_custom_ops.py 104 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, Optional
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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.torch_utils import direct_register_custom_op
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try:
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    from lmslim import quant_ops 
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    from lmslim import quant_tools 
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except Exception:
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    print("INFO: Please install lmslim if you want to infer gptq or awq  or w8a8 model.\n") 
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try:
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    import lightop
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except Exception:
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    print("INFO: Please install lightop if you want to infer awq of marlin.\n") 
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logger = init_logger(__name__)

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

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

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


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

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


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# pos encoding ops
def rotary_embedding(
    positions: torch.Tensor,
    query: torch.Tensor,
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    key: torch.Tensor | None,
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    head_size: int,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
) -> None:
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    torch.ops._C.rotary_embedding(
        positions, query, key, head_size, cos_sin_cache, is_neox
    )
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# layer norm ops
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def rms_norm(
    out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, epsilon: float
) -> None:
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    torch.ops._C.rms_norm(out, input, weight, epsilon)
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def fused_add_rms_norm(
    input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, epsilon: float
) -> None:
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    torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)
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def fused_qk_norm_rope(
    qkv: torch.Tensor,
    num_heads_q: int,
    num_heads_k: int,
    num_heads_v: int,
    head_dim: int,
    eps: float,
    q_weight: torch.Tensor,
    k_weight: torch.Tensor,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
    position_ids: torch.Tensor,
) -> None:
    torch.ops._C.fused_qk_norm_rope(
        qkv,
        num_heads_q,
        num_heads_k,
        num_heads_v,
        head_dim,
        eps,
        q_weight,
        k_weight,
        cos_sin_cache,
        is_neox,
        position_ids,
    )
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def apply_repetition_penalties_torch(
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    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
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    repetition_penalties = repetition_penalties.unsqueeze(dim=1).repeat(
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        1, logits.size(1)
    )
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    # If token appears in prompt or output, apply, otherwise use 1.0 for no-op.
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    penalties = torch.where(prompt_mask | output_mask, repetition_penalties, 1.0)
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    # If logits are positive, divide by penalty, otherwise multiply by penalty.
    scaling = torch.where(logits > 0, 1.0 / penalties, penalties)
    logits *= scaling


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

    Args:
        logits: The logits tensor of shape [num_seqs, vocab_size].
        prompt_mask: A boolean tensor indicating which tokens appear in the prompt.
        output_mask: A boolean tensor indicating which tokens appear in the output.
        repetition_penalties: The repetition penalties of shape (num_seqs, ).
    """
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    if logits.is_cuda and logits.is_contiguous():
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        apply_repetition_penalties_cuda(
            logits, prompt_mask, output_mask, repetition_penalties
        )
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    else:
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        apply_repetition_penalties_torch(
            logits, prompt_mask, output_mask, repetition_penalties
        )
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# trans_w16
def trans_w16_gemm(dst: torch.Tensor, src: torch.Tensor,
                row:int, col:int) -> None :
    torch.ops._C.trans_w16_gemm(dst,src,row,col)
    
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# fused quant layer norm ops
def rms_norm_dynamic_per_token_quant(
    input: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
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    scale_ub: torch.Tensor | None = None,
    residual: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    output = torch.empty_like(input, dtype=quant_dtype)
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    scales = torch.empty(
        (input.numel() // input.shape[-1], 1), device=input.device, dtype=torch.float32
    )
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    torch.ops._C.rms_norm_dynamic_per_token_quant(
        output, input, weight, scales, epsilon, scale_ub, residual
    )
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    return output, scales


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

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


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# quantization ops
# awq
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def GetAWQShareWorkspaceSize()->int:
    return quant_ops.GetAWQShareWorkspaceSize()

def GetAWQShareWorkspace()->torch.Tensor:
    return quant_ops.GetAWQShareWorkspace()

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

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        return awq_dequantize_triton(qweight, scales, zeros)
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    return torch.ops._C.awq_dequantize(qweight, scales, zeros, split_k_iters, thx, thy)
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# def awq_gemm(
#     input: torch.Tensor,
#     qweight: torch.Tensor,
#     scales: torch.Tensor,
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#     qzeros: torch.Tensor,
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#     split_k_iters: int,
# ) -> torch.Tensor:
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#     if envs.VLLM_USE_TRITON_AWQ:
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#         from vllm.model_executor.layers.quantization.awq_triton import awq_gemm_triton
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#         return awq_gemm_triton(input, qweight, scales, qzeros, split_k_iters)
#     return torch.ops._C.awq_gemm(input, qweight, scales, qzeros, split_k_iters)
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def awq_gemm(
    input: torch.Tensor, 
    weight: torch.Tensor,
    zeros_and_scales:torch.Tensor,
    m:int,n:int,k:int,
    group_size:int,
    padding_group:int,
    splikspace:torch.Tensor,
    splikspacesize:int
    ) -> torch.Tensor:
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    return quant_ops.awq_gemm(input,
                              weight,
                              zeros_and_scales,
                              m,
                              n,
                              k,
                              group_size,
                              padding_group,
                              splikspace,
                              splikspacesize)

