_custom_ops.py 95.2 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import TYPE_CHECKING, Literal
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import torch

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import vllm.envs as envs
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from vllm.scalar_type import ScalarType
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from vllm.utils.flashinfer import (
    flashinfer_quant_nvfp4_8x4_sf_layout,
)
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from vllm.utils.math_utils import cdiv
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logger = init_logger(__name__)

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current_platform.import_kernels()
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if TYPE_CHECKING:
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    def register_fake(fn):
        return lambda name: fn
else:
    try:
        from torch.library import register_fake
    except ImportError:
        from torch.library import impl_abstract as register_fake

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# page attention ops
def paged_attention_v1(
    out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
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    seq_lens: torch.Tensor,
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    block_size: int,
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    max_seq_len: int,
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    alibi_slopes: torch.Tensor | None,
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    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
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    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
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) -> None:
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    torch.ops._C.paged_attention_v1(
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        out,
        query,
        key_cache,
        value_cache,
        num_kv_heads,
        scale,
        block_tables,
        seq_lens,
        block_size,
        max_seq_len,
        alibi_slopes,
        kv_cache_dtype,
        k_scale,
        v_scale,
        tp_rank,
        blocksparse_local_blocks,
        blocksparse_vert_stride,
        blocksparse_block_size,
        blocksparse_head_sliding_step,
    )
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def paged_attention_v2(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
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    seq_lens: torch.Tensor,
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    block_size: int,
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    max_seq_len: int,
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    alibi_slopes: torch.Tensor | None,
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    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
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    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
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) -> None:
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    torch.ops._C.paged_attention_v2(
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        out,
        exp_sum,
        max_logits,
        tmp_out,
        query,
        key_cache,
        value_cache,
        num_kv_heads,
        scale,
        block_tables,
        seq_lens,
        block_size,
        max_seq_len,
        alibi_slopes,
        kv_cache_dtype,
        k_scale,
        v_scale,
        tp_rank,
        blocksparse_local_blocks,
        blocksparse_vert_stride,
        blocksparse_block_size,
        blocksparse_head_sliding_step,
    )
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def paged_attention_rocm(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
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    query_start_loc: torch.Tensor | None,
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    block_size: int,
    max_seq_len: int,
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    alibi_slopes: torch.Tensor | None,
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    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
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    fp8_out_scale: torch.Tensor | None = None,
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    mfma_type: str = "fp8" if envs.VLLM_ROCM_FP8_MFMA_PAGE_ATTN else "f16",
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) -> None:
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    torch.ops._rocm_C.paged_attention(
        out,
        exp_sum,
        max_logits,
        tmp_out,
        query,
        key_cache,
        value_cache,
        num_kv_heads,
        scale,
        block_tables,
        seq_lens,
        query_start_loc,
        block_size,
        max_seq_len,
        alibi_slopes,
        kv_cache_dtype,
        k_scale,
        v_scale,
        fp8_out_scale,
        mfma_type,
    )
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def mla_decode_kvcache_cpu(
    out: torch.Tensor,
    query: torch.Tensor,
    kv_cache: torch.Tensor,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
) -> None:
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    torch.ops._C_cpu.mla_decode_kvcache(
        out, query, kv_cache, scale, block_tables, seq_lens
    )
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# merge attn states ops
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def merge_attn_states(
    output: torch.Tensor,
    prefix_output: torch.Tensor,
    prefix_lse: torch.Tensor,
    suffix_output: torch.Tensor,
    suffix_lse: torch.Tensor,
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    output_lse: torch.Tensor | None = None,
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) -> None:
    torch.ops._C.merge_attn_states(
        output, output_lse, prefix_output, prefix_lse, suffix_output, suffix_lse
    )
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def convert_vertical_slash_indexes(
    q_seqlens: torch.Tensor,  # [BATCH, ]
    kv_seqlens: torch.Tensor,  # [BATCH, ]
    vertical_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
    slash_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
    context_size: int,
    block_size_M: int,
    block_size_N: int,
    causal: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    batch_size = slash_indexes.size(0)
    num_heads = slash_indexes.size(1)
    nnz_slash = slash_indexes.size(2)
    nnz_vertical = vertical_indexes.size(2)
    num_rows = (context_size + block_size_M - 1) // block_size_M

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    block_count = torch.zeros(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    block_offset = torch.zeros(
        batch_size,
        num_heads,
        num_rows,
        nnz_slash,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
    column_count = torch.zeros(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    column_index = torch.zeros(
        batch_size,
        num_heads,
        num_rows,
        nnz_vertical,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
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    torch.ops._C.convert_vertical_slash_indexes(
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        block_count,
        block_offset,
        column_count,
        column_index,
        q_seqlens,
        kv_seqlens,
        vertical_indexes,
        slash_indexes,
        context_size,
        block_size_M,
        block_size_N,
        causal,
    )
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    return block_count, block_offset, column_count, column_index


def convert_vertical_slash_indexes_mergehead(
    q_seqlens: torch.Tensor,  # [BATCH, ]
    kv_seqlens: torch.Tensor,  # [BATCH, ]
    vertical_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
    slash_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
    # [N_HEADS] : different head use different number of indices
    vertical_indices_count: torch.Tensor,
    slash_indices_count: torch.Tensor,
    context_size: int,
    block_size_M: int,
    block_size_N: int,
    causal: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    batch_size = slash_indexes.size(0)
    num_heads = slash_indexes.size(1)
    nnz_slash = slash_indexes.size(2)
    nnz_vertical = vertical_indexes.size(2)
    num_rows = (context_size + block_size_M - 1) // block_size_M

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    block_count = torch.empty(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    block_offset = torch.empty(
        batch_size,
        num_heads,
        num_rows,
        nnz_slash,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
    column_count = torch.empty(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    column_index = torch.empty(
        batch_size,
        num_heads,
        num_rows,
        nnz_vertical,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
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    torch.ops._C.convert_vertical_slash_indexes_mergehead(
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        block_count,
        block_offset,
        column_count,
        column_index,
        q_seqlens,
        kv_seqlens,
        vertical_indexes,
        slash_indexes,
        vertical_indices_count,
        slash_indices_count,
        context_size,
        block_size_M,
        block_size_N,
        causal,
    )
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    return block_count, block_offset, column_count, column_index


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# pos encoding ops
def rotary_embedding(
    positions: torch.Tensor,
    query: torch.Tensor,
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    key: torch.Tensor | None,
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    head_size: int,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
) -> None:
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    torch.ops._C.rotary_embedding(
        positions, query, key, head_size, cos_sin_cache, is_neox
    )
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# layer norm ops
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def rms_norm(
    out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, epsilon: float
) -> None:
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    torch.ops._C.rms_norm(out, input, weight, epsilon)
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def fused_add_rms_norm(
    input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, epsilon: float
) -> None:
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    torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)
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def fused_qk_norm_rope(
    qkv: torch.Tensor,
    num_heads_q: int,
    num_heads_k: int,
    num_heads_v: int,
    head_dim: int,
    eps: float,
    q_weight: torch.Tensor,
    k_weight: torch.Tensor,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
    position_ids: torch.Tensor,
) -> None:
    torch.ops._C.fused_qk_norm_rope(
        qkv,
        num_heads_q,
        num_heads_k,
        num_heads_v,
        head_dim,
        eps,
        q_weight,
        k_weight,
        cos_sin_cache,
        is_neox,
        position_ids,
    )


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def apply_repetition_penalties_torch(
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    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
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    repetition_penalties = repetition_penalties.unsqueeze(dim=1).repeat(
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        1, logits.size(1)
    )
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    # If token appears in prompt or output, apply, otherwise use 1.0 for no-op.
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    penalties = torch.where(prompt_mask | output_mask, repetition_penalties, 1.0)
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    # If logits are positive, divide by penalty, otherwise multiply by penalty.
    scaling = torch.where(logits > 0, 1.0 / penalties, penalties)
    logits *= scaling


def apply_repetition_penalties_cuda(
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    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
    torch.ops._C.apply_repetition_penalties_(
        logits, prompt_mask, output_mask, repetition_penalties
    )
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def apply_repetition_penalties(
    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
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    """Apply repetition penalties to logits in-place.

    Args:
        logits: The logits tensor of shape [num_seqs, vocab_size].
        prompt_mask: A boolean tensor indicating which tokens appear in the prompt.
        output_mask: A boolean tensor indicating which tokens appear in the output.
        repetition_penalties: The repetition penalties of shape (num_seqs, ).
    """
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    if logits.is_cuda and logits.is_contiguous():
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        apply_repetition_penalties_cuda(
            logits, prompt_mask, output_mask, repetition_penalties
        )
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    else:
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        apply_repetition_penalties_torch(
            logits, prompt_mask, output_mask, repetition_penalties
        )
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# fused quant layer norm ops
def rms_norm_dynamic_per_token_quant(
    input: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
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    scale_ub: torch.Tensor | None = None,
    residual: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    output = torch.empty_like(input, dtype=quant_dtype)
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    scales = torch.empty(
        (input.numel() // input.shape[-1], 1), device=input.device, dtype=torch.float32
    )
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    torch.ops._C.rms_norm_dynamic_per_token_quant(
        output, input, weight, scales, epsilon, scale_ub, residual
    )
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    return output, scales


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# fused quant layer norm ops blocked
def rms_norm_per_block_quant(
    input: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
    group_size: list[int],
    scale_ub: torch.Tensor | None = None,
    residual: torch.Tensor | None = None,
    is_scale_transposed: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    assert len(group_size) == 2
    output = torch.empty_like(input, dtype=quant_dtype)
    if is_scale_transposed:
        scales = torch.empty(
            (input.shape[-1] // group_size[1], input.numel() // input.shape[-1]),
            device=input.device,
            dtype=torch.float32,
        ).transpose(0, 1)
    else:
        scales = torch.empty(
            (input.numel() // input.shape[-1], input.shape[-1] // group_size[1]),
            device=input.device,
            dtype=torch.float32,
        )

    torch.ops._C.rms_norm_per_block_quant(
        output,
        input,
        weight,
        scales,
        epsilon,
        scale_ub,
        residual,
        group_size[1],
        is_scale_transposed,
    )
    return output, scales


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# quantization ops
# awq
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def awq_dequantize(
    qweight: torch.Tensor,
    scales: torch.Tensor,
    zeros: torch.Tensor,
    split_k_iters: int,
    thx: int,
    thy: int,
) -> torch.Tensor:
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    if envs.VLLM_USE_TRITON_AWQ:
        from vllm.model_executor.layers.quantization.awq_triton import (
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            awq_dequantize_triton,
        )

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        return awq_dequantize_triton(qweight, scales, zeros)
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    return torch.ops._C.awq_dequantize(qweight, scales, zeros, split_k_iters, thx, thy)
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def awq_gemm(
    input: torch.Tensor,
    qweight: torch.Tensor,
    scales: torch.Tensor,
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    qzeros: torch.Tensor,
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    split_k_iters: int,
) -> torch.Tensor:
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    if envs.VLLM_USE_TRITON_AWQ:
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        from vllm.model_executor.layers.quantization.awq_triton import awq_gemm_triton

