)

    ops.cutlass_moe_mm(c1, a1q, w1, a1q_scale, w1_scale, expert_offsets,
                       problem_sizes1, ab_strides1, ab_strides1, c_strides1,
                       per_act_token, per_out_ch)

    activation_callable(c2, c1)

    a2q, a2q_scale = ops.scaled_fp8_quant(
        c2, a2_scale, use_per_token_if_dynamic=per_act_token)

    if expert_map is not None:
        c3.fill_(0)

    ops.cutlass_moe_mm(c3, a2q, w2, a2q_scale, w2_scale, expert_offsets,
                       problem_sizes2, ab_strides2, ab_strides2, c_strides2,
                       per_act_token, per_out_ch)

    if use_batched_format:
        output.copy_(c3.reshape(local_E, padded_M, K), non_blocking=True)
    else:
        # We can't do this inplace because output may point to the same tensor
        # as c3.
        output.copy_(c3[c_map].view(M * topk, K), non_blocking=True)


# TODO (bnell): split class batched vs. non-batched?
# maybe remove need for passing aq to workspace_shapes
class CutlassExpertsFp8(mk.FusedMoEPermuteExpertsUnpermute):

    def __init__(
        self,
        max_experts_per_worker: int,
        out_dtype: Optional[torch.dtype],
        per_act_token_quant: bool,
        per_out_ch_quant: bool,
        block_shape: Optional[list[int]] = None,
        num_dispatchers: Optional[int] = None,
        use_batched_format: bool = False,
    ):
        super().__init__(
            FusedMoEQuantConfig(
                quant_dtype=torch.float8_e4m3fn,
                per_act_token_quant=per_act_token_quant,
                per_out_ch_quant=per_out_ch_quant,
                block_shape=block_shape,
            ))
        assert max_experts_per_worker > 0
        assert not use_batched_format or num_dispatchers is not None
        self.max_experts_per_worker = max_experts_per_worker
        self.num_dispatchers = num_dispatchers
        self.out_dtype = out_dtype
        self.use_batched_format = use_batched_format

    @property
    def activation_formats(
        self
    ) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
        if self.use_batched_format:
            return (mk.FusedMoEActivationFormat.BatchedExperts,
                    mk.FusedMoEActivationFormat.BatchedExperts)
        else:
            return (mk.FusedMoEActivationFormat.Standard,
                    mk.FusedMoEActivationFormat.Standard)

    def supports_chunking(self) -> bool:
        return not self.use_batched_format

    def supports_expert_map(self) -> bool:
        return not self.use_batched_format

    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
        # Let PrepareAndFinalize::finalize() decide the impl.
        return TopKWeightAndReduceDelegate()

    def workspace_shapes(
        self,
        a: torch.Tensor,
        aq: torch.Tensor,
        M: int,
        N: int,
        K: int,
        topk: int,
        global_num_experts: int,
        local_num_experts: int,
        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
        workspace1: tuple[int, ...] = ()
        workspace2: tuple[int, ...] = ()
        output: tuple[int, ...] = ()
        if self.use_batched_format:
            padded_M = aq.size(1)
            num_dp = self.num_dispatchers
            assert num_dp is not None
            workspace1 = (self.max_experts_per_worker, padded_M * num_dp,
                          max(N, K))
            workspace2 = (self.max_experts_per_worker, padded_M * num_dp,
                          (N // 2))
            output = (self.max_experts_per_worker, padded_M, K)
        else:
            workspace1 = (M * topk, max(N, K))
            workspace2 = (M * topk, N // 2)
            output = (M * topk, K)
        return (workspace1, workspace2, output,
                self.out_dtype if self.out_dtype is not None else a.dtype)

    def apply(self, output: torch.Tensor, hidden_states: torch.Tensor,
              w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor,
              topk_ids: torch.Tensor, activation: str, global_num_experts: int,
              expert_map: Optional[torch.Tensor],
              w1_scale: Optional[torch.Tensor],
              w2_scale: Optional[torch.Tensor], w1_zp: Optional[torch.Tensor],
              w2_zp: Optional[torch.Tensor], a1q_scale: Optional[torch.Tensor],
              a2_scale: Optional[torch.Tensor], workspace13: torch.Tensor,
              workspace2: torch.Tensor,
              expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
              apply_router_weight_on_input: bool,
              extra_expert_args: Optional[dict[str, Any]]):
        assert w1_zp is None, "w1_zp is not supported in CUTLASS MoE"
        assert w2_zp is None, "w2_zp is not supported in CUTLASS MoE"