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def awq_gemm_fake(
    input: torch.Tensor, 
    weight: torch.Tensor,
    zeros_and_scales:torch.Tensor,
    m:int,
    n:int,
    k:int,
    group_size:int,
    padding_group:int,
    splikspace:torch.Tensor,
    splikspacesize:int,
    ) -> torch.Tensor:
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    return torch.empty((m, n), dtype=input.dtype, device=input.device)

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def convert_s4(qw: torch.Tensor, qz: torch.Tensor, s: torch.Tensor,
               group_size: int):
    return quant_ops.convert_s4(qw,qz,s,group_size)

def sz_permute(sz:torch.Tensor)-> torch.Tensor:
    return quant_ops.sz_permute(sz)

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def dequant_w4_gemm_colmajor(
    qweight:torch.Tensor,
    zeros_and_scale:torch.Tensor,
    k:int,
    n:int,
    group_size:int
    )->torch.Tensor:
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    return quant_ops.dequant_w4_gemm_colmajor(qweight,zeros_and_scale,k,n,group_size)
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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:
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    # return torch.ops._C.gptq_gemm(
    #     a,
    #     b_q_weight,
    #     b_gptq_qzeros,
    #     b_gptq_scales,
    #     b_g_idx,
    #     use_exllama,
    #     use_v2_format,
    #     bit,
    # )
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    # return quant_ops.gptq_gemm(
    #     a, 
    #     b_q_weight, 
    #     b_gptq_qzeros, 
    #     b_gptq_scales,
    #     b_g_idx, 
    #     use_exllama, 
    #     bit,
    # )
    return torch.ops.vllm.gptq_gemm_(
        a, 
        b_q_weight, 
        b_gptq_qzeros, 
        b_gptq_scales,
        b_g_idx, 
        use_exllama, 
        bit,
    )

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,
    bit: int) -> torch.Tensor:
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    return quant_ops.gptq_gemm(
        a, 
        b_q_weight, 
        b_gptq_qzeros, 
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        b_gptq_scales,
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        b_g_idx, 
        use_exllama, 
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        bit,
    )
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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,
     bit: int) -> torch.Tensor:
    return torch.empty((a.shape[0], b_gptq_scales.shape[1]), dtype=a.dtype, device=a.device)
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# if hasattr(torch.ops._C, "gptq_gemm"):

#     @register_fake("_C::gptq_gemm")
#     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,
#         use_v2_format: bool,
#         bit: int,
#     ) -> torch.Tensor:
#         return torch.empty(
#             (a.size(0), b_q_weight.size(1)), dtype=a.dtype, device=a.device
#         )
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def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor, bit: int) -> None:
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    # torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
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    quant_ops.gptq_shuffle(q_weight, q_perm, bit)
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# marlin_24
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def gptq_marlin_24_gemm(
    a: torch.Tensor,
    b_q_weight: torch.Tensor,
    b_meta: torch.Tensor,
    b_scales: torch.Tensor,
    workspace: torch.Tensor,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_24_gemm(
        a, b_q_weight, b_meta, b_scales, workspace, b_q_type.id, size_m, size_n, size_k
    )
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# if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):

#     @register_fake("_C::gptq_marlin_24_gemm")
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#     def _gptq_marlin_24_gemm_fake(
#         a: torch.Tensor,
#         b_q_weight: torch.Tensor,
#         b_meta: torch.Tensor,
#         b_scales: torch.Tensor,
#         workspace: torch.Tensor,
#         b_q_type: ScalarType,
#         size_m: torch.SymInt,
#         size_n: torch.SymInt,
#         size_k: torch.SymInt,
#     ) -> torch.Tensor:
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#         return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)