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        return awq_gemm_triton(input, qweight, scales, qzeros, split_k_iters)
    return torch.ops._C.awq_gemm(input, qweight, scales, qzeros, split_k_iters)
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# gptq
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def gptq_gemm(
    a: torch.Tensor,
    b_q_weight: torch.Tensor,
    b_gptq_qzeros: torch.Tensor,
    b_gptq_scales: torch.Tensor,
    b_g_idx: torch.Tensor,
    use_exllama: bool,
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    use_v2_format: bool,
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    bit: int,
) -> torch.Tensor:
    return torch.ops._C.gptq_gemm(
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        a,
        b_q_weight,
        b_gptq_qzeros,
        b_gptq_scales,
        b_g_idx,
        use_exllama,
        use_v2_format,
        bit,
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    )
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if hasattr(torch.ops._C, "gptq_gemm"):
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    @register_fake("_C::gptq_gemm")
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    def _gptq_gemm_fake(
        a: torch.Tensor,
        b_q_weight: torch.Tensor,
        b_gptq_qzeros: torch.Tensor,
        b_gptq_scales: torch.Tensor,
        b_g_idx: torch.Tensor,
        use_exllama: bool,
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        use_v2_format: bool,
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        bit: int,
    ) -> torch.Tensor:
        return torch.empty(
            (a.size(0), b_q_weight.size(1)), dtype=a.dtype, device=a.device
        )
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def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor, bit: int) -> None:
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    torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
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# marlin_24
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def gptq_marlin_24_gemm(
    a: torch.Tensor,
    b_q_weight: torch.Tensor,
    b_meta: torch.Tensor,
    b_scales: torch.Tensor,
    workspace: torch.Tensor,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_24_gemm(
        a, b_q_weight, b_meta, b_scales, workspace, b_q_type.id, size_m, size_n, size_k
    )
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if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
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    @register_fake("_C::gptq_marlin_24_gemm")
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    def _gptq_marlin_24_gemm_fake(
        a: torch.Tensor,
        b_q_weight: torch.Tensor,
        b_meta: torch.Tensor,
        b_scales: torch.Tensor,
        workspace: torch.Tensor,
        b_q_type: ScalarType,
        size_m: torch.SymInt,
        size_n: torch.SymInt,
        size_k: torch.SymInt,
    ) -> torch.Tensor:
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        return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)

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    @register_fake("_C::marlin_gemm")
    def _marlin_gemm_fake(
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        a: torch.Tensor,
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        c: torch.Tensor | None,
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        b_q_weight: torch.Tensor,
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        b_bias: torch.Tensor | None,
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        b_scales: torch.Tensor,
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        a_scales: torch.Tensor | None,
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        global_scale: torch.Tensor | None,
        b_zeros: torch.Tensor | None,
        g_idx: torch.Tensor | None,
        perm: torch.Tensor | None,
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        workspace: torch.Tensor,
        b_q_type_id: int,
        size_m: torch.SymInt,
        size_n: torch.SymInt,
        size_k: torch.SymInt,
        is_k_full: bool = True,
        use_atomic_add: bool = False,
        use_fp32_reduce: bool = False,
        is_zp_float: bool = False,
    ) -> torch.Tensor:
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        dtype = a.dtype
        if dtype not in [torch.half, torch.bfloat16]:
            dtype = b_scales.dtype
        return torch.empty((size_m, size_n), device=a.device, dtype=dtype)
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    @register_fake("_C::awq_dequantize")
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    def _awq_dequantize_fake(
        qweight: torch.Tensor,
        scales: torch.Tensor,
        zeros: torch.Tensor,
        split_k_iters: torch.SymInt,
        thx: int,
        thy: int,
    ) -> torch.Tensor:
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        in_c = qweight.size(0)
        qout_c = qweight.size(1)
        out_c = qout_c * 8
633
        return torch.empty((in_c, out_c), dtype=scales.dtype, device=scales.device)
634

635
    @register_fake("_C::awq_gemm")
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    def _awq_gemm_fake(
        input: torch.Tensor,
        qweight: torch.Tensor,
        scales: torch.Tensor,
640
        qzeros: torch.Tensor,
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        split_k_iters: torch.SymInt,
    ) -> torch.Tensor:
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        num_in_feats = input.size(0)
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        return torch.empty(
            (split_k_iters, num_in_feats, qweight.size(1) * 8),
            dtype=input.dtype,
            device=input.device,
        ).sum(0)
649

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    @register_fake("_C::machete_mm")
    def machete_mm_fake(
652
        a: torch.Tensor,
653
        # b_q Should be the tensor returned by machete_prepack_B
654
        b_q: torch.Tensor,
655
        b_type: ScalarType,
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        out_type: torch.dtype | None = None,
        b_group_scales: torch.Tensor | None = None,
        b_group_zeros: torch.Tensor | None = None,
        b_group_size: int | None = None,
        b_channel_scales: torch.Tensor | None = None,
        a_token_scales: torch.Tensor | None = None,
        schedule: str | None = None,
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    ) -> torch.Tensor:
        m = a.size(0)
        n = b_q.size(1)
        return torch.empty((m, n), device=a.device, dtype=a.dtype)

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    @register_fake("_C::machete_prepack_B")
669
    def machete_prepack_B_fake(
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        b_q_weight: torch.Tensor,
        a_type: torch.dtype,
        b_type: ScalarType,
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        group_scales_type: torch.dtype | None,
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    ) -> torch.Tensor:
        return torch.empty_like(b_q_weight, memory_format=torch.contiguous_format)
676

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    @register_fake("_C::cutlass_w4a8_mm")
    def cutlass_w4a8_mm_fake(
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        a: torch.Tensor,
        # b_q Should be the tensor returned by cutlass_encode_and_reorder_int4b
        b_q: torch.Tensor,
        b_group_scales: torch.Tensor,
        b_group_size: int,
        b_channel_scales: torch.Tensor,
        a_token_scales: torch.Tensor,
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        out_type: torch.dtype | None = None,
        maybe_schedule: str | None = None,
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    ) -> torch.Tensor:
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        m = a.size(0)
        n = b_q.size(1)
        out_dtype = out_type if out_type is not None else torch.bfloat16
        return torch.empty((m, n), device=a.device, dtype=out_dtype)

    @register_fake("_C::cutlass_pack_scale_fp8")
    def cutlass_pack_scale_fp8_fake(scales: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(scales, memory_format=torch.contiguous_format)

    @register_fake("_C::cutlass_encode_and_reorder_int4b")
    def cutlass_encode_and_reorder_int4b_fake(b: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(b, memory_format=torch.contiguous_format)

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    @register_fake("_C::cutlass_encode_and_reorder_int4b_grouped")
    def cutlass_encode_and_reorder_int4b_grouped_fake(b: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(b, memory_format=torch.contiguous_format)

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if hasattr(torch.ops._C, "allspark_w8a16_gemm"):

    @register_fake("_C::allspark_w8a16_gemm")
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    def _allspark_w8a16_gemm_fake(
        a: torch.Tensor,
        b_qweight: torch.Tensor,
        b_scales: torch.Tensor,
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        b_qzeros: torch.Tensor | None,
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        n: torch.SymInt,
        group_size: torch.SymInt,
        sm_count: torch.SymInt,
        sm_version: torch.SymInt,
        CUBLAS_M_THRESHOLD: torch.SymInt,
        has_zp: bool,
        n32k16_reorder: bool,
    ) -> torch.Tensor:
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        m = a.size(0)
        return torch.empty((m, n), device=a.device, dtype=a.dtype)


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if hasattr(torch.ops._C, "ggml_dequantize"):

    @register_fake("_C::ggml_dequantize")
730
    def _ggml_dequantize_fake(
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        W: torch.Tensor,
        quant_type: int,
        m: torch.SymInt,
        n: torch.SymInt,
735
        dtype: torch.dtype | None = None,
736
    ) -> torch.Tensor:
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        return torch.empty((m, n), dtype=torch.float16, device=W.device)

    @register_fake("_C::ggml_mul_mat_vec_a8")
    def _ggml_mul_mat_vec_a8_fake(
        W: torch.Tensor,
        X: torch.Tensor,
        quant_type: int,
        row: torch.SymInt,
    ) -> torch.Tensor:
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        return torch.empty((X.shape[0], row), dtype=X.dtype, device=W.device)
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    @register_fake("_C::ggml_mul_mat_a8")
    def _ggml_mul_mat_a8_fake(
        W: torch.Tensor,
        X: torch.Tensor,
        quant_type: int,
        row: torch.SymInt,
    ) -> torch.Tensor:
        batch = X.size(0)
756
        return torch.empty((batch, row), dtype=X.dtype, device=W.device)
757

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    @register_fake("_C::ggml_moe_a8")
    def _ggml_moe_a8_fake(
        X: torch.Tensor,
        W: torch.Tensor,
        sorted_token_ids: torch.Tensor,
        expert_ids: torch.Tensor,
        num_tokens_post_padded: torch.Tensor,
        quant_type: int,
        row: torch.SymInt,
        top_k: torch.SymInt,
        tokens: torch.SymInt,
    ) -> torch.Tensor:
        tokens = X.size(0)
771
        return torch.empty((tokens * top_k, row), dtype=torch.float16, device=W.device)
772

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if hasattr(torch.ops._C, "ggml_moe_a8_vec"):

    @register_fake("_C::ggml_moe_a8_vec")
    def _ggml_moe_a8_vec_fake(
        X: torch.Tensor,
        W: torch.Tensor,
        topk_ids: torch.Tensor,
        top_k: int,
        quant_type: int,
        row: torch.SymInt,
        tokens: torch.SymInt,
    ) -> torch.Tensor:
        tokens = X.size(0)
787
        return torch.empty((tokens * top_k, row), dtype=X.dtype, device=W.device)
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789


790
# cutlass
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def cutlass_scaled_mm_supports_fp4(cuda_device_capability: int) -> bool:
    return torch.ops._C.cutlass_scaled_mm_supports_fp4(cuda_device_capability)


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def cutlass_scaled_fp4_mm(
    a: torch.Tensor,
    b: torch.Tensor,
    block_scale_a: torch.Tensor,
    block_scale_b: torch.Tensor,
    alpha: torch.Tensor,
    out_dtype: torch.dtype,
) -> torch.Tensor:
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    assert a.ndim == 2 and b.ndim == 2
    m, n = a.shape[0], b.shape[0]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)
806
    torch.ops._C.cutlass_scaled_fp4_mm(out, a, b, block_scale_a, block_scale_b, alpha)
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    return out


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def cutlass_scaled_mm_supports_fp8(cuda_device_capability: int) -> bool:
    return torch.ops._C.cutlass_scaled_mm_supports_fp8(cuda_device_capability)


814
def cutlass_scaled_mm_supports_block_fp8(cuda_device_capability: int) -> bool:
815
    return torch.ops._C.cutlass_scaled_mm_supports_block_fp8(cuda_device_capability)
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817


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def cutlass_scaled_mm(
    a: torch.Tensor,
    b: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
824
    bias: torch.Tensor | None = None,
825
) -> torch.Tensor:
826
    """
827
    `cutlass_scaled_mm` implements a fused version of
828
        `output = torch.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
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    where scale_a * a and scale_b * b are implemented using numpy-style
    broadcasting.

    In order to support blockwise scaling like found in DeepSeek V3 we also
    support extended "group" broadcast rules. We extend the numpy-style
    broadcasting rules with the following rule:
        "if the extent of a dimension in the source shape is between 1 and
        corresponding extent in the target shape we repeat each element along
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        that dimension  src_shape[dim] // target_shape[dim] times consecutively"
    example if we have:
          a = [[1, 2], and target_shape = (2, 4)
               [3, 4]]
    then we would expand a to:
          a = [[1, 1, 2, 2],
               [3, 3, 4, 4]]
    currently we only support the case:
        scale_a.shape * [1, 128] == a.shape
        scale_b.shape * [128, 128] == b.shape
    """
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    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.numel() == b.shape[1] and bias.dtype == out_dtype
850

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    # Massage the input to be 2D
    target_shape = (*a.shape[:-1], b.shape[1])
    a = a.view(-1, a.shape[-1])
854

855
    cutlass_compatible_b = b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
856
    if current_platform.is_rocm() or not cutlass_compatible_b:
857
        from vllm.model_executor.layers.quantization.compressed_tensors.triton_scaled_mm import (  # noqa
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            triton_scaled_mm,
        )

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        out = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    else:
863
        out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
864
        torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
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866
    return out.view(*target_shape)
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def cutlass_scaled_mm_azp(
    a: torch.Tensor,
    b: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
    azp_adj: torch.Tensor,
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    azp: torch.Tensor | None = None,
    bias: torch.Tensor | None = None,
878
) -> torch.Tensor:
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    """
    :param azp_adj: In the per-tensor case, this should include the azp.
    Always per-channel.
    :param azp: Only set in the per-token case. Per-token if set.
    """
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    assert b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.numel() == b.shape[1] and bias.dtype == out_dtype
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    # Massage the input to be 2D
    target_shape = (*a.shape[:-1], b.shape[1])
    a = a.view(-1, a.shape[-1])
    assert azp is None or azp.numel() == a.shape[0]
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    out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
    torch.ops._C.cutlass_scaled_mm_azp(out, a, b, scale_a, scale_b, azp_adj, azp, bias)
895
    return out.view(*target_shape)
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898
def cutlass_sparse_scaled_mm_supported(cuda_device_capability: int) -> bool:
899
    return torch.ops._C.cutlass_sparse_scaled_mm_supported(cuda_device_capability)
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902
def cutlass_group_gemm_supported(cuda_device_capability: int) -> bool:
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    try:
        return torch.ops._C.cutlass_group_gemm_supported(cuda_device_capability)
    except AttributeError:
        # Return False on non-CUDA platforms where it is not available
        return False
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910
def cutlass_sparse_compress(a: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    Compresses a sparse matrix for use with Cutlass sparse operations.

