_custom_ops.py 70.6 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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import contextlib
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import importlib
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from typing import TYPE_CHECKING, Optional, Union
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import torch
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import torch.library
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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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logger = init_logger(__name__)

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if not current_platform.is_tpu() and not current_platform.is_hpu():
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    try:
        import vllm._C
    except ImportError as e:
        logger.warning("Failed to import from vllm._C with %r", e)
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supports_moe_ops = False
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with contextlib.suppress(ImportError):
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    import vllm._moe_C  # noqa: F401
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    supports_moe_ops = True
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if TYPE_CHECKING:
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    def register_fake(fn):
        return lambda name: fn
else:
    try:
        from torch.library import register_fake
    except ImportError:
        from torch.library import impl_abstract as register_fake

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# page attention ops
def paged_attention_v1(
    out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
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    seq_lens: torch.Tensor,
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    block_size: int,
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    max_seq_len: int,
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    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
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    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
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) -> None:
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    torch.ops._C.paged_attention_v1(
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        out, query, key_cache, value_cache, num_kv_heads, scale, block_tables,
        seq_lens, block_size, max_seq_len, alibi_slopes, kv_cache_dtype,
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        k_scale, v_scale, tp_rank, blocksparse_local_blocks,
        blocksparse_vert_stride, blocksparse_block_size,
        blocksparse_head_sliding_step)
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def paged_attention_v2(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
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    seq_lens: torch.Tensor,
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    block_size: int,
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    max_seq_len: int,
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    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
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    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
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) -> None:
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    torch.ops._C.paged_attention_v2(
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        out, exp_sum, max_logits, tmp_out, query, key_cache, value_cache,
        num_kv_heads, scale, block_tables, seq_lens, block_size, max_seq_len,
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        alibi_slopes, kv_cache_dtype, k_scale, v_scale, tp_rank,
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        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: Optional[torch.Tensor],
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    block_size: int,
    max_seq_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
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    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
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    fp8_out_scale: Optional[torch.Tensor] = None,
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) -> None:
    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,
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                                      query_start_loc, block_size, max_seq_len,
                                      alibi_slopes, kv_cache_dtype, k_scale,
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                                      v_scale, fp8_out_scale)
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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:
    torch.ops._C_cpu.mla_decode_kvcache(out, query, kv_cache, scale,
                                        block_tables, seq_lens)


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# merge attn states ops
def merge_attn_states(output: torch.Tensor,
                      prefix_output: torch.Tensor,
                      prefix_lse: torch.Tensor,
                      suffix_output: torch.Tensor,
                      suffix_lse: torch.Tensor,
                      output_lse: Optional[torch.Tensor] = None) -> 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

    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)

    torch.ops._C.convert_vertical_slash_indexes(
        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)
    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

    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)

    torch.ops._C.convert_vertical_slash_indexes_mergehead(
        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)
    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: Optional[torch.Tensor],
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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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def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
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                             key: Optional[torch.Tensor], head_size: int,
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                             cos_sin_cache: torch.Tensor, is_neox: bool,
                             rot_dim: int,
                             cos_sin_cache_offsets: torch.Tensor) -> None:
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    torch.ops._C.batched_rotary_embedding(positions, query, key, head_size,
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                                          cos_sin_cache, is_neox, rot_dim,
                                          cos_sin_cache_offsets)
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# layer norm ops
def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
             epsilon: float) -> None:
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    # TODO: Remove this contiguous call when the kernel is updated to support non-contiguous input
    input_contiguous = input.contiguous()
    torch.ops._C.rms_norm(out, input_contiguous, 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 advance_step_flashattn(num_seqs: int, num_queries: int, block_size: int,
                           input_tokens: torch.Tensor,
                           sampled_token_ids: torch.Tensor,
                           input_positions: torch.Tensor,
                           seq_lens: torch.Tensor, slot_mapping: torch.Tensor,
                           block_tables: torch.Tensor) -> None:
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    """Advance a step on GPU for existing inputs for a multi-step runner"""
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    return torch.ops._C.advance_step_flashattn(num_seqs, num_queries,
                                               block_size, input_tokens,
                                               sampled_token_ids,
                                               input_positions, seq_lens,
                                               slot_mapping, block_tables)