        expert_num_tokens = None
        if expert_tokens_meta is not None:
            expert_num_tokens = expert_tokens_meta.expert_num_tokens

        activation_callable = lambda o, i: self.activation(activation, o, i)

        in_dtype = hidden_states.dtype
        run_cutlass_moe_fp8(
            output, hidden_states, w1, w2, topk_ids, activation_callable,
            global_num_experts, expert_map, w1_scale, w2_scale, a1q_scale,
            a2_scale, workspace13, workspace2, expert_num_tokens,
            self.out_dtype if self.out_dtype is not None else in_dtype,
            self.per_act_token_quant, self.per_out_ch_quant,
            self.use_batched_format)


def cutlass_moe_fp8(
    a: torch.Tensor,
    w1_q: torch.Tensor,
    w2_q: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: torch.Tensor,
    w2_scale: torch.Tensor,
    per_act_token: Optional[bool] = None,
    activation: str = "silu",
    a1_scale: Optional[torch.Tensor] = None,
    a2_scale: Optional[torch.Tensor] = None,
    expert_map: Optional[torch.Tensor] = None,
    apply_router_weight_on_input: bool = False,
    global_num_experts: int = -1,
) -> torch.Tensor:
    """
    This function computes a a8w8-quantized Mixture of Experts (MoE) layer
    using two sets of quantized weights, w1_q and w2_q, and top-k gating
    mechanism. The matrix multiplications are implemented with CUTLASS
    grouped gemm.

    Parameters:
    - a (torch.Tensor): The input tensor to the MoE layer.
        Shape: [M, K]
    - w1_q (torch.Tensor): The first set of fp8-quantized expert weights.
        Shape: [num_experts, K, 2N] (the weights are passed transposed)
    - w2_q (torch.Tensor): The second set of fp8-quantized expert weights.
        Shape: [num_experts, N, K] (the weights are passed transposed)
    - topk_weights (torch.Tensor): The weights of each token->expert mapping.
    - topk_ids (torch.Tensor): The token->expert mappings.
    - w1_scale (torch.Tensor): The fp32 scale to dequantize w1_q.
        Shape: [num_experts] or [num_experts, 2N]
    - w2_scale (torch.Tensor): The fp32 scale to dequantize w2_q.
        Shape: [num_experts] or [num_experts, K]
    - a1_scale (Optional[torch.Tensor]): The optional fp32 scale to quantize a.
        Shape: scalar or [M]
    - a2_scale (Optional[torch.Tensor]): The optional fp32 scale to
        quantize the intermediate result between the gemms.
        Shape: scalar or [M]
    - expert_map (Optional[torch.Tensor]): In the case of Expert parallel,
        every Rank is responsible for a subset of experts. expert_map is a
        mapping from global expert-id to local expert-id. When expert_map[i]
        is -1, it means that this Rank is not responsible for global
        expert-id i.
    - apply_router_weight_on_input (bool): When true, the topk weights are
        applied directly on the inputs. This is only applicable when topk is 1.
    - global_num_experts (int): The total number of experts.

    Returns:
    - torch.Tensor: The fp16 output tensor after applying the MoE layer.
    """
    if per_act_token is None:
        per_act_token = a1_scale.numel() != 1 if a1_scale is not None else (
            a2_scale.numel() != 1 if a2_scale is not None else False)
    per_out_ch = w1_scale.numel() != w1_q.size(0)

    num_experts = global_num_experts if global_num_experts != -1 else w1_q.size(
        0)

    fn = mk.FusedMoEModularKernel(
        MoEPrepareAndFinalizeNoEP(),
        CutlassExpertsFp8(
            max_experts_per_worker=num_experts,
            out_dtype=a.dtype,
            per_act_token_quant=per_act_token,
            per_out_ch_quant=per_out_ch,
            use_batched_format=False,
        ),
    )

    return fn(
        a,
        w1_q,
        w2_q,
        topk_weights,
        topk_ids,
        False,
        activation,
        num_experts,
        expert_map,
        w1_scale,
        w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
        apply_router_weight_on_input=apply_router_weight_on_input,
    )


FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max


def run_cutlass_moe_fp4(
    output: torch.Tensor,
    a: torch.Tensor,
    a1_gscale: torch.Tensor,
    w1_fp4: torch.Tensor,
    w1_blockscale: torch.Tensor,
    w1_alphas: torch.Tensor,
    a2_gscale: torch.Tensor,
    w2_fp4: torch.Tensor,
    w2_blockscale: torch.Tensor,
    w2_alphas: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    workspace13: torch.Tensor,
    workspace2: torch.Tensor,
    m: int,
    n: int,
    k: int,
    e: int,
    device: torch.device,
    apply_router_weight_on_input: bool = False,
) -> None:
    """
    MoE implementation for FP4 Inputs
    
    # Gemm 1
    a: Input tensor: [m, k] (half/bfloat16)
    a1_gscale: Activation scale per expert: [e]  (float32)
    w1(gate up) (not an argument to cutlass_moe_fp4): [e, 2 * n, k]
    w1_fp4: [e, 2 * n, k // 2], dtype: torch.uint8 (stacked fp4: E2M1)
    (Note: `n` is the up projection output dim, `k` is the input dim in
     full precision)
    w1_blockscale: [e, 2 * n, k // block_size] (float8_e4m3)
                   (Block size = 16 for NVFP4)
    
    # Gemm 2
    a2_gscale: Activation scale per expert: [e]
    w2(down projection) (not an argument to cutlass_moe_fp4): [e, k, n]
    w2_fp4: [e, k, n // 2], dtype: torch.uint8 (stacked E2M1)
    w2_blockscale: [e, k, n // block_size], dtype: float8_e4m3
    
    topk_weights: [m, topk] dtype: float8
    topk_ids: [m, topk] dtype: float8
    
    m, n, k: Unquantized weight shapes, dtype: int
    e: number of experts, dtype: int

    assumes that topk < k < n to satisfy - up/down projection expectations.
    """
    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
    assert w1_fp4.dtype == torch.uint8, "weight 1 must be uint8"
    assert w2_fp4.dtype == torch.uint8, "weight 2 must be uint8"
    assert (w1_fp4.ndim == 3 and w2_fp4.ndim == 3 and w1_blockscale.ndim == 3
            and w2_blockscale.ndim
            == 3), ("All Weights must be of rank 3 for cutlass_moe_fp4")
    m_a, k_a = a.shape
    e_w1, nx2_w1, half_k_w1 = w1_fp4.shape
    e_w2, k_w2, half_n_w2 = w2_fp4.shape

    assert (e_w1 == e_w2 and e_w1 == e), ("Number of experts must match",
                                          " between weights.")
    assert (k_a == half_k_w1 * 2
            and k == k_w2), ("Hidden size mismatch between a, w1 and w2")
    assert (nx2_w1 == n * 2 and half_n_w2 * 2 == n), ("mismatch in "
                                                      "expected `n`")
    assert (m == m_a), "input shape mismatch"
    assert 2 * half_k_w1 == k_w2, "Hidden size mismatch w2 and w1"
    assert a.dtype in [torch.half, torch.bfloat16], "Invalid input dtype"
    assert (topk_weights.size(0) == m and topk_ids.size(0)
            == m), ("topk must be provided for each row of a")
    topk = topk_ids.size(1)
    out_dtype = a.dtype
    num_topk = topk_ids.size(1)

    expert_offsets = torch.empty((e + 1), dtype=torch.int32, device=device)
    blockscale_offsets = torch.empty((e + 1), dtype=torch.int32, device=device)
    # Problem size:  (num_experts, (m,2n,k))
    problem_sizes1 = torch.empty((e, 3), dtype=torch.int32, device=device)
    # Problem size:  (num_experts, (m,n,k))
    problem_sizes2 = torch.empty((e, 3), dtype=torch.int32, device=device)

    a_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)
    c_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)

    if apply_router_weight_on_input:
        # TODO: this only works for topK=1, will need to update for topK>1
        assert num_topk == 1, \
            "apply_router_weight_on_input is only implemented for topk=1"
        a.mul_(topk_weights.to(out_dtype))

