_custom_ops.py 106 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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# scaled_fp4_quant functional + out variant for torch.compile buffer management


def create_fp4_scale_tensor(
    m: int,
    n: int,
    device: torch.device,
    is_sf_swizzled_layout: bool,
) -> torch.Tensor:
    """
    Allocate the output scale tensor for scaled_fp4_quant.

    When is_sf_swizzled_layout=True, we use rounded values to store the
    swizzled scales. 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 (rows) and 4 (cols). 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
    """
    from vllm.utils.math_utils import round_up

    block_size = 16
    if is_sf_swizzled_layout:
        rounded_m = round_up(m, 128)
        scale_n = n // block_size
        rounded_n = round_up(scale_n, 4)
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        return torch.zeros(
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            (rounded_m, rounded_n // 4), device=device, dtype=torch.int32
        )
    else:
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        return torch.zeros((m, n // block_size), device=device, dtype=torch.uint8)
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def create_fp4_output_tensors(
    m: int,
    n: int,
    device: torch.device,
    is_sf_swizzled_layout: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Allocate both output tensors for scaled_fp4_quant:
    (quantized_output, output_scale).

    Must match the C++ scaled_fp4_quant_func allocation exactly.
    """
    output = torch.empty((m, n // 2), device=device, dtype=torch.uint8)
    output_scale = create_fp4_scale_tensor(m, n, device, is_sf_swizzled_layout)
    return output, output_scale


if hasattr(torch.ops, "_C") and hasattr(torch.ops._C, "scaled_fp4_quant"):

    @register_fake("_C::scaled_fp4_quant")
    def _scaled_fp4_quant_fake(
        input: torch.Tensor,
        input_scale: torch.Tensor,
        is_sf_swizzled_layout: bool,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        n = input.shape[-1]
        m = input.numel() // n
        return create_fp4_output_tensors(m, n, input.device, is_sf_swizzled_layout)

    @register_fake("_C::scaled_fp4_quant.out")
    def _scaled_fp4_quant_out_fake(
        input: torch.Tensor,
        input_scale: torch.Tensor,
        is_sf_swizzled_layout: bool,
        *,
        output: torch.Tensor,
        output_scale: torch.Tensor,
    ) -> None:
        return None


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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.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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    prefill_tokens_with_context: int | None = None,
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    output_scale: torch.Tensor | None = None,
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) -> None:
    torch.ops._C.merge_attn_states(
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        output,
        output_lse,
        prefix_output,
        prefix_lse,
        suffix_output,
        suffix_lse,
        prefill_tokens_with_context,
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        output_scale,
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    )
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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,
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    forced_token_heads_per_warp: int = -1,
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) -> 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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        forced_token_heads_per_warp,
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    )


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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(input.shape, dtype=quant_dtype, device=input.device)
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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,
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    tma_alignment: int = 0,
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) -> tuple[torch.Tensor, torch.Tensor]:
    assert len(group_size) == 2
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    output = torch.empty(input.shape, dtype=quant_dtype, device=input.device)
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    if is_scale_transposed:
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        if tma_alignment == 0:
            scales = torch.empty(
                (input.shape[-1] // group_size[1], input.numel() // input.shape[-1]),
                device=input.device,
                dtype=torch.float32,
            ).transpose(0, 1)
        else:
            m = input.shape[-2]
            sf_k = input.shape[-1] // group_size[1]
            tma_aligned_m = (m + tma_alignment - 1) // tma_alignment * tma_alignment
            shape = input.shape[:-2] + (m, sf_k)
            stride = (
                (1, tma_aligned_m)
                if input.dim() == 2
                else (tma_aligned_m * sf_k, 1, tma_aligned_m)
            )
            scales = torch.empty_strided(
                shape, stride, device=input.device, dtype=torch.float32
            )
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    else:
        scales = torch.empty(
            (input.numel() // input.shape[-1], input.shape[-1] // group_size[1]),
            device=input.device,
            dtype=torch.float32,
        )

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    assert tma_alignment in [0, 4], "Expected TMA alignment 0 or 4, but got " + str(
        tma_alignment
    )

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    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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# fused silu_and_mul + block quant
def silu_and_mul_per_block_quant(
    input: torch.Tensor,
    group_size: int,  # Changed from list[int]
    quant_dtype: torch.dtype,
    scale_ub: torch.Tensor | None = None,
    is_scale_transposed: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    assert input.ndim == 2, f"input must be 2D [batch, hidden*2], got {input.shape}"
    assert input.shape[-1] % 2 == 0, (
        f"input last dim must be even (gate||up layout), got {input.shape[-1]}"
    )

    # Output is half the width of input (after silu_and_mul)
    num_tokens = input.shape[0]
    hidden_size = input.shape[-1] // 2  # Divide by 2 because input is [gate || up]