def advance_step_flashinfer(num_seqs: int, num_queries: int, block_size: int,
                            input_tokens: torch.Tensor,
                            sampled_token_ids: torch.Tensor,
                            input_positions: torch.Tensor,
                            seq_lens: torch.Tensor, slot_mapping: torch.Tensor,
                            block_tables: torch.Tensor,
                            paged_kv_indices: torch.Tensor,
                            paged_kv_indptr: torch.Tensor,
                            paged_kv_last_page_len: torch.Tensor,
                            block_table_bound: torch.Tensor) -> None:

    return torch.ops._C.advance_step_flashinfer(
        num_seqs, num_queries, block_size, input_tokens, sampled_token_ids,
        input_positions, seq_lens, slot_mapping, block_tables,
        paged_kv_indices, paged_kv_indptr, paged_kv_last_page_len,
        block_table_bound)
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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,
    scale_ub: Optional[torch.Tensor] = None,
    residual: Optional[torch.Tensor] = None
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) -> tuple[torch.Tensor, torch.Tensor]:
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    output = torch.empty_like(input, dtype=quant_dtype)
    scales = torch.empty((input.numel() // input.shape[-1], 1),
                         device=input.device,
                         dtype=torch.float32)

    torch.ops._C.rms_norm_dynamic_per_token_quant(output, input, weight,
                                                  scales, epsilon, scale_ub,
                                                  residual)
    return output, scales


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# quantization ops
# awq
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 (
            awq_dequantize_triton)
        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, qzeros: torch.Tensor,
             scales: torch.Tensor, split_k_iters: int) -> torch.Tensor:
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    if envs.VLLM_USE_TRITON_AWQ:
        from vllm.model_executor.layers.quantization.awq_triton import (
            awq_gemm_triton)
        return awq_gemm_triton(input, qweight, qzeros, scales, split_k_iters)
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    return torch.ops._C.awq_gemm(input, qweight, qzeros, scales, split_k_iters)
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# gptq
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 torch.ops._C.gptq_gemm(a, b_q_weight, b_gptq_qzeros, b_gptq_scales,
                                  b_g_idx, use_exllama, bit)
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if hasattr(torch.ops._C, "gptq_gemm"):
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    @register_fake("_C::gptq_gemm")
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    def _gptq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                        b_gptq_qzeros: torch.Tensor,
                        b_gptq_scales: torch.Tensor, b_g_idx: torch.Tensor,
                        use_exllama: bool, bit: int) -> torch.Tensor:
        return torch.empty((a.size(0), b_q_weight.size(1)),
                           dtype=a.dtype,
                           device=a.device)


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def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor,
                 bit: int) -> None:
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    torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
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# marlin
def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                b_scales: torch.Tensor, workspace: torch.Tensor, size_m: int,
                size_n: int, size_k: int) -> torch.Tensor:
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    return torch.ops._C.marlin_gemm(a, b_q_weight, b_scales, workspace, size_m,
                                    size_n, size_k)
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# marlin_24
def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                        b_meta: torch.Tensor, b_scales: torch.Tensor,
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                        workspace: torch.Tensor, b_q_type: ScalarType,
                        size_m: int, size_n: int, size_k: int) -> torch.Tensor:
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    return torch.ops._C.gptq_marlin_24_gemm(a, b_q_weight, b_meta, b_scales,
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                                            workspace, b_q_type.id, size_m,
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                                            size_n, size_k)
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if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
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    @register_fake("_C::gptq_marlin_24_gemm")
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    def _gptq_marlin_24_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                                  b_meta: torch.Tensor, b_scales: torch.Tensor,
                                  workspace: torch.Tensor,
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                                  b_q_type: ScalarType, size_m: torch.SymInt,
                                  size_n: torch.SymInt,
                                  size_k: torch.SymInt) -> torch.Tensor:
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        return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)

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    @register_fake("_C::gptq_marlin_gemm")
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    def _gptq_marlin_gemm_fake(a: torch.Tensor,
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                               c: Optional[torch.Tensor],
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                               b_q_weight: torch.Tensor,
                               b_scales: torch.Tensor,
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                               global_scale: Optional[torch.Tensor],
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                               b_zeros: Optional[torch.Tensor],
                               g_idx: Optional[torch.Tensor],
                               perm: Optional[torch.Tensor],
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                               workspace: torch.Tensor,
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                               b_q_type_id: int,
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                               size_m: torch.SymInt,
                               size_n: torch.SymInt,
                               size_k: torch.SymInt,
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                               is_k_full: bool = True,
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                               use_atomic_add: bool = False,
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                               use_fp32_reduce: bool = False,
                               is_zp_float: bool = False) -> torch.Tensor:
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        return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)