    This function takes a dense tensor and compresses it into two components:
    non-zero elements and metadata. The compressed representation is compatible
    with Cutlass sparse kernels.

    Args:
919
        a (torch.Tensor):
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            The input tensor to be compressed. Must have one of the following data types:
            - `torch.int8`
            - `torch.float8_e4m3fn`
            - `torch.bfloat16`
            - `torch.float16`

    Returns:
927
        tuple[torch.Tensor, torch.Tensor]:
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            A tuple containing:
            - `a_nzs` (torch.Tensor): A tensor containing non-zero elements of `a`.
            - `a_meta` (torch.Tensor): A tensor containing metadata for the sparse representation.

    Raises:
        ValueError: If the compression operation fails.

    Notes:
        - The `a_meta` tensor has a data type of `torch.uint8`.
        - Each metadata element encodes the sparsity of 4 non-zero elements (i.e., `elemsPerMetaElem = 4`).
        - The shape of `a_nzs` is `(m, k // 2)`, where `m` and `k` are the dimensions of the input tensor.
        - The shape of `a_meta` is `(m, k // 2 // elemsPerMetaElem)`.
    """
941
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    assert a.dtype in [torch.int8, torch.float8_e4m3fn, torch.bfloat16, torch.float16]
    assert a.is_contiguous()
943
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945

    # a_meta.dtype: torch.uint8 so elemsPerMetaElem = 8b / 2b_per_nz = 4
    elemsPerMetaElem = 4
946
    assert a.shape[1] % (2 * elemsPerMetaElem) == 0
947

948
    return torch.ops._C.cutlass_sparse_compress(a)
949
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951


def cutlass_scaled_sparse_mm(
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    a: torch.Tensor,
    bt_nzs: torch.Tensor,
    bt_meta: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
958
    bias: torch.Tensor | None = None,
959
) -> torch.Tensor:
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    """
    Performs a scaled sparse matrix multiplication using Cutlass.

    Steps:
    1. Create a dense matrix `a` of shape (m, k) on the CUDA device:
    `a = torch.randn((m, k), device='cuda')`.

    2. Create a dense matrix `b` of shape (k, n) on the CUDA device:
    `b = torch.randn((k, n), device='cuda')`.

    3. Prune matrix `b` to 2:4 sparsity along the specified dimension:
    `b = prune_to_2_4(b, dim=0)`.

    4. Compress the transposed sparse matrix `b.t()`:
    `bt_nzs, bt_meta = cutlass_sparse_compress(b.t())`.

    5. Perform sparse matrix multiplication using the compressed matrix,
    applying scaling factors for `a` and `b`, and the output data type:
    `out = cutlass_scaled_sparse_mm(a, bt_nzs, bt_meta, scale_a, scale_b, out_dtype)`.

    Returns:
    - The result of the scaled sparse matrix multiplication.
    """
983
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    assert bt_nzs.shape[0] % 16 == 0 and bt_nzs.shape[1] % 16 == 0
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.shape[0] == bt_nzs.shape[0] and bias.dtype == out_dtype
986
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990

    m = a.shape[0]
    n = bt_nzs.shape[0]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)

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    torch.ops._C.cutlass_scaled_sparse_mm(
        out, a, bt_nzs, bt_meta, scale_a, scale_b, bias
    )
994
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997

    return out


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def get_cutlass_moe_mm_data(
    topk_ids: torch.Tensor,
    expert_offsets: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    input_permutation: torch.Tensor,
    output_permutation: torch.Tensor,
    num_experts: int,
    n: int,
    k: int,
1008
    blockscale_offsets: torch.Tensor | None = None,
1009
):
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    """
    Prepare data necessary to perform CUTLASS grouped matrix multiplications
    used in CUTLASS-based fused MoE.

    The function takes in topk_ids (token-expert mapping) and uses it to
    compute:
    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation after the input is sorted with
                      input_permutation. The number of tokens computed with
                      expert E is expert_offsets[E + 1] - expert_offsets[E]
    - problem_sizes1, problem_sizes2: MxNxK sizes of each expert's
                                      multiplication in two grouped MMs used in
                                      the fused MoE operation.
    - input_permutation: Permutation that must be used to shuffle the input
                         before executing the MMs.
    - output_permutation: Permutation that must be used to shuffle the output
                          after executing the MMs.
1027
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1031
    - blockscale_offsets: Optional argument passed for fp4 moe. Indices that
                          mark at which block scale index each expert begins
                          its computation. The number of block scale rows
                          computed with expert E is blockscale_offsets[E + 1] -
                          blockscale_offsets[E]
1032
    """
1033
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1038
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1044
    return torch.ops._C.get_cutlass_moe_mm_data(
        topk_ids,
        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        input_permutation,
        output_permutation,
        num_experts,
        n,
        k,
        blockscale_offsets,
    )
1045
1046


1047
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1065
def get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
    expert_first_token_offset: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    n: int,
    k: int,
    swap_ab: bool,
):
    """Compute per-expert (M, N, K) problem sizes from expert_first_token_offset"""
    return torch.ops._C.get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
        expert_first_token_offset,
        problem_sizes1,
        problem_sizes2,
        n,
        k,
        swap_ab,
    )


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def shuffle_rows(input_tensor: torch.Tensor, dst2src_map: torch.Tensor):
    """
    Shuffle and expand the input tensor according to the dst2src_map and store the result in output_tensor.
    This is used in MoE to permute the input tensor before performing grouped matrix multiplications.
    """
    num_tokens_permuted = dst2src_map.shape[0]
1072
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1076
    output_tensor = torch.empty(
        (num_tokens_permuted, input_tensor.shape[1]),
        device=input_tensor.device,
        dtype=input_tensor.dtype,
    )
1077
1078
    torch.ops._moe_C.shuffle_rows(input_tensor, dst2src_map, output_tensor)
    return output_tensor
1079
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1081
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1090
def get_cutlass_pplx_moe_mm_data(
    expert_offsets: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    expert_num_tokens: torch.Tensor,
    num_local_experts: int,
    padded_m: int,
    n: int,
    k: int,
):
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1095
    """
    Prepare data necessary to perform CUTLASS grouped matrix multiplications
    used in CUTLASS-based fused MoE.

    The function takes in expert_num_tokens (token count per expert) and
1096
    non_zero_expert_idxs (consecutive indices of experts with non-zero token
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1104
    counts) and uses them to compute:
    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation.
    - problem_sizes1, problem_sizes2: MxNxK sizes of each expert's
                                      multiplication in two grouped MMs used in
                                      the fused MoE operation.
    """
    return torch.ops._C.get_cutlass_pplx_moe_mm_data(
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        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        expert_num_tokens,
        num_local_experts,
        padded_m,
        n,
        k,
    )
1114
1115


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def cutlass_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    expert_offsets: torch.Tensor,
    problem_sizes: torch.Tensor,
    a_strides: torch.Tensor,
    b_strides: torch.Tensor,
    c_strides: torch.Tensor,
    per_act_token: bool,
    per_out_ch: bool,
):
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1139
1140
    """
    A single grouped matrix multiplication used in CUTLASS-based fused MoE.
    The function executes fp8-quantized OUT = AB matrix multiplication.

    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation. The number of tokens computed with
                      expert E is expert_offsets[E + 1] - expert_offsets[E]
    - problem_sizes: MxNxK sizes of each expert's multiplication in two grouped
                     MMs used in the fused MoE operation.
    - a/b/c_strides: The data strides passed to grouped matrix multiplication.
    """
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    return torch.ops._C.cutlass_moe_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        expert_offsets,
        problem_sizes,
        a_strides,
        b_strides,
        c_strides,
        per_act_token,
        per_out_ch,
    )
1155
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def cutlass_fp4_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    alphas: torch.Tensor,
    problem_sizes: torch.Tensor,
    expert_offsets: torch.Tensor,
    sf_offsets: torch.Tensor,
):
1168
    """
1169
    An FP4 Blockscaled Group Gemm that takes in  a_tensors, b_tensors and runs
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    the gemms for each combination based on the specified problem sizes.

    This is used as the MoE gemm during NVFP4 Quantized FusedMoE forward.
    - a/b_tensors: the NVFP4 a_ptrs and b_ptrs tensors which are quantized
                     input and expert weights.
    - a_/b_scales: The blockscales in FP8-E4M3 precision
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    - expert_offsets/sf_offsets: Indices that mark at which token index
                    each expert begins its computation. The number of tokens
                    computed with expert E is expert_offsets[E + 1] -
                    expert_offsets[E] And the sf_size per expert is
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                    sf_offset[E+1] - sf_offset[E]
    - problem_sizes: MxNxK sizes of each expert's multiplication in two grouped
                     MMs used in the fused MoE operation.
    """
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    return torch.ops._C.cutlass_fp4_group_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        alphas,
        problem_sizes,
        expert_offsets,
        sf_offsets,
    )
1195
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1197
# gptq_marlin
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def gptq_marlin_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1204
    is_a_8bit: bool = False,
1205
) -> torch.Tensor:
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    return torch.ops._C.gptq_marlin_repack(
        b_q_weight, perm, size_k, size_n, num_bits, is_a_8bit
    )
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if hasattr(torch.ops._C, "gptq_marlin_repack"):

    @register_fake("_C::gptq_marlin_repack")
    def _gptq_marlin_repack_fake(
        b_q_weight: torch.Tensor,
        perm: torch.Tensor,
        size_k: torch.SymInt,
        size_n: torch.SymInt,
        num_bits: int,
1220
        is_a_8bit: bool = False,
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    ) -> torch.Tensor:
        pack_factor = 32 // num_bits
        marlin_tile_size = 16
        return torch.empty(
            (size_k // marlin_tile_size, size_n * marlin_tile_size // pack_factor),
            dtype=b_q_weight.dtype,
            device=b_q_weight.device,
        )


# awq_marlin
1232
def awq_marlin_repack(
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    b_q_weight: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
    is_a_8bit: bool = False,
1238
) -> torch.Tensor:
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    return torch.ops._C.awq_marlin_repack(
        b_q_weight, size_k, size_n, num_bits, is_a_8bit
    )
1242
1243


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if hasattr(torch.ops._C, "awq_marlin_repack"):

    @register_fake("_C::awq_marlin_repack")
    def _awq_marlin_repack_fake(
        b_q_weight: torch.Tensor,
        size_k: torch.SymInt,
        size_n: torch.SymInt,
        num_bits: int,
1252
        is_a_8bit: bool = False,
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    ) -> torch.Tensor:
        pack_factor = 32 // num_bits
        marlin_tile_size = 16
        return torch.empty(
            (size_k // marlin_tile_size, size_n * marlin_tile_size // pack_factor),
            dtype=b_q_weight.dtype,
            device=b_q_weight.device,
        )