#     @register_fake("_C::gptq_marlin_gemm")
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#     def _gptq_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)
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#     @register_fake("_C::awq_dequantize")
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#     def _awq_dequantize_fake(
#         qweight: torch.Tensor,
#         scales: torch.Tensor,
#         zeros: torch.Tensor,
#         split_k_iters: torch.SymInt,
#         thx: int,
#         thy: int,
#     ) -> torch.Tensor:
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#         in_c = qweight.size(0)
#         qout_c = qweight.size(1)
#         out_c = qout_c * 8
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#         return torch.empty((in_c, out_c), dtype=scales.dtype, device=scales.device)
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#     @register_fake("_C::awq_gemm")
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#     def _awq_gemm_fake(
#         input: torch.Tensor,
#         qweight: torch.Tensor,
#         scales: torch.Tensor,
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#         qzeros: torch.Tensor,
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#         split_k_iters: torch.SymInt,
#     ) -> torch.Tensor:
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#         num_in_feats = input.size(0)
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#         return torch.empty(
#             (split_k_iters, num_in_feats, qweight.size(1) * 8),
#             dtype=input.dtype,
#             device=input.device,
#         ).sum(0)
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#     @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,
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#         out_type: torch.dtype | None = None,
#         b_group_scales: torch.Tensor | None = None,
#         b_group_zeros: torch.Tensor | None = None,
#         b_group_size: int | None = None,
#         b_channel_scales: torch.Tensor | None = None,
#         a_token_scales: torch.Tensor | None = None,
#         schedule: str | None = None,
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#     ) -> torch.Tensor:
#         m = a.size(0)
#         n = b_q.size(1)
#         return torch.empty((m, n), device=a.device, dtype=a.dtype)
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#     @register_fake("_C::machete_prepack_B")
#     def machete_prepack_B_fake(
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#         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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#     @register_fake("_C::cutlass_w4a8_mm")
#     def cutlass_w4a8_mm_fake(
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#         a: torch.Tensor,
#         # b_q Should be the tensor returned by cutlass_encode_and_reorder_int4b
#         b_q: torch.Tensor,
#         b_group_scales: torch.Tensor,
#         b_group_size: int,
#         b_channel_scales: torch.Tensor,
#         a_token_scales: torch.Tensor,
#         out_type: torch.dtype | None = None,
#         maybe_schedule: str | None = None,
#     ) -> torch.Tensor:
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#         m = a.size(0)
#         n = b_q.size(1)
#         out_dtype = out_type if out_type is not None else torch.bfloat16
#         return torch.empty((m, n), device=a.device, dtype=out_dtype)

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

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

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

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# if hasattr(torch.ops._C, "allspark_w8a16_gemm"):
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#     @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,
#         b_qzeros: torch.Tensor | None,
#         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"):
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    @register_fake("_C::ggml_dequantize")
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    def _ggml_dequantize_fake(
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        W: torch.Tensor,
        quant_type: int,
        m: torch.SymInt,
        n: torch.SymInt,
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        dtype: torch.dtype | None = None,
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    ) -> torch.Tensor:
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        return torch.empty((m, n), dtype=torch.float16, device=W.device)

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


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def cutlass_scaled_mm_supports_block_fp8(cuda_device_capability: int) -> bool:
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    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,
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    bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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    """
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        `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
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    # Massage the input to be 2D
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    # target_shape = (*a.shape[:-1], b.shape[1])
    # a = a.view(-1, a.shape[-1])
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    # cutlass_compatible_b = (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
    # if current_platform.is_rocm() or not cutlass_compatible_b:
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    #     from vllm.model_executor.layers.quantization.compressed_tensors.triton_scaled_mm import (  # noqa
    #         triton_scaled_mm)
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    #     out = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    # else:
    #     out = torch.empty((a.shape[0], b.shape[1]),
    #                       dtype=out_dtype,
    #                       device=a.device)
    #     torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)

    # return out.view(*target_shape)
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    return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)
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def rocblas_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
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                      bias: torch.Tensor | None = None) -> torch.Tensor:
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    # cutlass_compatible_b = b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
    # if current_platform.is_rocm() or not cutlass_compatible_b:
    #     from vllm.model_executor.layers.quantization.compressed_tensors.triton_scaled_mm import (  # noqa
    #         triton_scaled_mm,
    #     )

    #     out = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    # else:
    #     out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
    #     torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
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    return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)

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def blaslt_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
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                      bias: torch.Tensor | None = None) -> torch.Tensor:
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    m = a.shape[0]
    n = b.shape[0]
    k = a.shape[1]
    _, out = quant_ops.hipblaslt_w8a8_gemm(a, b, scale_a, scale_b, m, n, k, 'NT', out_dtype)
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    if bias is not None:
        out += bias
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    return out

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def triton_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
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                      bias: torch.Tensor | None = None,
                      best_config: list | None = None) -> torch.Tensor:
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    return quant_ops.triton_scaled_mm(a, b,scale_a,scale_b,out_dtype,bias,best_config)
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def triton_int8_gemm_helper(m: int,
                             n: int,
                             k: int,
                             per_token_act_quant: bool,
                             per_out_channel_weight_quant: bool,
                             use_bias: bool,
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                             out_dtype: type[torch.dtype] = torch.float16,
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                             device: str = "cuda:0",
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                             best_config: list | None = None,
                             repeat: int | None = 2):
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    return quant_tools.triton_int8_gemm_helper(m,n,k,per_token_act_quant,per_out_channel_weight_quant,use_bias,out_dtype,device,best_config,repeat)