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    @register_fake("_C::marlin_qqq_gemm")
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    def _marlin_qqq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                              s_tok: torch.Tensor, s_ch: torch.Tensor,
                              s_group: torch.Tensor, workspace: torch.Tensor,
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                              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),
                           dtype=torch.float16,
                           device=a.device)

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    @register_fake("_C::marlin_gemm")
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    def _marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                          b_scales: torch.Tensor, workspace: torch.Tensor,
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                          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),
                           dtype=torch.float16,
                           device=a.device)

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

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    @register_fake("_C::aqlm_gemm")
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    def _aqlm_gemm_fake(input: torch.Tensor, codes: torch.Tensor,
                        codebooks: torch.Tensor, scales: torch.Tensor,
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                        codebook_partition_sizes: list[int],
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                        bias: Optional[torch.Tensor]) -> torch.Tensor:
        out_features = codes.size(0) * codebooks.size(2)
        flat_input = input.reshape((-1, input.size(-1)))
        flat_output = torch.empty((flat_input.size(0), out_features),
                                  dtype=input.dtype,
                                  device=input.device)

        output_sizes = list(input.shape)
        output_sizes.pop()
        output_sizes.append(-1)
        return flat_output.reshape(tuple(output_sizes))

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    @register_fake("_C::aqlm_dequant")
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    def _aqlm_dequant_fake(
            codes: torch.Tensor, codebooks: torch.Tensor,
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            codebook_partition_sizes: list[int]) -> torch.Tensor:
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        in_features = codes.size(1) * 8
        out_features = codes.size(0)
        return torch.empty((out_features, in_features),
                           dtype=codebooks.dtype,
                           device=codebooks.device)

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    @register_fake("_C::machete_mm")
    def machete_mm_fake(
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        a: torch.Tensor,
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        # b_q Should be the tensor returned by machete_prepack_B
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        b_q: torch.Tensor,
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        b_type: ScalarType,
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        out_type: Optional[torch.dtype] = None,
        b_group_scales: Optional[torch.Tensor] = None,
        b_group_zeros: Optional[torch.Tensor] = None,
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        b_group_size: Optional[int] = None,
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        b_channel_scales: Optional[torch.Tensor] = None,
        a_token_scales: Optional[torch.Tensor] = None,
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        schedule: Optional[str] = None,
    ) -> 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")
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    def machete_prepack_B_fake(
            b_q_weight: torch.Tensor, a_type: torch.dtype, b_type: ScalarType,
            group_scales_type: Optional[torch.dtype]) -> torch.Tensor:
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        return torch.empty_like(b_q_weight,
                                memory_format=torch.contiguous_format)
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if hasattr(torch.ops._C, "allspark_w8a16_gemm"):

    @register_fake("_C::allspark_w8a16_gemm")
    def _allspark_w8a16_gemm_fake(a: torch.Tensor, b_qweight: torch.Tensor,
                                  b_scales: torch.Tensor,
                                  b_qzeros: Optional[torch.Tensor],
                                  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:
        m = a.size(0)
        return torch.empty((m, n), device=a.device, dtype=a.dtype)


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

    @register_fake("_C::ggml_dequantize")
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    def _ggml_dequantize_fake(
            W: torch.Tensor,
            quant_type: int,
            m: torch.SymInt,
            n: torch.SymInt,
            dtype: Optional[torch.dtype] = None) -> 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((1, 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)
        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)
        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:
    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)
    torch.ops._C.cutlass_scaled_fp4_mm(out, a, b, block_scale_a, block_scale_b,
                                       alpha)
    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:
    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,
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                      scale_b: torch.Tensor,
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                      out_dtype: torch.dtype,
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                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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    """
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    `cutlass_scaled_mm` implements a fused version of
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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)
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    assert bias is None or bias.shape[0] == b.shape[
        1] and bias.dtype == out_dtype
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    m = a.shape[0]
    n = b.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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        triton_scaled_mm_module = importlib.import_module(
            "vllm.model_executor.layers.quantization.compressed_tensors."
            "triton_scaled_mm")
        triton_scaled_mm = triton_scaled_mm_module.triton_scaled_mm
        return triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)