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def gptq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1269
    is_a_8bit: bool = False,
1270
) -> torch.Tensor:
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    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
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    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
1278
    for e in range(num_experts):
1279
        output[e] = torch.ops._C.gptq_marlin_repack(
1280
            b_q_weight[e], perm[e], size_k, size_n, num_bits, is_a_8bit
1281
        )
1282
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1284
    return output


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def awq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1291
    is_a_8bit: bool = False,
1292
) -> torch.Tensor:
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    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
1295
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1299
    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
1300
    for e in range(num_experts):
1301
        output[e] = torch.ops._C.awq_marlin_repack(
1302
            b_q_weight[e], size_k, size_n, num_bits, is_a_8bit
1303
        )
1304
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    return output


1307
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1312
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def marlin_int4_fp8_preprocess(
    qweight: torch.Tensor,
    qzeros_or_none: torch.Tensor | None = None,
    inplace: bool = False,
):
    return torch.ops._C.marlin_int4_fp8_preprocess(qweight, qzeros_or_none, inplace)


1315
def marlin_gemm(
1316
    a: torch.Tensor,
1317
    c: torch.Tensor | None,
1318
    b_q_weight: torch.Tensor,
1319
    b_bias: torch.Tensor | None,
1320
    b_scales: torch.Tensor,
1321
    a_scales: torch.Tensor | None,
1322
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    global_scale: torch.Tensor | None,
    b_zeros: torch.Tensor | None,
    g_idx: torch.Tensor | None,
    perm: torch.Tensor | None,
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    workspace: torch.Tensor,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
    is_k_full: bool = True,
    use_atomic_add: bool = False,
    use_fp32_reduce: bool = False,
    is_zp_float: bool = False,
) -> torch.Tensor:
1336
    return torch.ops._C.marlin_gemm(
1337
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1341
        a,
        c,
        b_q_weight,
        b_bias,
        b_scales,
1342
        a_scales,
1343
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        global_scale,
        b_zeros,
        g_idx,
        perm,
        workspace,
        b_q_type.id,
        size_m,
        size_n,
        size_k,
        is_k_full,
        use_atomic_add,
        use_fp32_reduce,
        is_zp_float,
    )
1357
1358


1359
# machete
1360
def machete_supported_schedules(
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1362
    a_type: torch.dtype,
    b_type: ScalarType,
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1367
    group_scales_type: torch.dtype | None,
    group_zeros_type: torch.dtype | None = None,
    channel_scales_type: torch.dtype | None = None,
    token_scales_type: torch.dtype | None = None,
    out_type: torch.dtype | None = None,
1368
) -> list[str]:
1369
    return torch.ops._C.machete_supported_schedules(
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        a_type,
        b_type.id,
        group_scales_type,
        group_zeros_type,
        channel_scales_type,
        token_scales_type,
        out_type,
    )
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1380


def machete_mm(
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1384
    a: torch.Tensor,
    # b_q Should be the tensor returned by machete_prepack_B
    b_q: torch.Tensor,
    b_type: ScalarType,
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    out_type: torch.dtype | None = None,
    b_group_scales: torch.Tensor | None = None,
    b_group_zeros: torch.Tensor | None = None,
    b_group_size: int | None = None,
    b_channel_scales: torch.Tensor | None = None,
    a_token_scales: torch.Tensor | None = None,
    schedule: str | None = None,
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1400
1401
1402
1403
1404
) -> torch.Tensor:
    return torch.ops._C.machete_mm(
        a,
        b_q,
        b_type.id,
        out_type,
        b_group_scales,
        b_group_zeros,
        b_group_size,
        b_channel_scales,
        a_token_scales,
        schedule,
    )
1405
1406
1407


def machete_prepack_B(
1408
1409
1410
    b_q_weight: torch.Tensor,
    a_type: torch.dtype,
    b_type: ScalarType,
1411
    group_scales_type: torch.dtype | None,
1412
1413
1414
1415
) -> torch.Tensor:
    return torch.ops._C.machete_prepack_B(
        b_q_weight, a_type, b_type.id, group_scales_type
    )
1416
1417


1418
1419
# CUTLASS W4A8
def cutlass_w4a8_mm(
1420
1421
1422
1423
1424
1425
1426
    a: torch.Tensor,
    # b_q Should be the tensor returned by cutlass_encode_and_reorder_int4b
    b_q: torch.Tensor,
    b_group_scales: torch.Tensor,
    b_group_size: int,
    b_channel_scales: torch.Tensor,
    a_token_scales: torch.Tensor,
1427
1428
    out_type: torch.dtype | None = None,
    maybe_schedule: str | None = None,
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
) -> torch.Tensor:
    return torch.ops._C.cutlass_w4a8_mm(
        a,
        b_q,
        b_group_scales,
        b_group_size,
        b_channel_scales,
        a_token_scales,
        out_type,
        maybe_schedule,
    )
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449


def cutlass_pack_scale_fp8(scales: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.cutlass_pack_scale_fp8(scales)


def cutlass_encode_and_reorder_int4b(b: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.cutlass_encode_and_reorder_int4b(b)


1450
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1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
def cutlass_w4a8_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    b_group_scales: torch.Tensor,
    b_group_size: int,
    expert_offsets: torch.Tensor,
    problem_sizes: torch.Tensor,
    a_strides: torch.Tensor,
    b_strides: torch.Tensor,
    c_strides: torch.Tensor,
    group_scale_strides: torch.Tensor,
    maybe_schedule: str | None = None,
):
    """
    Executes the CUTLASS-based fused-MoE grouped matrix multiplication for the
    W4A8 quantization scheme. Uses group-wise quantization (INT4 -> FP8)
    and both per-channel + per-token scaling in the epilogue.

    Args:
        out_tensors:
            Output buffer for all experts (updated in-place).
        a_tensors:
            FP8 (E4M3FN) activations for all experts.
        b_tensors:
            INT4-packed weight matrix for all experts, packed to INT32
        a_scales:
            Per-token FP8 activation scales, applied in the epilogue.
        b_scales:
            Per-channel FP8 weight scales for each expert, applied in the epilogue.
        b_group_scales:
            FP8 scale values for group-wise INT4 weight blocks.
        b_group_size:
            Number of elements grouped under each entry of b_group_scales.
        expert_offsets:
            Cumulative token offsets
        problem_sizes:
            Per-expert (M, N, K) GEMM sizes used by the grouped GEMM launcher.
        a/b/c/group_scale_strides:
            Strides describing the memory layout of the input tensors.
        maybe_schedule:
            Optional override to choose a specific kernel or epilogue schedule.

    Returns:
        out_tensors updated in-place with the dequantized INT4xFP8 grouped GEMM result.
    """
    return torch.ops._C.cutlass_w4a8_moe_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        b_group_scales,
        b_group_size,
        expert_offsets,
        problem_sizes,
        a_strides,
        b_strides,
        c_strides,
        group_scale_strides,
        maybe_schedule,
    )


def cutlass_encode_and_reorder_int4b_grouped(
    b_tensors: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    return torch.ops._C.cutlass_encode_and_reorder_int4b_grouped(b_tensors)


1522
if hasattr(torch.ops._C, "permute_cols"):
1523

1524
    @register_fake("_C::permute_cols")
1525
    def _permute_cols_fake(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
1526
1527
1528
1529
1530
1531
1532
        return torch.empty_like(a)


def permute_cols(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.permute_cols(a, perm)


1533
1534
# fp4
def scaled_fp4_quant(
1535
1536
    input: torch.Tensor,
    input_global_scale: torch.Tensor,
1537
    is_sf_swizzled_layout: bool = True,
1538
    backend: str = "none",
1539
) -> tuple[torch.Tensor, torch.Tensor]:
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
    """
    Quantize input tensor to FP4 and return quantized tensor and scale.

    This function quantizes the last dimension of the given tensor `input`. For
    every 16 consecutive elements, a single dynamically computed scaling factor
    is shared. This scaling factor is quantized using the `input_global_scale`
    and is stored in a swizzled layout (see
    https://docs.nvidia.com/cuda/parallel-thread-execution/#tcgen05-mma-scale-factor-b-layout-4x).

    Args:
        input: The input tensor to be quantized to FP4
        input_global_scale: A scalar scaling factor for the entire tensor.
1552
        use_8x4_sf_layout: Whether to use the 8x4 or 128x4 layout for the scaling
1553
1554

    Returns:
1555
        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP4 but every
1556
1557
1558
            two values are packed into a uint8 and float8_e4m3 scaling factors
            in the sizzled layout.
    """
1559
    assert not current_platform.is_rocm()
1560
    assert input.ndim >= 1, f"input.ndim needs to be >= 1, but got {input.ndim}."
1561
1562
1563
1564
1565
1566
    other_dims = 1 if input.ndim == 1 else -1
    input = input.reshape(other_dims, input.shape[-1])
    m, n = input.shape
    block_size = 16
    device = input.device

1567
    assert n % block_size == 0, f"last dim has to be multiple of 16, but got {n}."
1568
    assert input.dtype in (torch.float16, torch.bfloat16), (
1569
1570
        f"input.dtype needs to be fp16 or bf16 but got {input.dtype}."
    )
1571

1572
1573
1574
1575
1576
1577
1578
1579
1580
    use_8x4_sf_layout = True if "trtllm" in backend and m <= 32 else False  # noqa: SIM210

    if use_8x4_sf_layout:
        output, output_scale = flashinfer_quant_nvfp4_8x4_sf_layout(
            input, input_global_scale
        )
    else:
        # Two fp4 values will be packed into an uint8.
        output = torch.empty((m, n // 2), device=device, dtype=torch.uint8)
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
        if is_sf_swizzled_layout:
            # We use the rounded values to store the swizzled values. Due to the
            # requirement of the Tensor Core, the minimum tile is 128x4 for the scales.
            # So, we first pad the scales to multiples of 128 and 4. Then, the scales
            # (in float8_e4m3fn) are packed into an int32 for every 4 values. More:
            # https://docs.nvidia.com/cuda/parallel-thread-execution/#tcgen05-mma-scale-factor-b-layout-4x
            round_up = lambda x, y: (x + y - 1) // y * y
            rounded_m = round_up(m, 128)
            scale_n = n // block_size
            rounded_n = round_up(scale_n, 4)
            output_scale = torch.empty(
                (rounded_m, rounded_n // 4), device=device, dtype=torch.int32
            )
        else:
            output_scale = torch.empty((m, n // 16), device=device, dtype=torch.uint8)

        torch.ops._C.scaled_fp4_quant(
            output, input, output_scale, input_global_scale, is_sf_swizzled_layout
1599
1600
        )

1601
1602
1603
1604
    output_scale = output_scale.view(torch.float8_e4m3fn)
    return output, output_scale


1605
1606
1607
1608
1609
1610
1611
1612
def scaled_fp4_experts_quant(
    input_tensor: torch.Tensor,
    input_global_scale: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
1613
    Quantize input tensor to NVFP4 and return quantized tensor and scale, for
1614
1615
    packed MoE Inputs.
    Args:
1616
        input_tensor: The input tensor to be quantized to NVFP4
1617
1618
1619
1620
        input_global_scale: A scalar scaling factor for the entire tensor.
        expert_offsets: The expert offsets tensor
        blockscale_offsets: The blockscale offsets tensor
    Outputs:
1621
        output: The quantized tensor in NVFP4
1622
1623
1624
1625
        output_scales: The blockscale tensor in FP8-E4M3
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2, (
1626
1627
        f"input.ndim needs to be == 2, but got {input_tensor.ndim}."
    )
1628

1629
1630
1631
1632
1633
    # Control the maximum number of tokens per expert supported by the
    # NVFP4 MoE Expert Quantization. This is used to prevent the kernel
    # from running out of memory. This value can also be increased to support
    # larger models.
    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
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    m_numtopk, k = input_tensor.shape

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    assert m_numtopk <= MAX_TOKENS_PER_EXPERT * topk, (
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        f"m_numtopk must be less than MAX_TOKENS_PER_EXPERT("
        f"{MAX_TOKENS_PER_EXPERT})"
        f" for cutlass_moe_fp4, observed m_numtopk = {m_numtopk}. Use"
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        f" VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE to set this value."
    )
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    scales_k = k // 16
    padded_k = (scales_k + (4 - 1)) // 4

    # output is uint8 and packed fp4 values
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    output = torch.empty(
        m_numtopk, k // 2, device=input_tensor.device, dtype=torch.uint8
    )
    output_scales = torch.empty(
        MAX_TOKENS_PER_EXPERT * topk,
        padded_k,
        dtype=torch.int32,
        device=input_tensor.device,
    )
    torch.ops._C.scaled_fp4_experts_quant(
        output,
        output_scales,
        input_tensor,
        input_global_scale,
        expert_offsets,
        blockscale_offsets,
    )
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    output_scales = output_scales.view(torch.float8_e4m3fn)
    return output, output_scales


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def silu_and_mul_scaled_fp4_experts_quant(
    input_tensor: torch.Tensor,
    input_global_scale: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Fused SiLU+Mul+NVFP4 quantization for MoE intermediate activations.