def triton_blockint8_gemm_helper(m: int,
                                n: int,
                                k: int,
                                block_size:list=[128,128],
                                use_bias: bool=False,
                                out_dtype: type[torch.dtype] = torch.bfloat16,
                                device: str = "cuda:0",
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                                best_config: dict | None = None,
                                repeat: int | None = 2):
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    return quant_tools.triton_blockint8_gemm_helper(m,n,k,block_size,use_bias,out_dtype,device,best_config,repeat)
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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,
1063
) -> torch.Tensor:
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    """
    :param azp_adj: In the per-tensor case, this should include the azp.
    Always per-channel.
    :param azp: Only set in the per-token case. Per-token if set.
    """
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    assert b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.numel() == b.shape[1] and bias.dtype == out_dtype
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    # Massage the input to be 2D
    target_shape = (*a.shape[:-1], b.shape[1])
    a = a.view(-1, a.shape[-1])
    assert azp is None or azp.numel() == a.shape[0]
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    out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
    torch.ops._C.cutlass_scaled_mm_azp(out, a, b, scale_a, scale_b, azp_adj, azp, bias)
1080
    return out.view(*target_shape)
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1083
def cutlass_sparse_scaled_mm_supported(cuda_device_capability: int) -> bool:
1084
    return torch.ops._C.cutlass_sparse_scaled_mm_supported(cuda_device_capability)
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1086


1087
def cutlass_group_gemm_supported(cuda_device_capability: int) -> bool:
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    try:
        return torch.ops._C.cutlass_group_gemm_supported(cuda_device_capability)
    except AttributeError:
        # Return False on non-CUDA platforms where it is not available
        return False
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def cutlass_sparse_compress(a: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    Compresses a sparse matrix for use with Cutlass sparse operations.

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

    Args:
1104
        a (torch.Tensor):
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            The input tensor to be compressed. Must have one of the following data types:
            - `torch.int8`
            - `torch.float8_e4m3fn`
            - `torch.bfloat16`
            - `torch.float16`

    Returns:
1112
        tuple[torch.Tensor, torch.Tensor]:
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            A tuple containing:
            - `a_nzs` (torch.Tensor): A tensor containing non-zero elements of `a`.
            - `a_meta` (torch.Tensor): A tensor containing metadata for the sparse representation.

    Raises:
        ValueError: If the compression operation fails.

    Notes:
        - The `a_meta` tensor has a data type of `torch.uint8`.
        - Each metadata element encodes the sparsity of 4 non-zero elements (i.e., `elemsPerMetaElem = 4`).
        - The shape of `a_nzs` is `(m, k // 2)`, where `m` and `k` are the dimensions of the input tensor.
        - The shape of `a_meta` is `(m, k // 2 // elemsPerMetaElem)`.
    """
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    assert a.dtype in [torch.int8, torch.float8_e4m3fn, torch.bfloat16, torch.float16]
    assert a.is_contiguous()
1128
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1130

    # a_meta.dtype: torch.uint8 so elemsPerMetaElem = 8b / 2b_per_nz = 4
    elemsPerMetaElem = 4
1131
    assert a.shape[1] % (2 * elemsPerMetaElem) == 0
1132

1133
    return torch.ops._C.cutlass_sparse_compress(a)
1134
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1136


def cutlass_scaled_sparse_mm(
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    a: torch.Tensor,
    bt_nzs: torch.Tensor,
    bt_meta: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
1143
    bias: torch.Tensor | None = None,
1144
) -> torch.Tensor:
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    """
    Performs a scaled sparse matrix multiplication using Cutlass.

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

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

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

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

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

    Returns:
    - The result of the scaled sparse matrix multiplication.
    """
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    assert bt_nzs.shape[0] % 16 == 0 and bt_nzs.shape[1] % 16 == 0
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.shape[0] == bt_nzs.shape[0] and bias.dtype == out_dtype
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    m = a.shape[0]
    n = bt_nzs.shape[0]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)

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


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def get_cutlass_moe_mm_data(
    topk_ids: torch.Tensor,
    expert_offsets: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    input_permutation: torch.Tensor,
    output_permutation: torch.Tensor,
    num_experts: int,
    n: int,
    k: int,
1193
    blockscale_offsets: torch.Tensor | None = None,
1194
):
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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]
1217
    """
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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,
    )
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1231


1232
def get_cutlass_moe_mm_problem_sizes(
1233
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    topk_ids: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    num_experts: int,
    n: int,
    k: int,
1239
    blockscale_offsets: torch.Tensor | None = None,
1240
    force_swap_ab: bool | None = None,
1241
):
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    """
    Compute only the per-expert problem sizes needed by the two grouped matrix
    multiplications used in CUTLASS-based fused MoE.