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    out = torch.empty((m, n), dtype=out_dtype, device=a.device)

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    torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)

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    return out


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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,
                          azp: Optional[torch.Tensor] = None,
                          bias: Optional[torch.Tensor] = None) -> 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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    assert azp is None or azp.numel() == a.shape[0]
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    m = a.shape[0]
    n = b.shape[1]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)

    torch.ops._C.cutlass_scaled_mm_azp(out, a, b, scale_a, scale_b, azp_adj,
                                       azp, bias)
    return out


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


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

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def cutlass_sparse_compress(a: torch.Tensor) \
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    -> 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:
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        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:
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        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)`.
    """
    assert (a.dtype in [
        torch.int8, torch.float8_e4m3fn, torch.bfloat16, torch.float16
    ])
    assert (a.is_contiguous())

    # a_meta.dtype: torch.uint8 so elemsPerMetaElem = 8b / 2b_per_nz = 4
    elemsPerMetaElem = 4
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    assert (a.shape[1] % (2 * elemsPerMetaElem) == 0)
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    return torch.ops._C.cutlass_sparse_compress(a)
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def cutlass_scaled_sparse_mm(
        a: torch.Tensor,
        bt_nzs: torch.Tensor,
        bt_meta: torch.Tensor,
        scale_a: torch.Tensor,
        scale_b: torch.Tensor,
        out_dtype: torch.dtype,
        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    """
    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.
    """
    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

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

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

    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):
    """
    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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    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)
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def cutlass_moe_mm(out_tensors: torch.Tensor, a_tensors: torch.Tensor,
                   b_tensors: torch.Tensor, a_scales: torch.Tensor,
                   b_scales: torch.Tensor, expert_offsets: torch.Tensor,
                   problem_sizes: torch.Tensor, a_strides: torch.Tensor,
                   b_strides: torch.Tensor, c_strides: torch.Tensor):
    """
    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)


def cutlass_fp4_moe_mm(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,
                       out_dtype: torch.dtype, device: torch.device):
    """
    An FP4 Blockscaled Group Gemm that takes in  a_tensors, b_tensors and runs 
    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
    - 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 
                    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.
    """
    m_topk = a_tensors.shape[0]
    n = b_tensors.shape[1]
    c_shape = (m_topk, n)
    c = torch.empty(c_shape, device=device, dtype=out_dtype)
    torch.ops._C.cutlass_fp4_group_mm(c, a_tensors, b_tensors, a_scales,
                                      b_scales, alphas, problem_sizes,
                                      expert_offsets, sf_offsets)
    return c.to(out_dtype)
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# aqlm
def aqlm_gemm(input: torch.Tensor, codes: torch.Tensor,
              codebooks: torch.Tensor, scales: torch.Tensor,
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              codebook_partition_sizes: list[int],
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              bias: Optional[torch.Tensor]) -> torch.Tensor:
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    return torch.ops._C.aqlm_gemm(input, codes, codebooks, scales,
                                  codebook_partition_sizes, bias)
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def aqlm_dequant(codes: torch.Tensor, codebooks: torch.Tensor,
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                 codebook_partition_sizes: list[int]) -> torch.Tensor:
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    return torch.ops._C.aqlm_dequant(codes, codebooks,
                                     codebook_partition_sizes)
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# gptq_marlin
def gptq_marlin_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
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                       size_k: int, size_n: int,
                       num_bits: int) -> torch.Tensor:
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    return torch.ops._C.gptq_marlin_repack(b_q_weight, perm, size_k, size_n,
                                           num_bits)
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# gptq_marlin
def awq_marlin_repack(b_q_weight: torch.Tensor, size_k: int, size_n: int,
                      num_bits: int) -> torch.Tensor:
    return torch.ops._C.awq_marlin_repack(b_q_weight, size_k, size_n, num_bits)


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def gptq_marlin_moe_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
                           size_k: int, size_n: int,
                           num_bits: int) -> torch.Tensor:
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
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    output = torch.empty((num_experts, size_k // 16, size_n * (num_bits // 2)),
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                         device=b_q_weight.device,
                         dtype=b_q_weight.dtype)
    for e in range(num_experts):
        output[e] = torch.ops._C.gptq_marlin_repack(b_q_weight[e], perm[e],
                                                    size_k, size_n, num_bits)
    return output