    Args:
        input_tensor: The input tensor with gate || up layout [m_topk, k*2]
        input_global_scale: A per-expert scaling factor [n_experts]
        expert_offsets: The expert offsets tensor [n_experts+1]
        blockscale_offsets: The blockscale offsets tensor [n_experts+1]
        topk: Number of top-k experts selected
    Outputs:
        output: The quantized tensor in NVFP4 [m_topk, k/2]
        output_scales: The blockscale tensor in FP8-E4M3
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2, (
        f"input.ndim needs to be == 2, but got {input_tensor.ndim}."
    )

    # Control the maximum number of tokens per expert supported by the
    # NVFP4 MoE Expert Quantization. This is used to prevent the kernel
    # from running out of memory. This value can also be increased to support
    # larger models.
    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
    m_numtopk, k_times_2 = input_tensor.shape
    assert k_times_2 % 2 == 0, "input width must be even (gate || up layout)"
    k = k_times_2 // 2

    assert m_numtopk <= MAX_TOKENS_PER_EXPERT * topk, (
        f"m_numtopk must be less than MAX_TOKENS_PER_EXPERT("
        f"{MAX_TOKENS_PER_EXPERT})"
        f" for cutlass_moe_fp4, observed m_numtopk = {m_numtopk}. Use"
        f" VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE to set this value."
    )
    scales_k = k // 16
    padded_k = (scales_k + (4 - 1)) // 4

    # output is uint8 and packed fp4 values
    output = torch.empty(
        m_numtopk, k // 2, device=input_tensor.device, dtype=torch.uint8
    )
    output_scales = torch.empty(
        MAX_TOKENS_PER_EXPERT * topk,
        padded_k,
        dtype=torch.int32,
        device=input_tensor.device,
    )
    torch.ops._C.silu_and_mul_scaled_fp4_experts_quant(
        output,
        output_scales,
        input_tensor,
        input_global_scale,
        expert_offsets,
        blockscale_offsets,
    )
    output_scales = output_scales.view(torch.float8_e4m3fn)
    return output, output_scales


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# fp8
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def scaled_fp8_quant(
    input: torch.Tensor,
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    scale: torch.Tensor | None = None,
    num_token_padding: int | None = None,
    scale_ub: torch.Tensor | None = None,
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    use_per_token_if_dynamic: bool = False,
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    output: torch.Tensor | None = None,
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    group_shape: tuple[int, int] | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    Quantize input tensor to FP8 and return quantized tensor and scale.

    This function supports both static and dynamic quantization: If you
    provide the scale, it will use static scaling and if you omit it,
    the scale will be determined dynamically. The function also allows
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    optional padding of the output tensors for downstream kernels that
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    will benefit from padding.

    Args:
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        input: The input tensor to be quantized to FP8 (must be 2D: [M, N])
        scale: Optional scaling factor for the FP8 quantization. Supports:
            - 0D or [1]: per-tensor scaling
            - 1D: requires explicit group_shape to disambiguate per-channel
              vs per-token (use (-1, 1) for per-channel, (1, -1) for per-token)
            - 2D [M/group_m, N/group_n]: group scaling (e.g. [M, N/128] for
              DeepSeek-style (1,128) groups, or [M/128, N/128] for (128,128))
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        scale_ub: Optional upper bound for scaling factor in dynamic
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            per token case
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        num_token_padding: If specified, pad the first dimension
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            of the output to at least this value.
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        use_per_token_if_dynamic: Whether to do per_tensor or per_token
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            in the dynamic quantization case.
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        group_shape: Optional tuple (group_m, group_n) specifying the group
            shape for static quantization. Use -1 for "full extent" (e.g.,
            (-1, -1) for per-tensor, (-1, 1) for per-channel, etc.)
            Required for 1D scales; optional for 2D scales.
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    Returns:
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        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
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            scaling factor.
    """
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    # This code assumes batch_dim and num_tokens are flattened
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    assert input.ndim == 2
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    shape: tuple[int, int] | torch.Size = input.shape
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    # For ROCm on MI300, the output fp8 dtype is torch.float_e3m3fnuz
    out_dtype: torch.dtype = current_platform.fp8_dtype()
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    if num_token_padding:
        shape = (max(num_token_padding, input.shape[0]), shape[1])
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    if output is None:
        output = torch.empty(shape, device=input.device, dtype=out_dtype)
    else:
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        assert num_token_padding is None, "padding not supported if output passed in"
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        assert output.dtype == out_dtype
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    if scale is None:
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        if use_per_token_if_dynamic:
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            scale = torch.empty((shape[0], 1), device=input.device, dtype=torch.float32)
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            torch.ops._C.dynamic_per_token_scaled_fp8_quant(
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                output, input, scale, scale_ub
            )
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        else:
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            scale = torch.empty(1, device=input.device, dtype=torch.float32)
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            torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
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    else:
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        torch.ops._C.static_scaled_fp8_quant(output, input, scale, group_shape)
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    return output, scale
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# gptq allspark
def allspark_repack_weight(
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    qweight: torch.Tensor,
    scale: torch.Tensor,
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    zero_point: torch.Tensor | None = None,
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    has_zp: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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    """
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    Rearrange qweight, scale, and zero_point(if asymmetric) to n32k16 format
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    for Ampere W8A16 Fused Gemm kernel

    Args:
        qweight: uint8 weight tensor, original k x n format.
        scale: fp16/bf16 weight scale tensor, 1 x n format.
        zero_point: fp16/bf16 weight zero_point tensor, 1 x n format.
            Must be provided for asymmetric quantization.
        has_zp: if use symmetric quantization, has_zp = False.
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            if use asymmetric quantization, has_zp = True.

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    Returns:
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        tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] :
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            rearranged weight, scale, and optionally zero_point.
    """
    K = qweight.shape[0]
    N = qweight.shape[1]
    N_32align = (N + 32 - 1) // 32 * 32

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    qweight_reorder = torch.empty(
        (N_32align, K), device=qweight.device, dtype=qweight.dtype
    )
    scale_reorder = torch.empty((1, N_32align), device=scale.device, dtype=scale.dtype)
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    zero_point_reorder = None
    if has_zp:
        assert zero_point is not None, (
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            "zero_point must be provided for asymmetric quantization."
        )
        zero_point_reorder = torch.empty(
            (1, N_32align), device=zero_point.device, dtype=zero_point.dtype
        )
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    torch.ops._C.rearrange_kn_weight_as_n32k16_order(
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        qweight,
        scale,
        zero_point,
        has_zp,
        qweight_reorder,
        scale_reorder,
        zero_point_reorder,
        K,
        N,
        N_32align,
    )
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    return qweight_reorder, scale_reorder, zero_point_reorder


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def allspark_w8a16_gemm(
    a: torch.Tensor,
    b_qweight: torch.Tensor,
    b_scales: torch.Tensor,
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    b_qzeros: torch.Tensor | None,
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    n: int,
    group_size: int,
    sm_count: int,
    sm_version: int,
    CUBLAS_M_THRESHOLD: int,
    has_zp: bool,
    n32k16_reorder: bool,
) -> torch.Tensor:
    return torch.ops._C.allspark_w8a16_gemm(
        a,
        b_qweight,
        b_scales,
        b_qzeros,
        n,
        group_size,
        sm_count,
        sm_version,
        CUBLAS_M_THRESHOLD,
        has_zp,
        n32k16_reorder,
    )
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# int8
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def scaled_int8_quant(
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    input: torch.Tensor,
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    scale: torch.Tensor | None = None,
    azp: torch.Tensor | None = None,
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    symmetric: bool = True,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
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    """
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    Args:
        input: The input tensor to be quantized to int8.
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        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.
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        azp: Optional zero-point for the int8 quantization.
            Must be provided for asymmetric quantization if `scale` is provided.
        symmetric: Whether to use symmetric quantization (scale only, azp ignored).
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    Returns:
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      tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] : Output int8 tensor, scales, and optionally azp.
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    """
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    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
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        assert symmetric == (azp is None), (
            "azp must only be provided for asymmetric quantization."
        )
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        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
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        return output, scale, azp
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    # dynamic-per-token quantization.
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    input_scales = torch.empty(
        (input.numel() // input.shape[-1], 1), device=input.device, dtype=torch.float32
    )
    input_azp = None if symmetric else torch.empty_like(input_scales, dtype=torch.int32)
    torch.ops._C.dynamic_scaled_int8_quant(
        output, input.contiguous(), input_scales, input_azp
    )
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    return output, input_scales, input_azp
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# gguf
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def ggml_dequantize(
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    W: torch.Tensor, quant_type: int, m: int, n: int, dtype: torch.dtype | None
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) -> torch.Tensor:
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    return torch.ops._C.ggml_dequantize(W, quant_type, m, n, dtype)
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def ggml_mul_mat_vec_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
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) -> torch.Tensor:
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    return torch.ops._C.ggml_mul_mat_vec_a8(W, X, quant_type, row)


def ggml_mul_mat_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
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) -> torch.Tensor:
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    return torch.ops._C.ggml_mul_mat_a8(W, X, quant_type, row)


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def ggml_moe_a8(
    X: torch.Tensor,
    W: torch.Tensor,
    sorted_token_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_padded: torch.Tensor,
    quant_type: int,
    row: int,
    top_k: int,
    tokens: int,
) -> torch.Tensor:
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    return torch.ops._C.ggml_moe_a8(
        X,
        W,
        sorted_token_ids,
        expert_ids,
        num_tokens_post_padded,
        quant_type,
        row,
        top_k,
        tokens,
    )
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def ggml_moe_a8_vec(
    X: torch.Tensor,
    W: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    quant_type: int,
    row: torch.SymInt,
    tokens: torch.SymInt,
) -> torch.Tensor:
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    return torch.ops._C.ggml_moe_a8_vec(X, W, topk_ids, top_k, quant_type, row, tokens)
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def ggml_moe_get_block_size(quant_type: int) -> int:
    return torch.ops._C.ggml_moe_get_block_size(quant_type)


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# mamba
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def selective_scan_fwd(
    u: torch.Tensor,
    delta: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
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    D_: torch.Tensor | None,
    z_: torch.Tensor | None,
    delta_bias_: torch.Tensor | None,
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    delta_softplus: bool,
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    query_start_loc: torch.Tensor | None,
    cache_indices: torch.Tensor | None,
    has_initial_state: torch.Tensor | None,
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    ssm_states: torch.Tensor,
    pad_slot_id: int,
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    block_size: int = 1024,
    block_idx_first_scheduled_token: torch.Tensor | None = None,
    block_idx_last_scheduled_token: torch.Tensor | None = None,
    initial_state_idx: torch.Tensor | None = None,
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):
    torch.ops._C.selective_scan_fwd(
        u,
        delta,
        A,
        B,
        C,
        D_,
        z_,
        delta_bias_,
        delta_softplus,
        query_start_loc,
        cache_indices,
        has_initial_state,
        ssm_states,
        pad_slot_id,
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        block_size,
        block_idx_first_scheduled_token,
        block_idx_last_scheduled_token,
        initial_state_idx,
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    )
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# NOTE: The wvSplitK kernel (and all of the kernels in skinny_gemms.cu)
# are unable to properly handle non-contiguous
# tensors.  It might be a good TODO(rasmith) to augment these kernels
# to be able to handle non-contiguous kernels for better performance.
def rocm_enforce_contiguous_skinny_gemm_inputs(
    a: torch.Tensor, b: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    a = a.contiguous()  # no-op if already contiguous, else clone
    b = b.contiguous()  # no-op if already contiguous, else clone
    return a, b