    # problem shapes should have [m, n, k]
    # Note that problem sizes are based on logical number of elements.
    ops.get_cutlass_moe_mm_data(topk_ids, expert_offsets, problem_sizes1,
                                problem_sizes2, a_map, c_map, e, n, k,
                                blockscale_offsets)

    a = ops.shuffle_rows(a, a_map)
    rep_a_fp4, rep_a_blockscale = ops.scaled_fp4_experts_quant(
        a,
        a1_gscale,
        expert_offsets,
        blockscale_offsets,
        num_topk,
    )
    c1 = _resize_cache(workspace13, (m * topk, n * 2))
    c2 = _resize_cache(workspace2, (m * topk, n))
    c3 = _resize_cache(workspace13, (m * topk, k))
    ops.cutlass_fp4_moe_mm(c1, rep_a_fp4, w1_fp4, rep_a_blockscale,
                           w1_blockscale, w1_alphas, problem_sizes1,
                           expert_offsets[:-1], blockscale_offsets[:-1])
    del rep_a_fp4, rep_a_blockscale
    torch.ops._C.silu_and_mul(c2, c1)
    int_fp4, int_blockscale = ops.scaled_fp4_experts_quant(
        c2, a2_gscale, expert_offsets, blockscale_offsets, num_topk)

    ops.cutlass_fp4_moe_mm(c3, int_fp4, w2_fp4, int_blockscale, w2_blockscale,
                           w2_alphas, problem_sizes2, expert_offsets[:-1],
                           blockscale_offsets[:-1])
    del int_fp4, int_blockscale

    c3 = ops.shuffle_rows(c3, c_map)

    assert output.dtype == out_dtype
    if not apply_router_weight_on_input:
        output.copy_(
            (c3.view(m, num_topk, k) *
             topk_weights.view(m, num_topk, 1).to(out_dtype)).sum(dim=1),
            non_blocking=True)
    else:
        output.copy_(c3.view(m, num_topk, k).sum(dim=1), non_blocking=True)
    return


class CutlassExpertsFp4(mk.FusedMoEPermuteExpertsUnpermute):

    def __init__(
        self,
        max_experts_per_worker: int,
        out_dtype: torch.dtype,
        per_act_token_quant: bool,
        per_out_ch_quant: bool,
        block_shape: Optional[list[int]] = None,
        use_batched_format: bool = False,
    ):
        super().__init__(
            FusedMoEQuantConfig(
                quant_dtype=torch.uint8,
                per_act_token_quant=per_act_token_quant,
                per_out_ch_quant=per_out_ch_quant,
                block_shape=block_shape,
            ))
        self.max_experts_per_worker = max_experts_per_worker
        self.out_dtype = out_dtype
        self.use_batched_format = use_batched_format

    @property
    def activation_formats(
        self
    ) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
        if self.use_batched_format:
            return (mk.FusedMoEActivationFormat.BatchedExperts,
                    mk.FusedMoEActivationFormat.BatchedExperts)
        else:
            return (mk.FusedMoEActivationFormat.Standard,
                    mk.FusedMoEActivationFormat.Standard)

    def supports_expert_map(self) -> bool:
        return False

    def supports_chunking(self) -> bool:
        return True

    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
        # Let PrepareAndFinalize::finalize() decide the impl.
        return TopKWeightAndReduceDelegate()

    def workspace_shapes(
        self,
        a: torch.Tensor,
        aq: torch.Tensor,
        M: int,
        N: int,
        K: int,
        topk: int,
        global_num_experts: int,
        local_num_experts: int,
        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
        workspace1: tuple[int, ...] = ()
        workspace2: tuple[int, ...] = ()
        output: tuple[int, ...] = ()
        if self.use_batched_format:
            padded_M = aq.size(1)
            workspace1 = (self.max_experts_per_worker, padded_M, max(N, K))
            workspace2 = (self.max_experts_per_worker, padded_M, (N // 2))
            output = (self.max_experts_per_worker, padded_M, K)
        else:
            workspace1 = (M * topk, max(2 * N, K))
            workspace2 = (M * topk, N)
            output = (M, K)
        return (workspace1, workspace2, output,
                self.out_dtype if self.out_dtype is not None else a.dtype)