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def awq_marlin_moe_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
                          size_k: int, size_n: int,
                          num_bits: int) -> torch.Tensor:
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
    output = torch.empty((num_experts, size_k // 16, size_n * (num_bits // 2)),
                         device=b_q_weight.device,
                         dtype=b_q_weight.dtype)
    for e in range(num_experts):
        output[e] = torch.ops._C.awq_marlin_repack(b_q_weight[e], size_k,
                                                   size_n, num_bits)
    return output


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def gptq_marlin_gemm(a: torch.Tensor,
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                     c: Optional[torch.Tensor],
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                     b_q_weight: torch.Tensor,
                     b_scales: torch.Tensor,
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                     global_scale: Optional[torch.Tensor],
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                     b_zeros: Optional[torch.Tensor],
                     g_idx: Optional[torch.Tensor],
                     perm: Optional[torch.Tensor],
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                     workspace: torch.Tensor,
                     b_q_type: ScalarType,
                     size_m: int,
                     size_n: int,
                     size_k: int,
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                     is_k_full: bool = True,
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                     use_atomic_add: bool = False,
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                     use_fp32_reduce: bool = False,
                     is_zp_float: bool = False) -> torch.Tensor:
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    return torch.ops._C.gptq_marlin_gemm(a, c, b_q_weight, b_scales,
                                         global_scale, b_zeros, g_idx, perm,
                                         workspace, b_q_type.id, size_m,
                                         size_n, size_k, is_k_full,
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                                         use_atomic_add, use_fp32_reduce,
                                         is_zp_float)
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974
# machete
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def machete_supported_schedules(
        a_type: torch.dtype,
        b_type: ScalarType,
        group_scales_type: Optional[torch.dtype],
        group_zeros_type: Optional[torch.dtype] = None,
        channel_scales_type: Optional[torch.dtype] = None,
        token_scales_type: Optional[torch.dtype] = None,
982
        out_type: Optional[torch.dtype] = None) -> list[str]:
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    return torch.ops._C.machete_supported_schedules(
        a_type, b_type.id, group_scales_type, group_zeros_type,
        channel_scales_type, token_scales_type, out_type)


def machete_mm(
        a: torch.Tensor,
        # b_q Should be the tensor returned by machete_prepack_B
        b_q: torch.Tensor,
        b_type: ScalarType,
        out_type: Optional[torch.dtype] = None,
        b_group_scales: Optional[torch.Tensor] = None,
        b_group_zeros: Optional[torch.Tensor] = None,
        b_group_size: Optional[int] = None,
        b_channel_scales: Optional[torch.Tensor] = None,
        a_token_scales: Optional[torch.Tensor] = None,
        schedule: Optional[str] = None) -> 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)


def machete_prepack_B(
        b_q_weight: torch.Tensor, a_type: torch.dtype, b_type: ScalarType,
        group_scales_type: Optional[torch.dtype]) -> torch.Tensor:
    return torch.ops._C.machete_prepack_B(b_q_weight, a_type, b_type.id,
                                          group_scales_type)
1010
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1012
if hasattr(torch.ops._C, "permute_cols"):
1013

1014
    @register_fake("_C::permute_cols")
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    def _permute_cols_fake(a: torch.Tensor,
                           perm: torch.Tensor) -> torch.Tensor:
        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(
        input: torch.Tensor,
1027
        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
1043
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            two values are packed into a uint8 and float8_e4m3 scaling factors
            in the sizzled layout.
    """
1046
    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}.')
    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

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

    # 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)
    output_scale = torch.empty((rounded_m, rounded_n // 4),
                               device=device,
                               dtype=torch.int32)

    torch.ops._C.scaled_fp4_quant(output, input, output_scale,
                                  input_global_scale)
    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,
    expert_map: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Quantize input tensor to FP4 and return quantized tensor and scale, for
    packed MoE Inputs.
    Args:
        input: The input tensor to be quantized to FP4
        expert_map: The expert map tensor
        input_global_scale: A scalar scaling factor for the entire tensor.
        expert_offsets: The expert offsets tensor
        blockscale_offsets: The blockscale offsets tensor
    Outputs:
        output: The quantized tensor in FP4
        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}.')