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# ROCm skinny gemms
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def LLMM1(a: torch.Tensor, b: torch.Tensor, rows_per_block: int) -> torch.Tensor:
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    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
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    return torch.ops._rocm_C.LLMM1(a, b, rows_per_block)


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def wvSplitK(
    a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
) -> torch.Tensor:
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    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
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    return torch.ops._rocm_C.wvSplitK(a, b, bias, cu_count)


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def wvSplitKrc(
    a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
) -> torch.Tensor:
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    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
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    return torch.ops._rocm_C.wvSplitKrc(a, b, bias, cu_count)


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def wvSplitKQ(
    a: torch.Tensor,
    b: torch.Tensor,
    out_dtype: torch.dtype,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    cu_count: int,
    bias: torch.Tensor = None,
) -> torch.Tensor:
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    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
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    out = torch.empty((b.shape[0], a.shape[0]), dtype=out_dtype, device=b.device)
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    torch.ops._rocm_C.wvSplitKQ(a, b, bias, out, scale_a, scale_b, cu_count)
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    return out


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# moe
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def moe_sum(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum(input, output)


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def moe_align_block_size(
    topk_ids: torch.Tensor,
    num_experts: int,
    block_size: int,
    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
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    expert_map: torch.Tensor | None = None,
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) -> None:
    torch.ops._moe_C.moe_align_block_size(
        topk_ids,
        num_experts,
        block_size,
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
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        expert_map,
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    )
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def batched_moe_align_block_size(
    max_tokens_per_batch: int,
    block_size: int,
    expert_num_tokens: torch.Tensor,
    sorted_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
) -> None:
    torch.ops._moe_C.batched_moe_align_block_size(
        max_tokens_per_batch,
        block_size,
        expert_num_tokens,
        sorted_ids,
        expert_ids,
        num_tokens_post_pad,
    )


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def moe_lora_align_block_size(
    topk_ids: torch.Tensor,
    token_lora_mapping: torch.Tensor,
    num_experts: int,
    block_size: int,
    max_loras: int,
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    max_num_tokens_padded: int,
    max_num_m_blocks: int,
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    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
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    adapter_enabled: torch.Tensor,
    lora_ids: torch.Tensor,
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    expert_map: torch.Tensor | None = None,
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) -> None:
    torch.ops._moe_C.moe_lora_align_block_size(
        topk_ids,
        token_lora_mapping,
        num_experts,
        block_size,
        max_loras,
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        max_num_tokens_padded,
        max_num_m_blocks,
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        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
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        adapter_enabled,
        lora_ids,
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        expert_map,
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    )


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def moe_wna16_gemm(
    input: torch.Tensor,
    output: torch.Tensor,
    b_qweight: torch.Tensor,
    b_scales: torch.Tensor,
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    b_qzeros: torch.Tensor | None,
    topk_weights: torch.Tensor | None,
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    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
    top_k: int,
    BLOCK_SIZE_M: int,
    BLOCK_SIZE_N: int,
    BLOCK_SIZE_K: int,
    bit: int,
) -> torch.Tensor:
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    if not current_platform.is_cuda():
        raise NotImplementedError(
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            "The optimized moe_wna16_gemm kernel is only available on CUDA platforms"
        )
    torch.ops._moe_C.moe_wna16_gemm(
        input,
        output,
        b_qweight,
        b_scales,
        b_qzeros,
        topk_weights,
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
        top_k,
        BLOCK_SIZE_M,
        BLOCK_SIZE_N,
        BLOCK_SIZE_K,
        bit,
    )
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def topk_softmax(
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    token_expert_indices: torch.Tensor,
    gating_output: torch.Tensor,
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    renormalize: bool = False,
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    e_score_correction_bias: torch.Tensor | None = None,
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) -> None:
    torch.ops._moe_C.topk_softmax(
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        topk_weights,
        topk_ids,
        token_expert_indices,
        gating_output,
        renormalize,
        e_score_correction_bias,
    )


def topk_sigmoid(
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    token_expert_indices: torch.Tensor,
    gating_output: torch.Tensor,
    renormalize: bool = False,
    e_score_correction_bias: torch.Tensor | None = None,
) -> None:
    torch.ops._moe_C.topk_sigmoid(
        topk_weights,
        topk_ids,
        token_expert_indices,
        gating_output,
        renormalize,
        e_score_correction_bias,
2228
    )
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def grouped_topk(
    scores: torch.Tensor,
    num_expert_group: int,
    topk_group: int,
    topk: int,
    renormalize: bool,
    routed_scaling_factor: float,
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    bias: torch.Tensor,
    scoring_func: int = 0,
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):
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    """
    Perform grouped top-k routing for mixture of experts.

    Args:
        scores: Raw inputs (logits if scoring_func=1, scores if scoring_func=0)
        num_expert_group: Number of expert groups
        topk_group: Number of groups to select
        topk: Number of experts to select per token
        renormalize: Whether to renormalize the output weights
        routed_scaling_factor: Scaling factor for routing weights
        bias: Bias tensor (e_score_correction_bias). Always fused in kernel.
        scoring_func: 0=none (no activation), 1=sigmoid
    """
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    if not current_platform.is_cuda():
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        raise NotImplementedError(
            "The fused grouped_topk kernel is only available on CUDA platforms"
        )
    return torch.ops._moe_C.grouped_topk(
        scores,
        num_expert_group,
        topk_group,
        topk,
        renormalize,
        routed_scaling_factor,
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        bias,
        scoring_func,
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    )


def moe_wna16_marlin_gemm(
    input: torch.Tensor,
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    output: torch.Tensor | None,
2273
    b_qweight: torch.Tensor,
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    b_bias: torch.Tensor | None,
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    b_scales: torch.Tensor,
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    a_scales: torch.Tensor | None,
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    global_scale: torch.Tensor | None,
    b_qzeros: torch.Tensor | None,
    g_idx: torch.Tensor | None,
    perm: torch.Tensor | None,
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    workspace: torch.Tensor,
    sorted_token_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_past_padded: torch.Tensor,
    topk_weights: torch.Tensor,
    moe_block_size: int,
    top_k: int,
    mul_topk_weights: bool,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
    is_k_full: bool,
    use_atomic_add: bool,
    use_fp32_reduce: bool,
    is_zp_float: bool,
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    thread_k: int = -1,
    thread_n: int = -1,
    blocks_per_sm: int = -1,
2300
) -> torch.Tensor:
2301
    return torch.ops._moe_C.moe_wna16_marlin_gemm(
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        input,
        output,
        b_qweight,
        b_bias,
        b_scales,
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        a_scales,
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        global_scale,
        b_qzeros,
        g_idx,
        perm,
        workspace,
        sorted_token_ids,
        expert_ids,
        num_tokens_past_padded,
        topk_weights,
        moe_block_size,
        top_k,
        mul_topk_weights,
        b_q_type.id,
        size_m,
        size_n,
        size_k,
        is_k_full,
        use_atomic_add,
        use_fp32_reduce,
        is_zp_float,
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        thread_k,
        thread_n,
        blocks_per_sm,
2331
    )
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2334
if hasattr(torch.ops, "_moe_C") and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):
2335

2336
    @register_fake("_moe_C::marlin_gemm_moe")
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    def marlin_gemm_moe_fake(
        a: torch.Tensor,
        b_q_weights: torch.Tensor,
        sorted_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        b_scales: torch.Tensor,
        b_zero_points: torch.Tensor,
        g_idx: torch.Tensor,
        perm: torch.Tensor,
        workspace: torch.Tensor,
        b_q_type: ScalarType,
        size_m: torch.SymInt,
        size_n: torch.SymInt,
        size_k: torch.SymInt,
        is_k_full: bool,
        num_experts: int,
        topk: int,
        moe_block_size: int,
        replicate_input: bool,
        apply_weights: bool,
    ) -> torch.Tensor:
        return torch.empty((size_m, topk, size_n), dtype=a.dtype, device=a.device)
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    @register_fake("_moe_C::moe_wna16_marlin_gemm")
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    def moe_wna16_marlin_gemm_fake(
        input: torch.Tensor,
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        output: torch.Tensor | None,
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        b_qweight: torch.Tensor,
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        b_bias: torch.Tensor | None,
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        b_scales: torch.Tensor,
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        a_scales: torch.Tensor | None,
        global_scale: torch.Tensor | None,
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        b_qzeros: torch.Tensor | None,
        g_idx: torch.Tensor | None,
        perm: torch.Tensor | None,
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        workspace: torch.Tensor,
        sorted_token_ids: torch.Tensor,
        expert_ids: torch.Tensor,
        num_tokens_past_padded: torch.Tensor,
        topk_weights: torch.Tensor,
        moe_block_size: int,
        top_k: int,
        mul_topk_weights: bool,
        b_q_type: ScalarType,
        size_m: int,
        size_n: int,
        size_k: int,
        is_k_full: bool,
        use_atomic_add: bool,
        use_fp32_reduce: bool,
        is_zp_float: bool,
2389
    ):
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        return torch.empty(
            (size_m * top_k, size_n), dtype=input.dtype, device=input.device
        )
2393

2394

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def reshape_and_cache(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
2404
) -> None:
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    torch.ops._C_cache_ops.reshape_and_cache(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        kv_cache_dtype,
        k_scale,
        v_scale,
    )
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def reshape_and_cache_flash(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
2426
) -> None:
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    torch.ops._C_cache_ops.reshape_and_cache_flash(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        kv_cache_dtype,
        k_scale,
        v_scale,
    )
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def concat_and_cache_mla(
    kv_c: torch.Tensor,
    k_pe: torch.Tensor,
    kv_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
    scale: torch.Tensor,
) -> None:
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    torch.ops._C_cache_ops.concat_and_cache_mla(
        kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale
    )
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def concat_and_cache_mla_rope_fused(
    positions: torch.Tensor,
    q_pe: torch.Tensor,
    k_pe: torch.Tensor,
    kv_c: torch.Tensor,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
    slot_mapping: torch.Tensor,
    kv_cache: torch.Tensor,
    kv_cache_dtype: str,
    kv_cache_scale: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.concat_and_cache_mla_rope_fused(
        positions,
        q_pe,
        k_pe,
        kv_c,
        cos_sin_cache,
        is_neox,
        slot_mapping,
        kv_cache,
        kv_cache_dtype,
        kv_cache_scale,
    )


2478
def swap_blocks(
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    src: torch.Tensor,
    dst: torch.Tensor,
    block_size_in_bytes: int,
    block_mapping: torch.Tensor,
2483
) -> None:
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    """
    Copy specific blocks from one tensor to another.

    This method assumes each of the two input tensors is composed of
    consecutive contiguous blocks, of size block_size_in_bytes.
    i.e. the memory layout for each tensor is:
    [block0] [block1] ... [block N]

    block_mapping determines the subset of blocks to copy of the source tensor,
    and their matching destination block number on the destination tensor.
    block_mapping is expected to be a tensor of shape (num_blocks_to_copy, 2)
    where each block_mapping[i] represents a single copy operation, copying
    block #block_mapping[i][0] from the source tensor
    to block #block_mapping[i][1] on the destination tensor.
    block_mapping should have dtype int64.