    The function takes in topk_ids (token→expert mapping) and computes:
    - problem_sizes1, problem_sizes2: M×N×K sizes of each expert's
                                    multiplication for the two grouped MMs
                                    used in the fused MoE operation.
1250
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1253
    Optional:
    - force_swap_ab: If set to True or False, explicitly enable or disable the
                     A/B input swap optimization. If None (default), the swap
                     is selected automatically based on tensor sizes.
1254
1255
    """
    return torch.ops._C.get_cutlass_moe_mm_problem_sizes(
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        topk_ids,
        problem_sizes1,
        problem_sizes2,
        num_experts,
        n,
        k,
        blockscale_offsets,
        force_swap_ab,
1264
    )
1265
1266


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


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def shuffle_rows(input_tensor: torch.Tensor, dst2src_map: torch.Tensor):
    """
    Shuffle and expand the input tensor according to the dst2src_map and store the result in output_tensor.
    This is used in MoE to permute the input tensor before performing grouped matrix multiplications.
    """
    num_tokens_permuted = dst2src_map.shape[0]
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    output_tensor = torch.empty(
        (num_tokens_permuted, input_tensor.shape[1]),
        device=input_tensor.device,
        dtype=input_tensor.dtype,
    )
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    torch.ops._moe_C.shuffle_rows(input_tensor, dst2src_map, output_tensor)
    return output_tensor
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1300


1301
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1310
def get_cutlass_pplx_moe_mm_data(
    expert_offsets: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    expert_num_tokens: torch.Tensor,
    num_local_experts: int,
    padded_m: int,
    n: int,
    k: int,
):
1311
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1315
    """
    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
1316
    non_zero_expert_idxs (consecutive indices of experts with non-zero token
1317
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1320
1321
1322
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1324
    counts) and uses them to compute:
    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation.
    - problem_sizes1, problem_sizes2: MxNxK sizes of each expert's
                                      multiplication in two grouped MMs used in
                                      the fused MoE operation.
    """
    return torch.ops._C.get_cutlass_pplx_moe_mm_data(
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1333
        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        expert_num_tokens,
        num_local_experts,
        padded_m,
        n,
        k,
    )
1334
1335


1336
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1349
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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1360
    """
    A single grouped matrix multiplication used in CUTLASS-based fused MoE.
    The function executes fp8-quantized OUT = AB matrix multiplication.

    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation. The number of tokens computed with
                      expert E is expert_offsets[E + 1] - expert_offsets[E]
    - problem_sizes: MxNxK sizes of each expert's multiplication in two grouped
                     MMs used in the fused MoE operation.
    - a/b/c_strides: The data strides passed to grouped matrix multiplication.
    """
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    return torch.ops._C.cutlass_moe_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        expert_offsets,
        problem_sizes,
        a_strides,
        b_strides,
        c_strides,
        per_act_token,
        per_out_ch,
    )
1375
1376


1377
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1387
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,
):
1388
    """
1389
    An FP4 Blockscaled Group Gemm that takes in  a_tensors, b_tensors and runs
1390
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1395
    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
1396
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1399
    - 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
1400
1401
1402
1403
                    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.
    """
1404
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1410
1411
1412
1413
1414
    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,
    )
1415
1416


1417
# gptq_marlin
1418
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1423
def gptq_marlin_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1424
    is_a_8bit: bool = False,
1425
) -> torch.Tensor:
1426
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1428
    return torch.ops._C.gptq_marlin_repack(
        b_q_weight, perm, size_k, size_n, num_bits, is_a_8bit
    )
1429
1430


1431
1432
1433
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1439
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,
1440
        is_a_8bit: bool = False,
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
    ) -> 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
1452
def awq_marlin_repack(
1453
1454
1455
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1457
    b_q_weight: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
    is_a_8bit: bool = False,
1458
) -> torch.Tensor:
1459
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1461
    return torch.ops._C.awq_marlin_repack(
        b_q_weight, size_k, size_n, num_bits, is_a_8bit
    )
1462
1463


1464
if hasattr(torch.ops._C, "awq_marlin_repack"):
1465

1466
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1469
1470
1471
    @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,
1472
        is_a_8bit: bool = False,
1473
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1476
1477
1478
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1481
1482
    ) -> 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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1488
def gptq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1489
    is_a_8bit: bool = False,
1490
) -> torch.Tensor:
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1492
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
1493
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1495
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1497
    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
1498
    for e in range(num_experts):
1499
        output[e] = torch.ops._C.gptq_marlin_repack(
1500
            b_q_weight[e], perm[e], size_k, size_n, num_bits, is_a_8bit
1501
        )
1502
1503
1504
    return output


