_custom_ops.py 57.5 KB
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# SPDX-License-Identifier: Apache-2.0

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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,
    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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) -> 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,
                                      block_size, max_seq_len, alibi_slopes,
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                                      kv_cache_dtype, k_scale, v_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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# pos encoding ops
def rotary_embedding(
    positions: torch.Tensor,
    query: torch.Tensor,
    key: torch.Tensor,
    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,
                             key: torch.Tensor, head_size: int,
                             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,
                                          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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    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 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,
                               b_q_weight: torch.Tensor,
                               b_scales: torch.Tensor,
                               b_zeros: torch.Tensor,
                               g_idx: torch.Tensor,
                               perm: torch.Tensor,
                               workspace: torch.Tensor,
                               b_q_type: ScalarType,
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                               size_m: torch.SymInt,
                               size_n: torch.SymInt,
                               size_k: torch.SymInt,
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                               is_k_full: bool,
                               has_zp: bool = False,
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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::fp8_marlin_gemm")
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    def _fp8_marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                              b_scales: torch.Tensor, workspace: torch.Tensor,
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                              num_bits: int, 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=a.dtype, device=a.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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# 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 (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
    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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    if current_platform.is_rocm():
        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.
    """
    torch.ops._C.get_cutlass_moe_mm_data(topk_ids, expert_offsets,
                                         problem_sizes1, problem_sizes2,
                                         input_permutation, output_permutation,
                                         num_experts, n, k)


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.
    """
    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)


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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,
                     b_q_weight: torch.Tensor,
                     b_scales: torch.Tensor,
                     b_zeros: torch.Tensor,
                     g_idx: torch.Tensor,
                     perm: torch.Tensor,
                     workspace: torch.Tensor,
                     b_q_type: ScalarType,
                     size_m: int,
                     size_n: int,
                     size_k: int,
                     is_k_full: bool,
                     has_zp: bool = False,
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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, b_q_weight, b_scales, b_zeros,
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                                         g_idx, perm, workspace, b_q_type.id,
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                                         size_m, size_n, size_k, is_k_full,
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                                         has_zp, use_atomic_add,
                                         use_fp32_reduce, is_zp_float)
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# fp8 marlin
def fp8_marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                    b_scales: torch.Tensor, workspace: torch.Tensor,
                    num_bits: int, size_m: int, size_n: int,
                    size_k: int) -> torch.Tensor:
    return torch.ops._C.fp8_marlin_gemm(a, b_q_weight, b_scales, workspace,
                                        num_bits, size_m, size_n, size_k)


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# 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,
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        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)
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863
if hasattr(torch.ops._C, "permute_cols"):
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865
    @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,
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        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}.')
    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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# fp8
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def scaled_fp8_quant(
    input: torch.Tensor,
    scale: Optional[torch.Tensor] = None,
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    num_token_padding: Optional[int] = None,
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    scale_ub: Optional[torch.Tensor] = None,
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    use_per_token_if_dynamic: bool = False,
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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:
        input: The input tensor to be quantized to FP8
        scale: Optional scaling factor for the FP8 quantization
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        scale_ub: Optional upper bound for scaling factor in dynamic
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            per token case
955
        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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    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
    assert (input.ndim == 2)
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    shape: Union[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])
971
    output = torch.empty(shape, device=input.device, dtype=out_dtype)
972

973
    if scale is None:
974
        if use_per_token_if_dynamic:
975
            scale = torch.empty((shape[0], 1),
976
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978
                                device=input.device,
                                dtype=torch.float32)
            torch.ops._C.dynamic_per_token_scaled_fp8_quant(
979
                output, input, scale, scale_ub)
980
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        else:
            scale = torch.zeros(1, device=input.device, dtype=torch.float32)
            torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
983
    else:
984
985
        # num_token_padding not implemented for this case
        assert (scale.numel() == 1 or num_token_padding is None)
986
        torch.ops._C.static_scaled_fp8_quant(output, input, scale)
987

988
    return output, scale
989
990


991
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996
# gptq allspark
def allspark_repack_weight(
        qweight: torch.Tensor,
        scale: torch.Tensor,
        zero_point: Optional[torch.Tensor] = None,
        has_zp: bool = False
997
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
998
    """
999
    Rearrange qweight, scale, and zero_point(if asymmetric) to n32k16 format
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1001
1002
1003
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1007
    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.
1008
1009
            if use asymmetric quantization, has_zp = True.