    input_tensor = input_tensor[
        expert_map] if expert_map is not None else input_tensor
    m_numtopk, k = input_tensor.shape
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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
1115
    assert (m_numtopk <= MAX_TOKENS_PER_EXPERT * topk), (
1116
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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"
        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
    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)
    output_scales = output_scales.view(torch.float8_e4m3fn)
    return output, output_scales


1139
# fp8
1140
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1142
def scaled_fp8_quant(
    input: torch.Tensor,
    scale: Optional[torch.Tensor] = None,
1143
    num_token_padding: Optional[int] = None,
1144
    scale_ub: Optional[torch.Tensor] = None,
1145
    use_per_token_if_dynamic: bool = False,
1146
) -> 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
1153
    optional padding of the output tensors for downstream kernels that
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    will benefit from padding.

    Args:
        input: The input tensor to be quantized to FP8
        scale: Optional scaling factor for the FP8 quantization
1159
        scale_ub: Optional upper bound for scaling factor in dynamic
1160
            per token case
1161
        num_token_padding: If specified, pad the first dimension
1162
            of the output to at least this value.
1163
        use_per_token_if_dynamic: Whether to do per_tensor or per_token
1164
            in the dynamic quantization case.
1165
1166

    Returns:
1167
        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
    assert (input.ndim == 2)
1172
    shape: Union[tuple[int, int], torch.Size] = input.shape
1173
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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])
1177
    output = torch.empty(shape, device=input.device, dtype=out_dtype)
1178

1179
    if scale is None:
1180
        if use_per_token_if_dynamic:
1181
            scale = torch.empty((shape[0], 1),
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                                device=input.device,
                                dtype=torch.float32)
            torch.ops._C.dynamic_per_token_scaled_fp8_quant(
1185
                output, input, scale, scale_ub)
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1188
        else:
            scale = torch.zeros(1, device=input.device, dtype=torch.float32)
            torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
1189
    else:
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        # num_token_padding not implemented for this case
        assert (scale.numel() == 1 or num_token_padding is None)
1192
        torch.ops._C.static_scaled_fp8_quant(output, input, scale)
1193

1194
    return output, scale
1195
1196


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1202
# gptq allspark
def allspark_repack_weight(
        qweight: torch.Tensor,
        scale: torch.Tensor,
        zero_point: Optional[torch.Tensor] = None,
        has_zp: bool = False
1203
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
1204
    """
1205
    Rearrange qweight, scale, and zero_point(if asymmetric) to n32k16 format
1206
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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.
1214
1215
            if use asymmetric quantization, has_zp = True.

1216
    Returns:
1217
        tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] :
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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

    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)
    zero_point_reorder = None
    if has_zp:
        assert zero_point is not None, (
            "zero_point must be provided for asymmetric quantization.")
        zero_point_reorder = torch.empty((1, N_32align),
                                         device=zero_point.device,
                                         dtype=zero_point.dtype)

    torch.ops._C.rearrange_kn_weight_as_n32k16_order(
        qweight, scale, zero_point, has_zp, qweight_reorder, scale_reorder,
        zero_point_reorder, K, N, N_32align)

    return qweight_reorder, scale_reorder, zero_point_reorder


def allspark_w8a16_gemm(a: torch.Tensor, b_qweight: torch.Tensor,
                        b_scales: torch.Tensor,
                        b_qzeros: Optional[torch.Tensor], 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)


1258
# int8
1259
def scaled_int8_quant(
1260
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1263
    input: torch.Tensor,
    scale: Optional[torch.Tensor] = None,
    azp: Optional[torch.Tensor] = None,
    symmetric: bool = True
1264
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
1265
    """
1266
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.
1267
1268
1269

    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.
1272
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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).
1275
1276

    Returns:
1277
      tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] : Output int8 tensor, scales, and optionally azp.
1278
    """
1279
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1281
    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
1282
        assert symmetric == (
1283
1284
            azp
            is None), "azp must only be provided for asymmetric quantization."
1285
        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
1286
        return output, scale, azp
1287
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1289
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1291

    # dynamic-per-token quantization.
    input_scales = torch.empty((input.numel() // input.shape[-1], 1),
                               device=input.device,
                               dtype=torch.float32)
1292
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1296
    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)
    return output, input_scales, input_azp
1297
1298