    The source and the destination tensors can be either on cpu or gpu,
    but not both on cpu.
    the block mapping tensor must on cpu.
    """
    torch.ops._C_cache_ops.swap_blocks(src, dst, block_size_in_bytes, block_mapping)
2505
2506


2507
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def convert_fp8(
    output: torch.Tensor, input: torch.Tensor, scale: float = 1.0, kv_dtype: str = "fp8"
) -> None:
2510
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    torch.ops._C_cache_ops.convert_fp8(output, input, scale, kv_dtype)


2513
def gather_and_maybe_dequant_cache(
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2517
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
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    token_to_seq: torch.Tensor,
    num_tokens: int,
2520
2521
    kv_cache_dtype: str,
    scale: torch.Tensor,
2522
    seq_starts: torch.Tensor | None = None,
2523
) -> None:
2524
    torch.ops._C_cache_ops.gather_and_maybe_dequant_cache(
2525
2526
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2528
        src_cache,
        dst,
        block_table,
        cu_seq_lens,
2529
2530
        token_to_seq,
        num_tokens,
2531
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2534
        kv_cache_dtype,
        scale,
        seq_starts,
    )
2535
2536


2537
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def cp_gather_cache(
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
    batch_size: int,
2543
    seq_starts: torch.Tensor | None = None,
2544
2545
2546
2547
) -> None:
    torch.ops._C_cache_ops.cp_gather_cache(
        src_cache, dst, block_table, cu_seq_lens, batch_size, seq_starts
    )
2548
2549


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def cp_gather_and_upconvert_fp8_kv_cache(
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    seq_lens: torch.Tensor,
    workspace_starts: torch.Tensor,
    batch_size: int,
) -> None:
    """Gather and upconvert FP8 KV cache to BF16 workspace.

    Args:
        src_cache: FP8 KV cache [num_blocks, block_size, 656]
        dst: BF16 output workspace [total_tokens, 576]
        block_table: Block indices [num_reqs, max_blocks]
        seq_lens: Sequence lengths [num_reqs]
        workspace_starts: Workspace start offsets [num_reqs]
        batch_size: Number of requests
    """
    torch.ops._C_cache_ops.cp_gather_and_upconvert_fp8_kv_cache(
        src_cache, dst, block_table, seq_lens, workspace_starts, batch_size
    )


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2582
def indexer_k_quant_and_cache(
    k: torch.Tensor,
    kv_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    quant_block_size: int,
    kv_cache_dtype: str,
) -> None:
    torch.ops._C_cache_ops.indexer_k_quant_and_cache(
        k, kv_cache, slot_mapping, quant_block_size, kv_cache_dtype
    )
2583
2584


2585
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2596
def cp_gather_indexer_k_quant_cache(
    kv_cache: torch.Tensor,
    dst_k: torch.Tensor,
    dst_scale: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.cp_gather_indexer_k_quant_cache(
        kv_cache, dst_k, dst_scale, block_table, cu_seq_lens
    )


2597
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2600
2601
2602
2603
def get_device_attribute(attribute: int, device: int) -> int:
    return torch.ops._C_cuda_utils.get_device_attribute(attribute, device)


def get_max_shared_memory_per_block_device_attribute(device: int) -> int:
    # ruff: noqa: E501
    return torch.ops._C_cuda_utils.get_max_shared_memory_per_block_device_attribute(
2604
2605
        device
    )
2606
2607
2608


# custom ar
2609
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2617
def init_custom_ar(
    ipc_tensors: list[torch.Tensor],
    rank_data: torch.Tensor,
    rank: int,
    fully_connected: bool,
) -> int:
    return torch.ops._C_custom_ar.init_custom_ar(
        ipc_tensors, rank_data, rank, fully_connected
    )
2618
2619


2620
2621
2622
2623
2624
2625
2626
2627
def all_reduce(
    fa: int,
    inp: torch.Tensor,
    out: torch.Tensor,
    reg_buffer: int,
    reg_buffer_sz_bytes: int,
) -> None:
    torch.ops._C_custom_ar.all_reduce(fa, inp, out, reg_buffer, reg_buffer_sz_bytes)
2628

2629
2630
2631
2632
2633
2634
2635
2636
2637

def dispose(fa: int) -> None:
    torch.ops._C_custom_ar.dispose(fa)


def meta_size() -> int:
    return torch.ops._C_custom_ar.meta_size()


2638
def register_buffer(fa: int, ipc_tensors: list[int]) -> None:
2639
    return torch.ops._C_custom_ar.register_buffer(fa, ipc_tensors)
2640
2641


2642
def get_graph_buffer_ipc_meta(fa: int) -> tuple[list[int], list[int]]:
2643
2644
2645
    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


2646
2647
2648
def register_graph_buffers(
    fa: int, handles: list[list[int]], offsets: list[list[int]]
) -> None:
2649
    torch.ops._C_custom_ar.register_graph_buffers(fa, handles, offsets)
2650
2651


2652
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2663
def allocate_shared_buffer_and_handle(size: int) -> tuple[int, torch.Tensor]:
    return torch.ops._C_custom_ar.allocate_shared_buffer_and_handle(size)


def open_mem_handle(mem_handle: torch.Tensor):
    return torch.ops._C_custom_ar.open_mem_handle(mem_handle)


def free_shared_buffer(ptr: int) -> None:
    torch.ops._C_custom_ar.free_shared_buffer(ptr)


2664
# quick all reduce
2665
def init_custom_qr(rank: int, world_size: int, qr_max_size: int | None = None) -> int:
2666
2667
2668
2669
2670
2671
2672
    return torch.ops._C_custom_ar.init_custom_qr(rank, world_size, qr_max_size)


def qr_destroy(fa: int) -> None:
    torch.ops._C_custom_ar.qr_destroy(fa)


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2680
def qr_all_reduce(
    fa: int,
    inp: torch.Tensor,
    out: torch.Tensor,
    quant_level: int,
    cast_bf2half: bool = False,
) -> None:
    torch.ops._C_custom_ar.qr_all_reduce(fa, inp, out, quant_level, cast_bf2half)
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2694


def qr_get_handle(fa: int) -> torch.Tensor:
    return torch.ops._C_custom_ar.qr_get_handle(fa)


def qr_open_handles(fa: int, handles: list[torch.Tensor]) -> None:
    return torch.ops._C_custom_ar.qr_open_handles(fa, handles)


def qr_max_size() -> int:
    return torch.ops._C_custom_ar.qr_max_size()


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2698
def get_flash_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_heads_per_head_k: int,
    num_heads_k: int,
2699
) -> tuple[torch.Tensor, torch.Tensor]:
2700
2701
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2703
2704
2705
2706
2707
2708
2709
    """
    Arguments:
        cache_seqlens: (batch_size), dtype torch.int32.
        num_heads_per_head_k: Equals to seq_len_q * num_heads_q // num_heads_k.
        num_heads_k: num_heads_k.

    Return:
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), dtype torch.int32.
        num_splits: (batch_size + 1), dtype torch.int32.
    """
2710
2711
2712
    return torch.ops._C.get_flash_mla_metadata(
        cache_seqlens, num_heads_per_head_k, num_heads_k
    )
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722


def flash_mla_with_kvcache(
    q: torch.Tensor,
    k_cache: torch.Tensor,
    block_table: torch.Tensor,
    cache_seqlens: torch.Tensor,
    head_dim_v: int,
    tile_scheduler_metadata: torch.Tensor,
    num_splits: torch.Tensor,
2723
    softmax_scale: float | None = None,
2724
    causal: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    Arguments:
        q: (batch_size, seq_len_q, num_heads_q, head_dim).
        k_cache: (num_blocks, page_block_size, num_heads_k, head_dim).
        block_table: (batch_size, max_num_blocks_per_seq), torch.int32.
        cache_seqlens: (batch_size), torch.int32.
        head_dim_v: Head_dim of v.
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, return by get_mla_metadata.
        num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata.
        softmax_scale: float. The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim).
        causal: bool. Whether to apply causal attention mask.

    Return:
        out: (batch_size, seq_len_q, num_heads_q, head_dim_v).
        softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32.
    """
    if softmax_scale is None:
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        softmax_scale = q.shape[-1] ** (-0.5)
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    out, softmax_lse = torch.ops._C.flash_mla_fwd_kvcache(
        q,
        k_cache,
        None,
        head_dim_v,
        cache_seqlens,
        block_table,
        softmax_scale,
        causal,
        tile_scheduler_metadata,
        num_splits,
    )
    return out, softmax_lse
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def sm100_cutlass_mla_decode(
    out: torch.Tensor,
    lse: torch.Tensor,
    q_nope: torch.Tensor,
    q_pe: torch.Tensor,
    kv_c_and_k_pe_cache: torch.Tensor,
    seq_lens: torch.Tensor,
    page_table: torch.Tensor,
    workspace: torch.Tensor,
    scale: float,
    num_kv_splits: int,
) -> torch.Tensor:
    torch.ops._C.sm100_cutlass_mla_decode(
        out,
        lse,
        q_nope,
        q_pe,
        kv_c_and_k_pe_cache,
        seq_lens,
        page_table,
        workspace,
        scale,
        num_kv_splits,
    )
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    return out


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def sm100_cutlass_mla_get_workspace_size(
    max_seq_len: int, num_batches: int, sm_count: int, num_kv_splits: int
) -> int:
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    return torch.ops._C.sm100_cutlass_mla_get_workspace_size(
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        max_seq_len, num_batches, sm_count, num_kv_splits
    )
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if hasattr(torch.ops._C, "weight_packed_linear"):

    @register_fake("_C::weight_packed_linear")
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    def weight_packed_linear_fake(
        mat1: torch.Tensor,
        mat2: torch.Tensor,
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        bias: torch.Tensor | None,
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        is_vnni: bool,
    ) -> torch.Tensor:
        return torch.empty(
            (mat1.size(0), mat2.size(0)), dtype=mat1.dtype, device=mat2.device
        )
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if hasattr(torch.ops._C, "fused_experts_cpu"):

    @register_fake("_C::fused_experts_cpu")
    def fused_experts_cpu_fake(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        inplace: bool,
        use_int8_w8a8: bool,
        use_fp8_w8a16: bool,
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        w1_scale: torch.Tensor | None,
        w2_scale: torch.Tensor | None,
        block_size: list[int] | None,
        a1_scale: torch.Tensor | None,
        a2_scale: torch.Tensor | None,
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        is_vnni: bool,
    ) -> torch.Tensor:
        return torch.empty_like(hidden_states)


if hasattr(torch.ops._C, "int8_scaled_mm_with_quant"):

    @register_fake("_C::int8_scaled_mm_with_quant")
    def int8_scaled_mm_with_quant_fake(
        mat1: torch.Tensor,
        mat2: torch.Tensor,
        scales2: torch.Tensor,
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        bias: torch.Tensor | None,
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        out_dtype: torch.dtype,
        is_vnni: bool,
    ) -> torch.Tensor:
        M = mat1.size(0)
        N = mat2.size(0)
        return torch.empty((M, N), dtype=out_dtype)
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class CPUDNNLGEMMHandler:
    def __init__(self) -> None:
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        self.handler: int | None = None
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        self.n = -1
        self.k = -1

    def __del__(self):
        if self.handler is not None:
            torch.ops._C.release_dnnl_matmul_handler(self.handler)


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_supports_onednn = bool(hasattr(torch.ops._C, "create_onednn_mm_handler"))
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def is_onednn_acl_supported():
    return torch.ops._C.is_onednn_acl_supported()


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def create_onednn_mm(
    weight: torch.Tensor,  # [K, N]
    primitive_cache_size: int = 128,
) -> CPUDNNLGEMMHandler:
    handler = CPUDNNLGEMMHandler()
    handler.k, handler.n = weight.size()
    handler.handler = torch.ops._C.create_onednn_mm_handler(
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        weight, primitive_cache_size
    )
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    return handler


def onednn_mm(
    dnnl_handler: CPUDNNLGEMMHandler,
    x: torch.Tensor,
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    bias: torch.Tensor | None,
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) -> torch.Tensor:
    output = torch.empty((*x.shape[0:-1], dnnl_handler.n), dtype=x.dtype)
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    torch.ops._C.onednn_mm(
        output, x.reshape(-1, dnnl_handler.k), bias, dnnl_handler.handler
    )
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    return output


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def create_onednn_scaled_mm(
    weight: torch.Tensor,  # [K, N]
    weight_scales: torch.Tensor,
    output_type: torch.dtype,
    dynamic_quant: bool,
    use_azp: bool,
    primitive_cache_size: int = 128,
) -> CPUDNNLGEMMHandler:
    handler = CPUDNNLGEMMHandler()
    handler.k, handler.n = weight.size()
    handler.handler = torch.ops._C.create_onednn_scaled_mm_handler(
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        weight, weight_scales, output_type, dynamic_quant, use_azp, primitive_cache_size
    )
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    return handler


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def onednn_scaled_int8_quant(
    input: torch.Tensor,
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    scale: torch.Tensor | None = None,
    azp: torch.Tensor | None = None,
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    symmetric: bool = True,
):
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    """
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.