    # Allocate output tensor (FP8 or INT8)
    output = torch.empty(
        (num_tokens, hidden_size), device=input.device, dtype=quant_dtype
    )

    # Allocate scales tensor
    num_groups = hidden_size // group_size  # Directly use group_size
    if is_scale_transposed:
        scales = torch.empty(
            (num_groups, num_tokens),
            device=input.device,
            dtype=torch.float32,
        ).t()
    else:
        scales = torch.empty(
            (num_tokens, num_groups),
            device=input.device,
            dtype=torch.float32,
        )

    # Call the C++ kernel
    torch.ops._C.silu_and_mul_per_block_quant(
        output,
        input,
        scales,
        group_size,  # Pass directly as int
        scale_ub,
        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,
        )

651
        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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if hasattr(torch.ops._C, "awq_dequantize"):

    @register_fake("_C::awq_dequantize")
    def _awq_dequantize_fake(
        qweight: torch.Tensor,
        scales: torch.Tensor,
        zeros: torch.Tensor,
        split_k_iters: torch.SymInt,
        thx: int,
        thy: int,
    ) -> torch.Tensor:
        in_c = qweight.size(0)
        qout_c = qweight.size(1)
        out_c = qout_c * 8
        return torch.empty((in_c, out_c), dtype=scales.dtype, device=scales.device)


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

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


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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,
724
    )
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727
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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745
def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor, bit: int) -> None:
746
    torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
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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")
772
    def _ggml_dequantize_fake(
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        W: torch.Tensor,
        quant_type: int,
        m: torch.SymInt,
        n: torch.SymInt,
777
        dtype: torch.dtype | None = None,
778
    ) -> 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:
788
        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)
798
        return torch.empty((batch, row), dtype=X.dtype, device=W.device)
799

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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)
813
        return torch.empty((tokens * top_k, row), dtype=torch.float16, device=W.device)
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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)
829
        return torch.empty((tokens * top_k, row), dtype=X.dtype, device=W.device)
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831


832
# 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)
848
    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)


856
def cutlass_scaled_mm_supports_block_fp8(cuda_device_capability: int) -> bool:
857
    return torch.ops._C.cutlass_scaled_mm_supports_block_fp8(cuda_device_capability)
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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,
866
    bias: torch.Tensor | None = None,
867
) -> torch.Tensor:
868
    """
869
    `cutlass_scaled_mm` implements a fused version of
870
        `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
892

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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])
896

897
    cutlass_compatible_b = b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
898
    if current_platform.is_rocm() or not cutlass_compatible_b:
899
        from vllm.model_executor.layers.quantization.compressed_tensors.triton_scaled_mm import (  # noqa
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            triton_scaled_mm,
        )

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904
        out = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    else:
905
        out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
906
        torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
907

908
    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,
920
) -> 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
929

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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]
934

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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)
937
    return out.view(*target_shape)
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939


940
def cutlass_group_gemm_supported(cuda_device_capability: int) -> bool:
941
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    if cuda_device_capability < 90 or cuda_device_capability >= 110:
        return False
943
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947
    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
948

949

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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,
960
    blockscale_offsets: torch.Tensor | None = None,
961
    is_gated: bool = True,
962
):
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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.
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    - 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]
985
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    - is_gated: Whether the activation is gated (gate + up). When True, the
                first GEMM N dimension is 2*n; when False, it is n.
987
    """
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    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,
999
        is_gated,
1000
    )
1001
1002


1003
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1020
1021
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,
    )


1022
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1024
1025
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1027
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]
1028
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1032
    output_tensor = torch.empty(
        (num_tokens_permuted, input_tensor.shape[1]),
        device=input_tensor.device,
        dtype=input_tensor.dtype,
    )
1033
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    torch.ops._moe_C.shuffle_rows(input_tensor, dst2src_map, output_tensor)
    return output_tensor
1035
1036


1037
def get_cutlass_batched_moe_mm_data(
1038
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1046
    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,
):
1047
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1051
    """
    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
1052
    non_zero_expert_idxs (consecutive indices of experts with non-zero token
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    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.
    """
1060
    return torch.ops._C.get_cutlass_batched_moe_mm_data(
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        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        expert_num_tokens,
        num_local_experts,
        padded_m,
        n,
        k,
    )
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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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1093
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1095
1096
    """
    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.
    """
1097
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1102
1103
1104
1105
1106
1107
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1109
1110
    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,
    )
1111
1112


1113
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1120
1121
1122
1123
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,
):
1124
    """
1125
    An FP4 Blockscaled Group Gemm that takes in  a_tensors, b_tensors and runs
1126
1127
1128
1129
1130
1131
    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
1132
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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,
    )
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def cutlass_mxfp4_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    problem_sizes: torch.Tensor,
    expert_offsets: torch.Tensor,
    sf_offsets: torch.Tensor,
):
    """
    An MXFP4 Blockscaled Group Gemm for MoE (MXFP4 x MXFP4).