    def apply(self, output: torch.Tensor, hidden_states: torch.Tensor,
              w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor,
              topk_ids: torch.Tensor, activation: str, global_num_experts: int,
              expert_map: Optional[torch.Tensor], w1_scale: torch.Tensor,
              w2_scale: torch.Tensor, w1_zp: Optional[torch.Tensor],
              w2_zp: Optional[torch.Tensor], a1q_scale: Optional[torch.Tensor],
              a2_scale: torch.Tensor, workspace13: Optional[torch.Tensor],
              workspace2: Optional[torch.Tensor],
              expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
              apply_router_weight_on_input: bool,
              extra_expert_args: Optional[dict[str, Any]]):
        required_keys = [
            "g1_alphas", "g2_alphas", "a1_gscale", "a2_gscale", "m", "n", "k",
            "e", "device"
        ]
        (g1_alphas, g2_alphas, a1_gscale, a2_gscale, m, n, k, e,
         device) = extract_required_args(extra_expert_args, required_keys)
        run_cutlass_moe_fp4(
            output=output,
            a=hidden_states,
            a1_gscale=a1_gscale,
            w1_fp4=w1,
            w1_blockscale=w1_scale,
            w1_alphas=g1_alphas,
            a2_gscale=a2_gscale,
            w2_fp4=w2,
            w2_blockscale=w2_scale,
            w2_alphas=g2_alphas,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            workspace13=workspace13,
            workspace2=workspace2,
            m=m,
            n=n,
            k=k,
            e=e,
            device=device,
            apply_router_weight_on_input=apply_router_weight_on_input,
        )


def cutlass_moe_fp4(
        a: torch.Tensor,
        w1_fp4: torch.Tensor,
        w2_fp4: torch.Tensor,
        w1_blockscale: torch.Tensor,
        w2_blockscale: torch.Tensor,
        g1_alphas: torch.Tensor,
        g2_alphas: torch.Tensor,
        a1_gscale: torch.Tensor,
        a2_gscale: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        m: int,
        n: int,
        k: int,
        e: int,
        device: torch.device,
        expert_map: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False) -> torch.Tensor:
    assert expert_map is None, ("Expert Parallelism / expert_map "
                                "is currently not supported for "
                                "ModelOptNvFp4FusedMoE's cutlass_moe_fp4.")
    fn = mk.FusedMoEModularKernel(
        MoEPrepareAndFinalizeNoEP(),
        CutlassExpertsFp4(
            max_experts_per_worker=e,
            out_dtype=a.dtype,
            per_act_token_quant=False,
            per_out_ch_quant=False,
            use_batched_format=False,
        ),
    )
    extra_expert_args = {
        'g1_alphas': g1_alphas,
        'g2_alphas': g2_alphas,
        'a1_gscale': a1_gscale,
        'a2_gscale': a2_gscale,
        'm': m,
        'n': n,
        'k': k,
        'e': e,
        'device': device,
    }

    # NVFP4 requires two levels of quantization, which involves computing some
    # scaling factors dynamically. This makes it incompatible with the typical
    # prepare -> MoE -> finalize pipeline. Move the quantization logic into the
    # MoE body.
    extra_prepare_args = {
        'skip_quant': True,
    }
    # Similar reason as above.
    extra_finalize_args = {
        'skip_weight_reduce': True,
    }
    return fn(
        hidden_states=a,
        w1=w1_fp4,
        w2=w2_fp4,
        topk_weights=topk_weights,
        topk_ids=topk_ids,
        inplace=False,
        activation="silu",
        global_num_experts=e,
        expert_map=None,
        w1_scale=w1_blockscale,
        w2_scale=w2_blockscale,
        a1_scale=None,
        a2_scale=None,
        apply_router_weight_on_input=apply_router_weight_on_input,
        extra_expert_args=extra_expert_args,
        extra_prepare_args=extra_prepare_args,
        extra_finalize_args=extra_finalize_args,
    )


def _valid_cutlass_block_scaled_grouped_gemm(
        w1: torch.Tensor, w2: torch.Tensor, inplace: bool, activation: str,
        apply_router_weight_on_input: bool,
        expert_map: Optional[torch.Tensor]) -> bool:

    def _valid_cutlass_block_scaled_grouped_gemm_shape(N: int, K: int):
        return N % 128 == 0 and K % 128 == 0