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1307
# qqq ops
def marlin_qqq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                    s_tok: torch.Tensor, s_ch: torch.Tensor,
                    s_group: torch.Tensor, workspace: torch.Tensor,
                    size_m: int, size_n: int, size_k: int) -> torch.Tensor:
    return torch.ops._C.marlin_qqq_gemm(a, b_q_weight, s_tok, s_ch, s_group,
                                        workspace, size_m, size_n, size_k)


1308
# gguf
1309
1310
1311
def ggml_dequantize(W: torch.Tensor, quant_type: int, m: int, n: int,
                    dtype: Optional[torch.dtype]) -> torch.Tensor:
    return torch.ops._C.ggml_dequantize(W, quant_type, m, n, dtype)
1312
1313
1314
1315
1316
1317
1318


def ggml_mul_mat_vec_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
1319
) -> torch.Tensor:
1320
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1322
1323
1324
1325
1326
1327
    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,
1328
) -> torch.Tensor:
1329
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1331
    return torch.ops._C.ggml_mul_mat_a8(W, X, quant_type, row)


1332
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1343
1344
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1347
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:
    return torch.ops._C.ggml_moe_a8(X, W, sorted_token_ids, expert_ids,
                                    num_tokens_post_padded, quant_type, row,
                                    top_k, tokens)


1348
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1360
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:
    return torch.ops._C.ggml_moe_a8_vec(X, W, topk_ids, top_k, quant_type, row,
                                        tokens)


1361
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1363
1364
def ggml_moe_get_block_size(quant_type: int) -> int:
    return torch.ops._C.ggml_moe_get_block_size(quant_type)


1365
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1367
# mamba
def causal_conv1d_fwd(x: torch.Tensor, weight: torch.Tensor,
                      bias_: Optional[torch.Tensor],
1368
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1370
1371
                      conv_states: Optional[torch.Tensor],
                      query_start_loc: Optional[torch.Tensor],
                      cache_indices: Optional[torch.Tensor],
                      has_initial_state: Optional[torch.Tensor],
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                      silu_activation: bool, pad_slot_id: int):
    torch.ops._C.causal_conv1d_fwd(x, weight, bias_, conv_states,
                                   query_start_loc, cache_indices,
                                   has_initial_state, silu_activation,
                                   pad_slot_id)


def causal_conv1d_update(x: torch.Tensor, conv_state: torch.Tensor,
                         weight: torch.Tensor, bias_: Optional[torch.Tensor],
                         silu_activation: bool,
                         cache_seqlens: Optional[torch.Tensor],
                         conv_state_indices: Optional[torch.Tensor],
                         pad_slot_id: int):
    torch.ops._C.causal_conv1d_update(x, conv_state, weight, bias_,
                                      silu_activation, cache_seqlens,
                                      conv_state_indices, pad_slot_id)


def selective_scan_fwd(u: torch.Tensor, delta: torch.Tensor, A: torch.Tensor,
                       B: torch.Tensor, C: torch.Tensor,
                       D_: Optional[torch.Tensor], z_: Optional[torch.Tensor],
                       delta_bias_: Optional[torch.Tensor],
                       delta_softplus: bool,
                       query_start_loc: Optional[torch.Tensor],
                       cache_indices: Optional[torch.Tensor],
                       has_initial_state: Optional[torch.Tensor],
                       ssm_states: torch.Tensor, pad_slot_id: int):
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1401
    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,
1402
                                    ssm_states, pad_slot_id)
1403
1404


1405
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1414
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1418
1419
1420
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1424
# ROCm skinny gemms
def LLMM1(a: torch.Tensor, b: torch.Tensor,
          rows_per_block: int) -> torch.Tensor:
    return torch.ops._rocm_C.LLMM1(a, b, rows_per_block)


def wvSplitK(a: torch.Tensor, b: torch.Tensor, cu_count: int) -> torch.Tensor:
    return torch.ops._rocm_C.wvSplitK(a, b, cu_count)


def wvSplitKQ(a: torch.Tensor, b: torch.Tensor, out_dtype: torch.dtype,
              scale_a: torch.Tensor, scale_b: torch.Tensor,
              cu_count: int) -> torch.Tensor:
    out = torch.empty((b.shape[0], a.shape[0]),
                      dtype=out_dtype,
                      device=b.device)
    torch.ops._rocm_C.wvSplitKQ(a, b, out, scale_a, scale_b, cu_count)
    return out