1505
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1510
def awq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1511
    is_a_8bit: bool = False,
1512
) -> torch.Tensor:
1513
1514
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
1515
1516
1517
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1519
    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
1520
    for e in range(num_experts):
1521
        output[e] = torch.ops._C.awq_marlin_repack(
1522
            b_q_weight[e], size_k, size_n, num_bits, is_a_8bit
1523
        )
1524
1525
1526
    return output


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1532
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1534
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)


1535
1536
def gptq_marlin_gemm(
    a: torch.Tensor,
1537
    c: torch.Tensor | None,
1538
    b_q_weight: torch.Tensor,
1539
    b_bias: torch.Tensor | None,
1540
    b_scales: torch.Tensor,
1541
    a_scales: torch.Tensor | None,
1542
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1544
1545
    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:
    return torch.ops._C.gptq_marlin_gemm(
        a,
        c,
        b_q_weight,
        b_bias,
        b_scales,
1562
        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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# machete
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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,
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) -> list[str]:
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    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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def machete_prepack_B(
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    b_q_weight: torch.Tensor,
    a_type: torch.dtype,
    b_type: ScalarType,
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    group_scales_type: torch.dtype | None,
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) -> torch.Tensor:
    return torch.ops._C.machete_prepack_B(
        b_q_weight, a_type, b_type.id, group_scales_type
    )
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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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def cutlass_pack_scale_fp8(scales: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.cutlass_pack_scale_fp8(scales)


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


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


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


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# fp4
def scaled_fp4_quant(
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    input: torch.Tensor, input_global_scale: torch.Tensor
) -> 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.

    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
    device = input.device

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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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    # Two fp4 values will be packed into an uint8.
    output = torch.empty((m, n // 2), device=device, dtype=torch.uint8)

    # We use the rounded values to store the swizzled values. Due to the
    # requirement of the Tensor Core, the minimum tile is 128x4 for the scales.
    # So, we first pad the scales to multiples of 128 and 4. Then, the scales
    # (in float8_e4m3fn) are packed into an int32 for every 4 values. More:
    # https://docs.nvidia.com/cuda/parallel-thread-execution/#tcgen05-mma-scale-factor-b-layout-4x
    round_up = lambda x, y: (x + y - 1) // y * y
    rounded_m = round_up(m, 128)
    scale_n = n // block_size
    rounded_n = round_up(scale_n, 4)
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    output_scale = torch.empty(
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        (rounded_m, rounded_n // 4), device=device, dtype=torch.int32
    )
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    torch.ops._C.scaled_fp4_quant(output, input, output_scale, input_global_scale)
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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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# fp8
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# def scaled_fp8_quant(
#     input: torch.Tensor,
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#     scale: torch.Tensor | None = None,
#     num_token_padding: int | None = None,
#     scale_ub: torch.Tensor | None = None,
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#     use_per_token_if_dynamic: bool = False,
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#     output: torch.Tensor | None = None,
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#     group_shape: tuple[int, int] | None = None,
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# ) -> tuple[torch.Tensor, torch.Tensor]:
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#     """
#     Quantize input tensor to FP8 and return quantized tensor and scale.

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

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

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

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

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


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


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


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


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# mamba
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def selective_scan_fwd(
    u: torch.Tensor,
    delta: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
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    D_: torch.Tensor | None,
    z_: torch.Tensor | None,
    delta_bias_: torch.Tensor | None,
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    delta_softplus: bool,
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    query_start_loc: torch.Tensor | None,
    cache_indices: torch.Tensor | None,
    has_initial_state: torch.Tensor | None,
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    ssm_states: torch.Tensor,
    pad_slot_id: int,
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    block_size: int = 1024,
    block_idx_first_scheduled_token: torch.Tensor | None = None,
    block_idx_last_scheduled_token: torch.Tensor | None = None,
    initial_state_idx: torch.Tensor | None = None,
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):
    torch.ops._C.selective_scan_fwd(
        u,
        delta,
        A,
        B,
        C,
        D_,
        z_,
        delta_bias_,
        delta_softplus,
        query_start_loc,
        cache_indices,
        has_initial_state,
        ssm_states,
        pad_slot_id,
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        block_size,
        block_idx_first_scheduled_token,
        block_idx_last_scheduled_token,
        initial_state_idx,
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    )
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# 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 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_sum_opt1(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum_opt1(input, output)
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def moe_align_block_size(
    topk_ids: torch.Tensor,
    num_experts: int,
    block_size: int,
    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
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    expert_map: torch.Tensor | None = None,
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) -> None:
    torch.ops._moe_C.moe_align_block_size(
        topk_ids,
        num_experts,
        block_size,
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
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        expert_map,
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    )
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def batched_moe_align_block_size(
    max_tokens_per_batch: int,
    block_size: int,
    expert_num_tokens: torch.Tensor,
    sorted_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
) -> None:
    torch.ops._moe_C.batched_moe_align_block_size(
        max_tokens_per_batch,
        block_size,
        expert_num_tokens,
        sorted_ids,
        expert_ids,
        num_tokens_post_pad,
    )