    Uses mx_float4_t types with E8M0 scale factors and 32-element blocks.
    - a/b_tensors: MXFP4 packed activations/weights (uint8, 2 E2M1 per byte)
    - a_/b_scales: E8M0 blockscales (uint8, stored in swizzled layout)
    - Epilogue uses scalar alpha=1, beta=0 inside the CUDA op (no global scales).
    - expert_offsets/sf_offsets: expert boundary indices
    - problem_sizes: (num_experts, 3) with (M, N, K) per expert
    """
    return torch.ops._C.cutlass_mxfp4_group_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        problem_sizes,
        expert_offsets,
        sf_offsets,
    )


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def mxfp8_experts_quant(
    input_tensor: torch.Tensor,
    problem_sizes: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    quant_output: torch.Tensor,
    scale_factor: torch.Tensor,
) -> None:
    torch.ops._C.mxfp8_experts_quant(
        input_tensor,
        problem_sizes,
        expert_offsets,
        blockscale_offsets,
        quant_output,
        scale_factor,
    )


def cutlass_mxfp8_grouped_mm(
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    out_tensors: torch.Tensor,
    problem_sizes: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
) -> None:
    torch.ops._C.cutlass_mxfp8_grouped_mm(
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        out_tensors,
        problem_sizes,
        expert_offsets,
        blockscale_offsets,
    )


if hasattr(torch.ops._C, "mxfp8_experts_quant"):

    @register_fake("_C::mxfp8_experts_quant")
    def _mxfp8_experts_quant_fake(
        input_tensor: torch.Tensor,
        problem_sizes: torch.Tensor,
        expert_offsets: torch.Tensor,
        blockscale_offsets: torch.Tensor,
        quant_output: torch.Tensor,
        scale_factor: torch.Tensor,
    ) -> None:
        return None


if hasattr(torch.ops._C, "cutlass_mxfp8_grouped_mm"):

    @register_fake("_C::cutlass_mxfp8_grouped_mm")
    def _cutlass_mxfp8_grouped_mm_fake(
        a_tensors: torch.Tensor,
        b_tensors: torch.Tensor,
        a_scales: torch.Tensor,
        b_scales: torch.Tensor,
        out_tensors: torch.Tensor,
        problem_sizes: torch.Tensor,
        expert_offsets: torch.Tensor,
        blockscale_offsets: torch.Tensor,
    ) -> None:
        return None


1255
# 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,
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    is_a_8bit: bool = False,
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) -> 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,
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        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
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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,
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) -> 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
    )
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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,
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        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,
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    is_a_8bit: bool = False,
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) -> 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,
    )
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    for e in range(num_experts):
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        output[e] = torch.ops._C.gptq_marlin_repack(
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            b_q_weight[e], perm[e], size_k, size_n, num_bits, is_a_8bit
1339
        )
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    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,
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    is_a_8bit: bool = False,
1350
) -> 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,
    )
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    for e in range(num_experts):
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        output[e] = torch.ops._C.awq_marlin_repack(
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            b_q_weight[e], size_k, size_n, num_bits, is_a_8bit
1361
        )
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    return output


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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)


1373
def marlin_gemm(
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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: 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:
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    return torch.ops._C.marlin_gemm(
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        a,
        c,
        b_q_weight,
        b_bias,
        b_scales,
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        a_scales,
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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,
    )
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if hasattr(torch.ops._C, "marlin_gemm"):

    @register_fake("_C::marlin_gemm")
    def _marlin_gemm_fake(
        a: torch.Tensor,
        c: torch.Tensor | None,
        b_q_weight: torch.Tensor,
        b_bias: torch.Tensor | None,
        b_scales: torch.Tensor,
        a_scales: torch.Tensor | None,
        global_scale: torch.Tensor | None,
        b_zeros: torch.Tensor | None,
        g_idx: torch.Tensor | None,
        perm: torch.Tensor | None,
        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:
        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)


1447
# machete
1448
def machete_supported_schedules(
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    a_type: torch.dtype,
    b_type: ScalarType,
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    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,
1456
) -> list[str]:
1457
    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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def machete_mm(
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    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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) -> 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,
    )
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if hasattr(torch.ops._C, "machete_mm"):

    @register_fake("_C::machete_mm")
    def machete_mm_fake(
        a: torch.Tensor,
        # b_q Should be the tensor returned by machete_prepack_B
        b_q: torch.Tensor,
        b_type: ScalarType,
        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,
    ) -> torch.Tensor:
        m = a.size(0)
        n = b_q.size(1)
        return torch.empty((m, n), device=a.device, dtype=a.dtype)