    _, K, N = w2.size()
    if not _valid_cutlass_block_scaled_grouped_gemm_shape(N, K):
        log_once(
            "DEBUG",
            "CutlassBlockScaledGroupedGemm disabled: unaligned problem size. "
            "N: {}, K: {}",
            N,
            K,
        )
        return False

    if (w1.dtype != torch.float8_e4m3fn or w2.dtype != torch.float8_e4m3fn):
        log_once(
            "DEBUG",
            "CutlassBlockScaledGroupedGemm disabled: invalid weight dtype(s). "
            "w1.dtype: {}, w2.dtype: {}",
            w1.dtype,
            w2.dtype,
        )
        return False

    if expert_map is not None:
        log_once(
            "DEBUG",
            "CutlassBlockScaledGroupedGemm disabled: expert_parallel is"
            " not supported.")
        return False

    if activation != "silu":
        log_once(
            "DEBUG",
            "CutlassBlockScaledGroupedGemm disabled: only activation silu is"
            " supported.")
        return False

    if apply_router_weight_on_input:
        log_once(
            "DEBUG",
            "CutlassBlockScaledGroupedGemm disabled: "
            "apply_router_weight_on_input is not supported.",
        )
        return False

    if inplace:
        log_once(
            "DEBUG",
            "CutlassBlockScaledGroupedGemm disabled: inplace is not supported.",
        )
        return False

    return True


def run_cutlass_block_scaled_fused_experts(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    w1_scale: torch.Tensor,
    w2_scale: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
) -> torch.Tensor:
    w1_q = w1.transpose(1, 2)
    w2_q = w2.transpose(1, 2)
    w1_scale = w1_scale.transpose(1, 2)
    w2_scale = w2_scale.transpose(1, 2)

    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
    assert a.shape[0] == topk_ids.shape[
        0], "a and topk_ids must have the same batch size"
    assert w1_q.dtype == torch.float8_e4m3fn, "w1_q must be float8_e4m3fn"
    assert w2_q.dtype == torch.float8_e4m3fn, "w2_q must be float8_e4m3fn"
    assert a.shape[1] == w1_q.shape[1], "Hidden size mismatch w1"
    assert w1_q.shape[2] == w2_q.shape[1] * 2, "Hidden size mismatch w2"
    assert w1_q.shape[0] == w2_q.shape[0], "Expert number mismatch"
    assert w1_q.shape[0] == w1_scale.shape[
        0], "w1_scale expert number mismatch"
    assert w1_q.shape[0] == w2_scale.shape[
        0], "w2_scale expert number mismatch"
    assert a.dtype in [torch.half, torch.bfloat16], "Invalid output dtype"

    out_dtype = a.dtype
    num_experts = w1_q.size(0)
    m = a.size(0)
    k = w1_q.size(1)
    n = w2_q.size(1)

    expert_offsets = torch.empty((num_experts + 1, ),
                                 dtype=torch.int32,
                                 device="cuda")
    problem_sizes1 = torch.empty((num_experts, 3),
                                 dtype=torch.int32,
                                 device="cuda")
    problem_sizes2 = torch.empty((num_experts, 3),
                                 dtype=torch.int32,
                                 device="cuda")

    topk = topk_ids.size(1)

    a_q, a1_scale = _fp8_quantize(a,
                                  A_scale=None,
                                  per_act_token=False,
                                  block_shape=[128, 128])
    device = a_q.device

    a_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)
    c_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)

    ops.get_cutlass_moe_mm_data(
        topk_ids,
        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        a_map,
        c_map,
        num_experts,
        n,
        k,
    )

    rep_a_q = a_q.view(dtype=torch.uint8)[a_map].view(dtype=a_q.dtype)
    rep_a1_scales = a1_scale[a_map]

    c1 = torch.empty((m * topk, n * 2), dtype=out_dtype, device=device)
    c2 = torch.empty((m * topk, k), dtype=out_dtype, device=device)

    ops.cutlass_blockwise_scaled_grouped_mm(
        c1,
        rep_a_q,
        w1_q,
        rep_a1_scales,
        w1_scale,
        problem_sizes1,
        expert_offsets[:-1],
    )

    intermediate = torch.empty((m * topk, n), dtype=out_dtype, device=device)
    torch.ops._C.silu_and_mul(intermediate, c1)

    intermediate_q, a2_scale = _fp8_quantize(intermediate,
                                             A_scale=None,
                                             per_act_token=False,
                                             block_shape=[128, 128])

    ops.cutlass_blockwise_scaled_grouped_mm(
        c2,
        intermediate_q,
        w2_q,
        a2_scale,
        w2_scale,
        problem_sizes2,
        expert_offsets[:-1],
    )

    return (c2[c_map].view(m, topk, k) *
            topk_weights.view(m, topk, 1).to(out_dtype)).sum(dim=1)