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def moe_lora_align_block_size(
    topk_ids: torch.Tensor,
    token_lora_mapping: torch.Tensor,
    num_experts: int,
    block_size: int,
    max_loras: int,
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    max_num_tokens_padded: int,
    max_num_m_blocks: int,
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    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
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    adapter_enabled: torch.Tensor,
    lora_ids: torch.Tensor,
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    expert_map: torch.Tensor | None = None,
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) -> None:
    torch.ops._moe_C.moe_lora_align_block_size(
        topk_ids,
        token_lora_mapping,
        num_experts,
        block_size,
        max_loras,
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        max_num_tokens_padded,
        max_num_m_blocks,
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        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
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        adapter_enabled,
        lora_ids,
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        expert_map,
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    )
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def moe_wna16_gemm(
    input: torch.Tensor,
    output: torch.Tensor,
    b_qweight: torch.Tensor,
    b_scales: torch.Tensor,
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    b_qzeros: torch.Tensor | None,
    topk_weights: torch.Tensor | None,
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    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
    top_k: int,
    BLOCK_SIZE_M: int,
    BLOCK_SIZE_N: int,
    BLOCK_SIZE_K: int,
    bit: int,
) -> torch.Tensor:
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    if not current_platform.is_cuda():
        raise NotImplementedError(
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            "The optimized moe_wna16_gemm kernel is only available on CUDA platforms"
        )
    torch.ops._moe_C.moe_wna16_gemm(
        input,
        output,
        b_qweight,
        b_scales,
        b_qzeros,
        topk_weights,
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
        top_k,
        BLOCK_SIZE_M,
        BLOCK_SIZE_N,
        BLOCK_SIZE_K,
        bit,
    )
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def topk_softmax(
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    token_expert_indices: torch.Tensor,
    gating_output: torch.Tensor,
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    renormalize: bool = False,
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) -> None:
    torch.ops._moe_C.topk_softmax(
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        topk_weights, topk_ids, token_expert_indices, gating_output, renormalize
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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.
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    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_cuda(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache_cuda(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,
#     )
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def swap_blocks(
    src: torch.Tensor, dst: torch.Tensor, block_mapping: torch.Tensor
) -> None:
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    torch.ops._C_cache_ops.swap_blocks(src, dst, block_mapping)
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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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    torch.ops._C_cache_ops.convert_fp8(output, input, scale, kv_dtype)


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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,
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    token_to_seq: torch.Tensor,
    num_tokens: int,
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    kv_cache_dtype: str,
    scale: torch.Tensor,
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    seq_starts: torch.Tensor | None = None,
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) -> None:
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    torch.ops._C_cache_ops.gather_and_maybe_dequant_cache(
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        src_cache,
        dst,
        block_table,
        cu_seq_lens,
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        token_to_seq,
        num_tokens,
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        kv_cache_dtype,
        scale,
        seq_starts,
    )
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def cp_gather_cache(
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
    batch_size: int,
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    seq_starts: torch.Tensor | None = None,
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) -> None:
    torch.ops._C_cache_ops.cp_gather_cache(
        src_cache, dst, block_table, cu_seq_lens, batch_size, seq_starts
    )
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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 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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def indexer_k_cache(k: torch.Tensor, kv_cache: torch.Tensor,
                    slot_mapping: torch.Tensor,
                    kv_cache_dtype: str) -> None:
    torch.ops._C_cache_ops.indexer_k_cache(
        k, kv_cache, slot_mapping, kv_cache_dtype
    )
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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
    )
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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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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(
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        device
    )
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# custom ar
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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
    )
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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)
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def dispose(fa: int) -> None:
    torch.ops._C_custom_ar.dispose(fa)


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


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def register_buffer(fa: int, ipc_tensors: list[int]) -> None:
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    return torch.ops._C_custom_ar.register_buffer(fa, ipc_tensors)
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def get_graph_buffer_ipc_meta(fa: int) -> tuple[list[int], list[int]]:
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    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


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

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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)