1425
# moe
1426
1427
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1429
def moe_sum(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum(input, output)


1430
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1433
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) -> None:
1434
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    torch.ops._moe_C.moe_align_block_size(topk_ids, num_experts, block_size,
                                          sorted_token_ids, experts_ids,
                                          num_tokens_post_pad)
1437
1438


1439
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def sgl_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) -> None:
    torch.ops._moe_C.sgl_moe_align_block_size(topk_ids, num_experts,
                                              block_size, sorted_token_ids,
                                              experts_ids, num_tokens_post_pad)


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def moe_wna16_gemm(input: torch.Tensor, output: torch.Tensor,
                   b_qweight: torch.Tensor, b_scales: torch.Tensor,
                   b_qzeros: Optional[torch.Tensor],
                   topk_weights: Optional[torch.Tensor],
                   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(
            "The optimized moe_wna16_gemm kernel is only "
            "available on CUDA platforms")
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    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_indicies: torch.Tensor,
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                 gating_output: torch.Tensor) -> None:
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    torch.ops._moe_C.topk_softmax(topk_weights, topk_ids,
                                  token_expert_indicies, gating_output)
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def moe_wna16_marlin_gemm(input: torch.Tensor, output: Optional[torch.Tensor],
                          b_qweight: torch.Tensor, b_scales: torch.Tensor,
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                          global_scale: Optional[torch.Tensor],
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                          b_qzeros: Optional[torch.Tensor],
                          g_idx: Optional[torch.Tensor],
                          perm: Optional[torch.Tensor],
                          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, is_ep: 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) -> torch.Tensor:
    return torch.ops._moe_C.moe_wna16_marlin_gemm(
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        input, output, b_qweight, b_scales, 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, is_ep,
        b_q_type.id, size_m, size_n, size_k, is_k_full, use_atomic_add,
        use_fp32_reduce, is_zp_float)
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if supports_moe_ops 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,
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                             b_zero_points: torch.Tensor, g_idx: torch.Tensor,
                             perm: torch.Tensor, workspace: torch.Tensor,
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                             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,
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                             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")
    def moe_wna16_marlin_gemm_fake(input: torch.Tensor,
                                   output: Optional[torch.Tensor],
                                   b_qweight: torch.Tensor,
                                   b_scales: torch.Tensor,
                                   b_qzeros: Optional[torch.Tensor],
                                   g_idx: Optional[torch.Tensor],
                                   perm: Optional[torch.Tensor],
                                   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, is_ep: 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) -> torch.Tensor:
        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,
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                                             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,
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                                                   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:
    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 copy_blocks(key_caches: list[torch.Tensor],
                value_caches: list[torch.Tensor],
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                block_mapping: torch.Tensor) -> None:
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    torch.ops._C_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
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def copy_blocks_mla(kv_caches: list[torch.Tensor],
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                    block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.copy_blocks_mla(kv_caches, block_mapping)


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def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
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                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_cache(src_cache: torch.Tensor,
                 dst: torch.Tensor,
                 block_table: torch.Tensor,
                 cu_seq_lens: torch.Tensor,
                 batch_size: int,
                 seq_starts: Optional[torch.Tensor] = None) -> None:
    torch.ops._C_cache_ops.gather_cache(src_cache, dst, block_table,
                                        cu_seq_lens, batch_size, seq_starts)


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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(
        device)


# custom ar
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def init_custom_ar(ipc_tensors: list[torch.Tensor], rank_data: torch.Tensor,
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                   rank: int, fully_connected: bool) -> int:
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    return torch.ops._C_custom_ar.init_custom_ar(ipc_tensors, rank_data, rank,
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                                                 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 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.
    """
    return torch.ops._C.get_flash_mla_metadata(cache_seqlens,
                                               num_heads_per_head_k,
                                               num_heads_k)


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,
    softmax_scale: Optional[float] = None,
    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:
        softmax_scale = q.shape[-1]**(-0.5)
    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 cutlass_mla_decode(out: 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,
                       scale: float) -> torch.Tensor:
    torch.ops._C.cutlass_mla_decode(out, q_nope, q_pe, kv_c_and_k_pe_cache,
                                    seq_lens, page_table, scale)
    return out