1516
def machete_prepack_B(
1517
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    b_q_weight: torch.Tensor,
    a_type: torch.dtype,
    b_type: ScalarType,
1520
    group_scales_type: torch.dtype | None,
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) -> torch.Tensor:
    return torch.ops._C.machete_prepack_B(
        b_q_weight, a_type, b_type.id, group_scales_type
    )
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if hasattr(torch.ops._C, "machete_prepack_B"):

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


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# CUTLASS W4A8
def cutlass_w4a8_mm(
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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:
    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,
    )
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if hasattr(torch.ops._C, "cutlass_w4a8_mm"):

    @register_fake("_C::cutlass_w4a8_mm")
    def cutlass_w4a8_mm_fake(
        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,
        out_type: torch.dtype | None = None,
        maybe_schedule: str | None = None,
    ) -> torch.Tensor:
        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)


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


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if hasattr(torch.ops._C, "cutlass_pack_scale_fp8"):

    @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)


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


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1604
if hasattr(torch.ops._C, "cutlass_encode_and_reorder_int4b"):

    @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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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)


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if hasattr(torch.ops._C, "cutlass_encode_and_reorder_int4b_grouped"):
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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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def permute_cols(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.permute_cols(a, perm)


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

    @register_fake("_C::permute_cols")
    def _permute_cols_fake(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(a)


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# fp4
def scaled_fp4_quant(
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    input: torch.Tensor,
    input_global_scale: torch.Tensor,
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    is_sf_swizzled_layout: bool = True,
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    backend: str = "none",
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) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    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.
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        use_8x4_sf_layout: Whether to use the 8x4 or 128x4 layout for the scaling
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    Returns:
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        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP4 but every
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            two values are packed into a uint8 and float8_e4m3 scaling factors
            in the sizzled layout.
    """
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    assert not current_platform.is_rocm()
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    assert input.ndim >= 1, f"input.ndim needs to be >= 1, but got {input.ndim}."
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    other_dims = 1 if input.ndim == 1 else -1
    input = input.reshape(other_dims, input.shape[-1])
    m, n = input.shape
    block_size = 16

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    assert n % block_size == 0, f"last dim has to be multiple of 16, but got {n}."
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    assert input.dtype in (torch.float16, torch.bfloat16), (
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        f"input.dtype needs to be fp16 or bf16 but got {input.dtype}."
    )
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    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:
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        # Pre-allocate and call .out variant (same behavior as old in-place API)
        output, output_scale = create_fp4_output_tensors(
            m, n, input.device, is_sf_swizzled_layout
        )
        torch.ops._C.scaled_fp4_quant.out(
            input,
            input_global_scale,
            is_sf_swizzled_layout,
            output=output,
            output_scale=output_scale,
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        )

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    output_scale = output_scale.view(torch.float8_e4m3fn)
    return output, output_scale


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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]:
    """
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    Quantize input tensor to NVFP4 and return quantized tensor and scale, for
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    packed MoE Inputs.
    Args:
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        input_tensor: The input tensor to be quantized to NVFP4
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        input_global_scale: A scalar scaling factor for the entire tensor.
        expert_offsets: The expert offsets tensor
        blockscale_offsets: The blockscale offsets tensor
    Outputs:
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        output: The quantized tensor in NVFP4
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        output_scales: The blockscale tensor in FP8-E4M3
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2, (
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        f"input.ndim needs to be == 2, but got {input_tensor.ndim}."
    )
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    # 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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def mxfp4_experts_quant(
    input_tensor: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    n_experts: int,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Quantize input tensor to MXFP4 for packed MoE inputs.
    Uses 32-element blocks with E8M0 (power-of-two) scale factors.
    MXFP4 has no global scale - only block-level E8M0 scale factors.

    Args:
        input_tensor: [m_topk, k] BF16/FP16 activations
        expert_offsets: [n_experts+1] token boundaries per expert
        blockscale_offsets: [n_experts+1] SF row boundaries per expert
        n_experts: number of experts
        topk: number of top-k experts
    Returns:
        output: [m_topk, k//2] packed E2M1 values (uint8)
        output_scales: E8M0 blockscales in swizzled layout (uint8 view)
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2

    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
    m_numtopk, k = input_tensor.shape

    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_mxfp4, observed m_numtopk = {m_numtopk}. Use"
        f" VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE to set this value."
    )
    scales_k = k // 32
    padded_k = (scales_k + (4 - 1)) // 4

    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.mxfp4_experts_quant(
        output,
        output_scales,
        input_tensor,
        expert_offsets,
        blockscale_offsets,
        n_experts,
    )
    # E8M0 SFs are stored as uint8
    output_scales = output_scales.view(torch.uint8)
    return output, output_scales


def silu_and_mul_mxfp4_experts_quant(
    input_tensor: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    n_experts: int,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Fused SiLU+Mul+MXFP4 quantization for MoE intermediate activations.
    MXFP4 has no global scale - only block-level E8M0 scale factors.
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2