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def read_cache(
        keys: torch.Tensor,
        values: torch.Tensor,
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        key_caches: list[torch.Tensor],
        value_caches: list[torch.Tensor],
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        slot_mapping: torch.Tensor,
        kv_cache_dtype: str
) -> None:
    torch.ops._C_cache_ops.read_cache(keys, values, key_caches,
                                      value_caches, slot_mapping,
                                      kv_cache_dtype)

def write_cache_multi_layers(
        keys: torch.Tensor,
        values: torch.Tensor,
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        key_caches: list[torch.Tensor],
        value_caches: list[torch.Tensor],
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        slot_mapping: torch.Tensor,
        kv_cache_dtype: str
) -> None:
    torch.ops._C_cache_ops.write_cache_multi_layers(keys, values, key_caches,
                                                    value_caches, slot_mapping,
                                                    kv_cache_dtype)
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# quick all reduce
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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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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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def get_flash_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_heads_per_head_k: int,
    num_heads_k: int,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    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.
    """
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    return torch.ops._C.get_flash_mla_metadata(
        cache_seqlens, num_heads_per_head_k, num_heads_k
    )
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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,
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    softmax_scale: float | None = None,
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    causal: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    """
    Arguments:
        q: (batch_size, seq_len_q, num_heads_q, head_dim).
        k_cache: (num_blocks, page_block_size, num_heads_k, head_dim).
        block_table: (batch_size, max_num_blocks_per_seq), torch.int32.
        cache_seqlens: (batch_size), torch.int32.
        head_dim_v: Head_dim of v.
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, return by get_mla_metadata.
        num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata.
        softmax_scale: float. The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim).
        causal: bool. Whether to apply causal attention mask.

    Return:
        out: (batch_size, seq_len_q, num_heads_q, head_dim_v).
        softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32.
    """
    if softmax_scale is None:
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        softmax_scale = q.shape[-1] ** (-0.5)
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    out, softmax_lse = torch.ops._C.flash_mla_fwd_kvcache(
        q,
        k_cache,
        None,
        head_dim_v,
        cache_seqlens,
        block_table,
        softmax_scale,
        causal,
        tile_scheduler_metadata,
        num_splits,
    )
    return out, softmax_lse
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def moe_fused_gate(
    input_tensor,
    bias,
    num_expert_group,
    topk_group,
    topk,
    n_share_experts_fusion=0,
    routed_scaling_factor=0,
):
    # This fused kernel function is used to select topk expert in a hierarchical 2-layer fashion
    # it split group of expert into num_expert_group, and use top2 expert weight sum in each group
    # as the group weight to select exerpt groups and then select topk experts within the selected groups
    # the #experts is decided by the input tensor shape and we currently only support power of 2 #experts
    # and #experts should be divisible by num_expert_group. #expert/num_expert_group <= 32 is limitted for now.
    # for non-supported case, we suggestion to use the biased_grouped_topk func in sglang.srt.layers.moe.topk
    # n_share_experts_fusion: if > 0, the last expert will be replaced with a round-robin shared expert
    # routed_scaling_factor: if > 0, the last expert will be scaled by this factor
    return torch.ops._moe_C.moe_fused_gate(
        input_tensor,
        bias,
        num_expert_group,
        topk_group,
        topk,
        n_share_experts_fusion,
        routed_scaling_factor,
    )

if hasattr(torch.ops._moe_C, "moe_fused_gate"):

    @register_fake("_moe_C::moe_fused_gate")
    def moe_fused_gate_fake(
        input_tensor: torch.Tensor,
        bias: torch.Tensor,
        num_expert_group: int,
        topk_group: int,
        topk: int,
        n_share_experts_fusion: int,
        routed_scaling_factor: int,
    ):
        return torch.empty((input_tensor.size(0), topk),
                           dtype=input_tensor.dtype,
                           device=input_tensor.device), \
                    torch.empty((input_tensor.size(0), topk),
                           dtype=input_tensor.dtype,
                           device=input_tensor.device)
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def sm100_cutlass_mla_decode(
    out: torch.Tensor,
    lse: torch.Tensor,
    q_nope: torch.Tensor,
    q_pe: torch.Tensor,
    kv_c_and_k_pe_cache: torch.Tensor,
    seq_lens: torch.Tensor,
    page_table: torch.Tensor,
    workspace: torch.Tensor,
    scale: float,
    num_kv_splits: int,
) -> torch.Tensor:
    torch.ops._C.sm100_cutlass_mla_decode(
        out,
        lse,
        q_nope,
        q_pe,
        kv_c_and_k_pe_cache,
        seq_lens,
        page_table,
        workspace,
        scale,
        num_kv_splits,
    )
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    return out


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

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

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


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

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

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


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


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


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


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

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

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

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


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


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


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

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


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


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

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


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


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

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


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


def ceil_div(a, b):
    return (a + b - 1) // b


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
    n_row_blocks = ceil_div(rows, 128)
    n_col_blocks = ceil_div(cols, 4)
    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
    n_row_blocks = ceil_div(rows, 128)
    n_col_blocks = ceil_div(cols, 4)
    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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direct_register_custom_op(
    op_name="awq_gemm",
    op_func=awq_gemm,
    mutates_args=[],
    fake_impl=awq_gemm_fake,
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)
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direct_register_custom_op(
    op_name="gptq_gemm_",
    op_func=gptq_gemm_,
    mutates_args=[],
    fake_impl=gptq_gemm_fake_,
)