    Args:
        input: The input tensor to be quantized to int8.
        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.
        azp: Optional zero-point for the int8 quantization.
            Must be provided for asymmetric quantization if `scale` is provided.
        symmetric: Whether to use symmetric quantization (scale only, azp ignored).

    Returns:
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      tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] : Output int8 tensor, scales, and optionally azp.
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    """
    output = torch.empty_like(input, dtype=torch.int8)
    token_num = input.numel() // input.shape[-1]
    input = input.view((token_num, input.shape[-1]))
    if scale is not None:
        # static-per-tensor quantization.
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        assert symmetric == (azp is None), (
            "azp must only be provided for asymmetric quantization."
        )
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        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
        return output, scale, azp

    # dynamic-per-token quantization.
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    input_scales = torch.empty((token_num, 1), device=input.device, dtype=torch.float32)
    input_azp = None if symmetric else torch.empty_like(input_scales, dtype=torch.int32)
    torch.ops._C.dynamic_scaled_int8_quant(output, input, input_scales, input_azp)
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    return output, input_scales, input_azp


def onednn_scaled_mm(
    dnnl_handler: CPUDNNLGEMMHandler,
    x: torch.Tensor,
    output: torch.Tensor,
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    input_scale: torch.Tensor | None,
    input_zp: torch.Tensor | None,
    input_zp_adj: torch.Tensor | None,
    bias: torch.Tensor | None,
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) -> torch.Tensor:
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    torch.ops._C.onednn_scaled_mm(
        output, x, input_scale, input_zp, input_zp_adj, bias, dnnl_handler.handler
    )
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    return output
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def cpu_attn_get_scheduler_metadata(
    num_reqs: int,
    num_heads: int,
    num_kv_heads: int,
    head_dim: int,
    seq_lens: torch.Tensor,
    dtype: torch.dtype,
    query_start_loc: torch.Tensor,
    causal: bool,
    sliding_window_size: int,
    isa: str,
    enable_kv_split: bool,
) -> torch.Tensor:
    sheduler_metadata = torch.ops._C.get_scheduler_metadata(
        num_reqs,
        num_heads,
        num_kv_heads,
        head_dim,
        seq_lens,
        dtype,
        query_start_loc,
        causal,
        sliding_window_size,
        isa,
        enable_kv_split,
    )
    return sheduler_metadata


def cpu_attn_reshape_and_cache(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    isa: str,
) -> None:
    torch.ops._C.cpu_attn_reshape_and_cache(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        isa,
    )


def cpu_attention_with_kv_cache(
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    output: torch.Tensor,
    query_start_loc: torch.Tensor,
    seq_lens: torch.Tensor,
    scale: float,
    causal: bool,
    alibi_slopes: torch.Tensor | None,
    sliding_window: tuple[int, int],
    block_table: torch.Tensor,
    softcap: float,
    scheduler_metadata: torch.Tensor,
    s_aux: torch.Tensor | None,
) -> None:
    torch.ops._C.cpu_attention_with_kv_cache(
        query,
        key_cache,
        value_cache,
        output,
        query_start_loc,
        seq_lens,
        scale,
        causal,
        alibi_slopes,
        sliding_window[0],
        sliding_window[1],
        block_table,
        softcap,
        scheduler_metadata,
        s_aux,
    )


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def cpu_gemm_wna16(
    input: torch.Tensor,
    q_weight: torch.Tensor,
    scales: torch.Tensor,
    zeros: torch.Tensor | None,
    g_idx: torch.Tensor | None,
    bias: torch.Tensor | None,
    pack_factor: int,
    isa_hint: str,
) -> torch.Tensor:
    output = torch.empty((input.size(0), scales.size(1)), dtype=input.dtype)
    torch.ops._C.cpu_gemm_wna16(
        input,
        q_weight,
        output,
        scales,
        zeros,
        g_idx,
        bias,
        pack_factor,
        isa_hint,
    )
    return output


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def cpu_prepack_moe_weight(
    weight: torch.Tensor,
    isa: str,
) -> torch.Tensor:
    output = torch.empty_like(weight)
    torch.ops._C.prepack_moe_weight(weight, output, isa)
    return output


def cpu_fused_moe(
    input: torch.Tensor,
    w13: torch.Tensor,
    w2: torch.Tensor,
    w13_bias: torch.Tensor | None,
    w2_bias: torch.Tensor | None,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    act: str,
    isa: str,
) -> torch.Tensor:
    output = torch.empty_like(input)
    torch.ops._C.cpu_fused_moe(
        output,
        input,
        w13,
        w2,
        w13_bias,
        w2_bias,
        topk_weights,
        topk_ids,
        act,
        isa,
    )
    return output


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if hasattr(torch.ops._qutlass_C, "matmul_mxf4_bf16_tn"):

    @register_fake("_qutlass_C::matmul_mxf4_bf16_tn")
    def _fake_matmul_mxf4_bf16_tn(
        a: torch.Tensor,
        b: torch.Tensor,
        a_sf: torch.Tensor,
        b_sf: torch.Tensor,
        alpha: torch.Tensor,
    ):
        return a.new_empty(*a.shape[:-1], b.shape[0], dtype=torch.bfloat16)


def matmul_mxf4_bf16_tn(
    a: torch.Tensor,
    b: torch.Tensor,
    a_sf: torch.Tensor,
    b_sf: torch.Tensor,
    alpha: torch.Tensor,
) -> torch.Tensor:
    return torch.ops._qutlass_C.matmul_mxf4_bf16_tn(a, b, a_sf, b_sf, alpha)


if hasattr(torch.ops._qutlass_C, "matmul_ada_mxf4_bf16_tn"):

    @register_fake("_qutlass_C::matmul_ada_mxf4_bf16_tn")
    def _fake_matmul_ada_mxf4_bf16_tn(
        a: torch.Tensor,
        b: torch.Tensor,
        a_sf: torch.Tensor,
        b_sf: torch.Tensor,
        alpha: torch.Tensor,
    ):
        return a.new_empty(*a.shape[:-1], b.shape[0], dtype=torch.bfloat16)


def matmul_ada_mxf4_bf16_tn(
    a: torch.Tensor,
    b: torch.Tensor,
    a_sf: torch.Tensor,
    b_sf: torch.Tensor,
    alpha: torch.Tensor,
) -> torch.Tensor:
    return torch.ops._qutlass_C.matmul_ada_mxf4_bf16_tn(a, b, a_sf, b_sf, alpha)


if hasattr(torch.ops._qutlass_C, "fusedQuantizeMxQuest"):

    @register_fake("_qutlass_C::fusedQuantizeMxQuest")
    def _fake_fused_quantize_mx_quest(
        a: torch.Tensor, b: torch.Tensor, xh_e2m1: torch.Tensor, xh_e8m0: torch.Tensor
    ):
        return xh_e2m1, xh_e8m0


if hasattr(torch.ops._qutlass_C, "fusedQuantizeMxAbsMax"):

    @register_fake("_qutlass_C::fusedQuantizeMxAbsMax")
    def _fake_fused_quantize_mx_absmax(
        a: torch.Tensor, b: torch.Tensor, xh_e2m1: torch.Tensor, xh_e8m0: torch.Tensor
    ):
        return xh_e2m1, xh_e8m0


def fusedQuantizeMx(
    a: torch.Tensor, b: torch.Tensor, *, method: Literal["quest", "abs_max"] = "quest"
) -> tuple[torch.Tensor, torch.Tensor]:
    if a.dim() == 0:
        raise ValueError("`a` must have at least 1 dimension.")
    if a.size(-1) % 32 != 0:
        raise ValueError(f"last dim of `a` must be divisible by 32, got {a.size(-1)}.")
    if b.device != a.device:
        raise ValueError("`a` and `b` must be on the same device.")

    xh_e2m1 = torch.empty(
        *a.shape[:-1], a.size(-1) // 2, dtype=torch.uint8, device=a.device
    )

    rows, cols = a.numel() // a.size(-1), a.size(-1) // 32
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    n_row_blocks = cdiv(rows, 128)
    n_col_blocks = cdiv(cols, 4)
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    padded_rows = n_row_blocks * 128
    padded_cols = n_col_blocks * 4

    xh_e8m0 = torch.empty(
        padded_rows, padded_cols, dtype=torch.float8_e8m0fnu, device=a.device
    )

    if not hasattr(torch.ops, "_qutlass_C"):
        raise RuntimeError(
            "The `_qutlass_C` extension is not loaded. "
            "Make sure your custom op library is imported before calling fusedQuantizeMx."
        )

    if method == "quest":
        return torch.ops._qutlass_C.fusedQuantizeMxQuest(a, b, xh_e2m1, xh_e8m0)
    elif method == "abs_max":
        return torch.ops._qutlass_C.fusedQuantizeMxAbsMax(a, b, xh_e2m1, xh_e8m0)
    else:
        raise ValueError(f"invalid method {method!r}, must be 'quest' or 'abs_max'")


if hasattr(torch.ops._qutlass_C, "fusedQuantizeNv"):

    @register_fake("_qutlass_C::fusedQuantizeNv")
    def _fake_fused_quantize_nv(
        a: torch.Tensor,
        b: torch.Tensor,
        xh_e2m1: torch.Tensor,
        xh_e4m3: torch.Tensor,
        global_scale: torch.Tensor,
    ):
        return xh_e2m1, xh_e4m3


def fusedQuantizeNv(
    a: torch.Tensor, b: torch.Tensor, global_scale: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    xh_e2m1 = torch.empty(
        *a.shape[:-1], a.size(-1) // 2, dtype=torch.uint8, device=a.device
    )

    rows, cols = a.numel() // a.size(-1), a.size(-1) // 16
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    n_row_blocks = cdiv(rows, 128)
    n_col_blocks = cdiv(cols, 4)
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    padded_rows = n_row_blocks * 128
    padded_cols = n_col_blocks * 4
    xh_e4m3 = torch.empty(
        padded_rows, padded_cols, dtype=torch.float8_e4m3fn, device=a.device
    )

    return torch.ops._qutlass_C.fusedQuantizeNv(a, b, xh_e2m1, xh_e4m3, global_scale)


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def hadacore_transform(x: torch.Tensor, inplace: bool = True) -> torch.Tensor:
    """
    Perform Hadamard transforms using [Hadacore](https://arxiv.org/abs/2412.08832)
    kernels. Note that these kernels exploit the recursive properties of
    Sylvester Hadamards, and therefore do not require transform weight data

    Note that sylvester hadamard transforms are also symmetric, which means that
    this function is also applies the (transpose <=> inverse) transform.
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    :param x: value to be transformed inplace
    :param inplace: modify value in place
    :return: value after transformation
    """
    return torch.ops._C.hadacore_transform(x, inplace)


if hasattr(torch.ops._C, "hadacore_transform"):

    @register_fake("_C::hadacore_transform")
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    def _hadacore_transform_fake(x: torch.Tensor, inplace: bool) -> torch.Tensor:
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        return torch.empty_like(x) if not inplace else x