    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
    scales_k = k // 32
    padded_k = (scales_k + (4 - 1)) // 4

    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_mxfp4_experts_quant(
        output,
        output_scales,
        input_tensor,
        expert_offsets,
        blockscale_offsets,
        n_experts,
    )
    output_scales = output_scales.view(torch.uint8)
    return output, output_scales


1986
# fp8
1987
1988
def scaled_fp8_quant(
    input: torch.Tensor,
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1991
    scale: torch.Tensor | None = None,
    num_token_padding: int | None = None,
    scale_ub: torch.Tensor | None = None,
1992
    use_per_token_if_dynamic: bool = False,
1993
    output: torch.Tensor | None = None,
1994
    group_shape: tuple[int, int] | None = None,
1995
) -> tuple[torch.Tensor, torch.Tensor]:
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2000
2001
    """
    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
2002
    optional padding of the output tensors for downstream kernels that
2003
2004
2005
    will benefit from padding.

    Args:
2006
2007
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2009
2010
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2012
        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))
2013
        scale_ub: Optional upper bound for scaling factor in dynamic
2014
            per token case
2015
        num_token_padding: If specified, pad the first dimension
2016
            of the output to at least this value.
2017
        use_per_token_if_dynamic: Whether to do per_tensor or per_token
2018
            in the dynamic quantization case.
2019
2020
2021
2022
        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.
2023
2024

    Returns:
2025
        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
2026
2027
            scaling factor.
    """
2028
    # This code assumes batch_dim and num_tokens are flattened
2029
    assert input.ndim == 2
2030
    shape: tuple[int, int] | torch.Size = input.shape
2031
2032
    # For ROCm on MI300, the output fp8 dtype is torch.float_e3m3fnuz
    out_dtype: torch.dtype = current_platform.fp8_dtype()
2033
2034
    if num_token_padding:
        shape = (max(num_token_padding, input.shape[0]), shape[1])
2035
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2037
    if output is None:
        output = torch.empty(shape, device=input.device, dtype=out_dtype)
    else:
2038
        assert num_token_padding is None, "padding not supported if output passed in"
2039
        assert output.dtype == out_dtype
2040

2041
    if scale is None:
2042
        if use_per_token_if_dynamic:
2043
            scale = torch.empty((shape[0], 1), device=input.device, dtype=torch.float32)
2044
            torch.ops._C.dynamic_per_token_scaled_fp8_quant(
2045
2046
                output, input, scale, scale_ub
            )
2047
        else:
2048
            scale = torch.empty(1, device=input.device, dtype=torch.float32)
2049
            torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
2050
    else:
2051
        torch.ops._C.static_scaled_fp8_quant(output, input, scale, group_shape)
2052

2053
    return output, scale
2054
2055


2056
2057
# gptq allspark
def allspark_repack_weight(
2058
2059
    qweight: torch.Tensor,
    scale: torch.Tensor,
2060
    zero_point: torch.Tensor | None = None,
2061
    has_zp: bool = False,
2062
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
2063
    """
2064
    Rearrange qweight, scale, and zero_point(if asymmetric) to n32k16 format
2065
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2067
2068
2069
2070
2071
2072
    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.
2073
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            if use asymmetric quantization, has_zp = True.

2075
    Returns:
2076
        tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] :
2077
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2080
2081
2082
            rearranged weight, scale, and optionally zero_point.
    """
    K = qweight.shape[0]
    N = qweight.shape[1]
    N_32align = (N + 32 - 1) // 32 * 32

2083
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2086
    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)
2087
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2089
    zero_point_reorder = None
    if has_zp:
        assert zero_point is not None, (
2090
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2093
2094
            "zero_point must be provided for asymmetric quantization."
        )
        zero_point_reorder = torch.empty(
            (1, N_32align), device=zero_point.device, dtype=zero_point.dtype
        )
2095
2096

    torch.ops._C.rearrange_kn_weight_as_n32k16_order(
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2104
2105
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2107
        qweight,
        scale,
        zero_point,
        has_zp,
        qweight_reorder,
        scale_reorder,
        zero_point_reorder,
        K,
        N,
        N_32align,
    )
2108
2109
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2111

    return qweight_reorder, scale_reorder, zero_point_reorder


2112
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def allspark_w8a16_gemm(
    a: torch.Tensor,
    b_qweight: torch.Tensor,
    b_scales: torch.Tensor,
2116
    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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2139


2140
# int8
2141
def scaled_int8_quant(
2142
    input: torch.Tensor,
2143
2144
    scale: torch.Tensor | None = None,
    azp: torch.Tensor | None = None,
2145
    symmetric: bool = True,
2146
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
2147
    """
2148
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.
2149
2150
2151

    Args:
        input: The input tensor to be quantized to int8.
2152
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        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.
2154
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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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2158

    Returns:
2159
      tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] : Output int8 tensor, scales, and optionally azp.
2160
    """
2161
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    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
2164
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2166
        assert symmetric == (azp is None), (
            "azp must only be provided for asymmetric quantization."
        )
2167
        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
2168
        return output, scale, azp
2169
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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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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,
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    null_block_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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    cu_chunk_seqlen: torch.Tensor | None = None,
    last_chunk_indices: 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,
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        null_block_id,
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        block_size,
        block_idx_first_scheduled_token,
        block_idx_last_scheduled_token,
        initial_state_idx,
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        cu_chunk_seqlen,
        last_chunk_indices,
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    )
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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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    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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    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:
    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:
    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 router_gemm_bf16_fp32(input: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
    """bf16 x bf16 -> fp32 GEMM via cuBLAS. weight shape: (N, K)."""
    return torch.ops._moe_C.router_gemm_bf16_fp32(input, weight)


if hasattr(torch.ops, "_moe_C") and hasattr(torch.ops._moe_C, "router_gemm_bf16_fp32"):

    @register_fake("_moe_C::router_gemm_bf16_fp32")
    def router_gemm_bf16_fp32_fake(
        input: torch.Tensor,
        weight: torch.Tensor,
    ) -> torch.Tensor:
        return torch.empty(
            input.shape[0], weight.shape[0], dtype=torch.float32, device=input.device
        )


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def dsv3_router_gemm(
    hidden_states: torch.Tensor,
    router_weight: torch.Tensor,
    output_dtype: torch.dtype,
) -> torch.Tensor:
    output = torch.empty(
        hidden_states.shape[0],
        router_weight.shape[0],
        device=hidden_states.device,
        dtype=output_dtype,
    )
    torch.ops._moe_C.dsv3_router_gemm(output, hidden_states, router_weight)
    return output


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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,
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    )
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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,
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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,
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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,
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) -> torch.Tensor:
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    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,
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    )
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if hasattr(torch.ops, "_moe_C") and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):
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    @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,
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    ):
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        return torch.empty(
            (size_m * top_k, size_n), dtype=input.dtype, device=input.device
        )
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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,
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) -> 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,
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) -> 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,
    )


2752
def swap_blocks(
2753
2754
2755
2756
    src: torch.Tensor,
    dst: torch.Tensor,
    block_size_in_bytes: int,
    block_mapping: torch.Tensor,
2757
) -> None:
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2766
2767
2768
2769
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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)
2779
2780


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def swap_blocks_batch(
    src_ptrs: torch.Tensor,
    dst_ptrs: torch.Tensor,
    sizes: torch.Tensor,
) -> None:
    """
    Batch version of swap_blocks: submit all copies in a single driver call.

    Each entry specifies a raw pointer copy: src_ptrs[i] -> dst_ptrs[i]
    of sizes[i] bytes. All three tensors must be int64 CPU tensors.
    On CUDA 12.8+ this uses cuMemcpyBatchAsync for minimal submission
    overhead; on older CUDA it falls back to a loop of cudaMemcpyAsync.
    """
    torch.ops._C_cache_ops.swap_blocks_batch(src_ptrs, dst_ptrs, sizes)


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def convert_fp8(
    output: torch.Tensor, input: torch.Tensor, scale: float = 1.0, kv_dtype: str = "fp8"
) -> None:
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2802
    torch.ops._C_cache_ops.convert_fp8(output, input, scale, kv_dtype)


2803
def gather_and_maybe_dequant_cache(
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    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
2808
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    token_to_seq: torch.Tensor,
    num_tokens: int,
2810
2811
    kv_cache_dtype: str,
    scale: torch.Tensor,
2812
    seq_starts: torch.Tensor | None = None,
2813
) -> None:
2814
    torch.ops._C_cache_ops.gather_and_maybe_dequant_cache(
2815
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2817
2818
        src_cache,
        dst,
        block_table,
        cu_seq_lens,
2819
2820
        token_to_seq,
        num_tokens,
2821
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        kv_cache_dtype,
        scale,
        seq_starts,
    )
2825
2826


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2832
def cp_gather_cache(
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
    batch_size: int,
2833
    seq_starts: torch.Tensor | None = None,
2834
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2837
) -> None:
    torch.ops._C_cache_ops.cp_gather_cache(
        src_cache, dst, block_table, cu_seq_lens, batch_size, seq_starts
    )
2838
2839


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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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def concat_mla_q(
    ql_nope: torch.Tensor,
    q_pe: torch.Tensor,
    q_out: torch.Tensor,
) -> None:
    """Concatenate query nope and rope for MLA/DSA attention.

    Args:
        ql_nope: Query nope component [num_tokens, num_heads, nope_dim]
        q_pe: Query rope component [num_tokens, num_heads, rope_dim]
        q_out: Output tensor [num_tokens, num_heads, nope_dim + rope_dim]
    """
    torch.ops._C_cache_ops.concat_mla_q(ql_nope, q_pe, q_out)


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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
    )
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2889


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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
    )


2902
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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(
2909
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        device
    )
2911
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2913


# custom ar
2914
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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
    )
2923
2924


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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)
2933

2934
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2937
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2940
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2942

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


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


2943
def register_buffer(fa: int, ipc_tensors: list[int]) -> None:
2944
    return torch.ops._C_custom_ar.register_buffer(fa, ipc_tensors)
2945
2946


2947
def get_graph_buffer_ipc_meta(fa: int) -> tuple[list[int], list[int]]:
2948
2949
2950
    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


2951
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2953
def register_graph_buffers(
    fa: int, handles: list[list[int]], offsets: list[list[int]]
) -> None:
2954
    torch.ops._C_custom_ar.register_graph_buffers(fa, handles, offsets)
2955
2956


2957
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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)


2969
# quick all reduce
2970
def init_custom_qr(rank: int, world_size: int, qr_max_size: int | None = None) -> int:
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    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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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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2990
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2993
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2999


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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3003
def get_flash_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_heads_per_head_k: int,
    num_heads_k: int,
3004
) -> tuple[torch.Tensor, torch.Tensor]:
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3014
    """
    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.
    """
3015
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3017
    return torch.ops._C.get_flash_mla_metadata(
        cache_seqlens, num_heads_per_head_k, num_heads_k
    )
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3020
3021
3022
3023
3024
3025
3026
3027


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,
3028
    softmax_scale: float | None = None,
3029
    causal: bool = False,
3030
) -> 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:
3048
        softmax_scale = q.shape[-1] ** (-0.5)
3049
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3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
    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
3062
3063


3064
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3067
3068
3069
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3071
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3087
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,
    )
3088
3089
3090
    return out


3091
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3093
def sm100_cutlass_mla_get_workspace_size(
    max_seq_len: int, num_batches: int, sm_count: int, num_kv_splits: int
) -> int:
3094
    return torch.ops._C.sm100_cutlass_mla_get_workspace_size(
3095
3096
        max_seq_len, num_batches, sm_count, num_kv_splits
    )
3097
3098


3099
3100
3101
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3104
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3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
def dsv3_fused_a_gemm(
    output: torch.Tensor,
    mat_a: torch.Tensor,
    mat_b: torch.Tensor,
) -> None:
    """DeepSeek V3 fused A GEMM (SM 9.0+, bf16 only, 1-16 tokens).

    Computes output = mat_a @ mat_b.T where:
      mat_a: [num_tokens, 7168] row-major bf16 (hidden states)
      mat_b: [7168, 2112] column-major bf16 (weight transposed)
      output: [num_tokens, 2112] row-major bf16

    Optimized for the DeepSeek V2/V3 QKV A-projection at small batch sizes.
    Requires SM 9.0+ (Hopper).
    """
    torch.ops._C.dsv3_fused_a_gemm(output, mat_a, mat_b)


3117
3118
3119
if hasattr(torch.ops._C, "weight_packed_linear"):

    @register_fake("_C::weight_packed_linear")
3120
3121
3122
    def weight_packed_linear_fake(
        mat1: torch.Tensor,
        mat2: torch.Tensor,
3123
        bias: torch.Tensor | None,
3124
3125
3126
3127
3128
        is_vnni: bool,
    ) -> torch.Tensor:
        return torch.empty(
            (mat1.size(0), mat2.size(0)), dtype=mat1.dtype, device=mat2.device
        )
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142


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,
3143
3144
3145
3146
3147
        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,
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
        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,
3160
        bias: torch.Tensor | None,
3161
3162
3163
3164
3165
3166
        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)
3167
3168


3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
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3182
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3186
3187
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3191
3192
3193
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3195
3196
3197
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3200
if hasattr(torch.ops._C, "convert_weight_packed_scale_zp"):

    @register_fake("_C::convert_weight_packed_scale_zp")
    def convert_weight_packed_scale_zp_fake(
        qweight: torch.Tensor,
        qzeros: torch.Tensor,
        scales: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        return (
            torch.empty_like(qweight),
            torch.empty_like(qzeros),
            torch.empty_like(scales),
        )


if hasattr(torch.ops._C, "int4_scaled_mm_cpu"):

    @register_fake("_C::int4_scaled_mm_cpu")
    def int4_scaled_mm_cpu_fake(
        x: torch.Tensor,
        w: torch.Tensor,
        w_zeros: torch.Tensor,
        w_scales: torch.Tensor,
        bias: torch.Tensor | None,
    ) -> torch.Tensor:
        N = w_scales.size(0) * w_scales.size(-1)
        return torch.empty((x.size(0), N), dtype=x.dtype, device=x.device)


_supports_cpu_w4a8_int8 = bool(hasattr(torch.ops._C, "convert_weight_packed_scale_zp"))


3201
3202
class CPUDNNLGEMMHandler:
    def __init__(self) -> None:
3203
        self.handler_tensor: torch.Tensor | None = None
3204
3205
3206
3207
        self.n = -1
        self.k = -1

    def __del__(self):
3208
3209
        if self.handler_tensor is not None:
            torch.ops._C.release_dnnl_matmul_handler(self.handler_tensor.item())
3210
3211


3212
_supports_onednn = bool(hasattr(torch.ops._C, "create_onednn_mm_handler"))
3213
3214


3215
3216
3217
3218
def is_onednn_acl_supported():
    return torch.ops._C.is_onednn_acl_supported()


3219
3220
3221
3222
3223
3224
def create_onednn_mm(
    weight: torch.Tensor,  # [K, N]
    primitive_cache_size: int = 128,
) -> CPUDNNLGEMMHandler:
    handler = CPUDNNLGEMMHandler()
    handler.k, handler.n = weight.size()
3225
3226
3227
3228
    # store the handler pointer in a tensor it doesn't get inlined
    handler.handler_tensor = torch.tensor(
        torch.ops._C.create_onednn_mm_handler(weight, primitive_cache_size),
        dtype=torch.int64,
3229
    )
3230
3231
3232
3233
3234
3235
    return handler


def onednn_mm(
    dnnl_handler: CPUDNNLGEMMHandler,
    x: torch.Tensor,
3236
    bias: torch.Tensor | None,
3237
3238
) -> torch.Tensor:
    output = torch.empty((*x.shape[0:-1], dnnl_handler.n), dtype=x.dtype)
3239
    torch.ops._C.onednn_mm(
3240
        output, x.reshape(-1, dnnl_handler.k), bias, dnnl_handler.handler_tensor
3241
    )
3242
3243
3244
3245

    return output


3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
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()
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
    # store the handler pointer in a tensor so it doesn't get inlined
    handler.handler_tensor = torch.tensor(
        torch.ops._C.create_onednn_scaled_mm_handler(
            weight,
            weight_scales,
            output_type,
            dynamic_quant,
            use_azp,
            primitive_cache_size,
        ),
        dtype=torch.int64,
3267
    )
3268
3269
3270
    return handler


3271
3272
def onednn_scaled_int8_quant(
    input: torch.Tensor,
3273
3274
    scale: torch.Tensor | None = None,
    azp: torch.Tensor | None = None,
3275
3276
    symmetric: bool = True,
):
3277
3278
3279
3280
3281
3282
3283
3284
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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(
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        output,
        x,
        input_scale,
        input_zp,
        input_zp_adj,
        bias,
        dnnl_handler.handler_tensor,
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    )
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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:
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    scheduler_metadata = torch.ops._C.get_scheduler_metadata(
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        num_reqs,
        num_heads,
        num_kv_heads,
        head_dim,
        seq_lens,
        dtype,
        query_start_loc,
        causal,
        sliding_window_size,
        isa,
        enable_kv_split,
    )
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    return scheduler_metadata
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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_activation_lut_bf16(input: torch.Tensor, activation: str) -> torch.Tensor:
    out = torch.empty_like(input)
    torch.ops._C.activation_lut_bf16(out, input, activation)
    return out


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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,
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    skip_weighted: bool = False,
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) -> 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,
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        skip_weighted,
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        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
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if hasattr(torch.ops._C, "minimax_allreduce_rms"):

    @register_fake("_C::minimax_allreduce_rms")
    def _minimax_allreduce_rms_fake(
        input: torch.Tensor,
        norm_weight: torch.Tensor,
        workspace: torch.Tensor,
        rank: int,
        nranks: int,
        eps: float,
    ) -> torch.Tensor:
        return torch.empty_like(input)


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

    @register_fake("_C::minimax_allreduce_rms_qk")
    def _minimax_allreduce_rms_qk_fake(
        qkv: torch.Tensor,
        norm_weight_q: torch.Tensor,
        norm_weight_k: torch.Tensor,
        workspace: torch.Tensor,
        q_size: int,
        kv_size: int,
        rank: int,
        nranks: int,
        eps: float,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        token_num = qkv.shape[0]
        return (
            torch.empty([token_num, q_size], dtype=qkv.dtype, device=qkv.device),
            torch.empty([token_num, kv_size], dtype=qkv.dtype, device=qkv.device),
        )