1010
    Returns:
1011
        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)


1052
# int8
1053
def scaled_int8_quant(
1054
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1057
    input: torch.Tensor,
    scale: Optional[torch.Tensor] = None,
    azp: Optional[torch.Tensor] = None,
    symmetric: bool = True
1058
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
1059
    """
1060
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.
1061
1062
1063

    Args:
        input: The input tensor to be quantized to int8.
1064
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        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.
1066
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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).
1069
1070

    Returns:
1071
      tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] : Output int8 tensor, scales, and optionally azp.
1072
    """
1073
1074
1075
    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
1076
        assert symmetric == (
1077
1078
            azp
            is None), "azp must only be provided for asymmetric quantization."
1079
        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
1080
        return output, scale, azp
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1085

    # dynamic-per-token quantization.
    input_scales = torch.empty((input.numel() // input.shape[-1], 1),
                               device=input.device,
                               dtype=torch.float32)
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    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
1091
1092


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1100
1101
# 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)


1102
# gguf
1103
1104
1105
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)
1106
1107
1108
1109
1110
1111
1112


def ggml_mul_mat_vec_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
1113
) -> torch.Tensor:
1114
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1121
    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,
1122
) -> torch.Tensor:
1123
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1125
    return torch.ops._C.ggml_mul_mat_a8(W, X, quant_type, row)


1126
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1129
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1142
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1145
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)


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


1146
1147
1148
# mamba
def causal_conv1d_fwd(x: torch.Tensor, weight: torch.Tensor,
                      bias_: Optional[torch.Tensor],
1149
1150
1151
1152
                      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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1177
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1179
                      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):
1180
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1182
    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,
1183
                                    ssm_states, pad_slot_id)
1184
1185


1186
# moe
1187
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1190
def moe_sum(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum(input, output)


1191
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1194
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:
1195
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1197
    torch.ops._moe_C.moe_align_block_size(topk_ids, num_experts, block_size,
                                          sorted_token_ids, experts_ids,
                                          num_tokens_post_pad)
1198
1199


1200
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1204
1205
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1208
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)


1209
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1216
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:
1217
1218
1219
1220
    if not current_platform.is_cuda():
        raise NotImplementedError(
            "The optimized moe_wna16_gemm kernel is only "
            "available on CUDA platforms")
1221
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1224
1225
1226
1227
    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)


1228
1229
def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
                 token_expert_indicies: torch.Tensor,
1230
                 gating_output: torch.Tensor) -> None:
1231
1232
    torch.ops._moe_C.topk_softmax(topk_weights, topk_ids,
                                  token_expert_indicies, gating_output)
1233
1234


1235
1236
if supports_moe_ops and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):

1237
    @register_fake("_moe_C::marlin_gemm_moe")
1238
1239
1240
1241
    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,
1242
1243
                             b_zero_points: torch.Tensor, g_idx: torch.Tensor,
                             perm: torch.Tensor, workspace: torch.Tensor,
1244
1245
1246
1247
                             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,
1248
1249
1250
1251
1252
1253
                             apply_weights: bool) -> torch.Tensor:
        return torch.empty((size_m, topk, size_n),
                           dtype=a.dtype,
                           device=a.device)


1254
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1258
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1260
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,
1261
1262
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
1263
) -> None:
1264
1265
    torch.ops._C_cache_ops.reshape_and_cache(key, value, key_cache,
                                             value_cache, slot_mapping,
1266
                                             kv_cache_dtype, k_scale, v_scale)
1267
1268


1269
1270
1271
1272
1273
1274
1275
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,
1276
1277
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
1278
) -> None:
1279
1280
    torch.ops._C_cache_ops.reshape_and_cache_flash(key, value, key_cache,
                                                   value_cache, slot_mapping,
1281
1282
                                                   kv_cache_dtype, k_scale,
                                                   v_scale)
1283
1284


1285
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1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
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)


1298
1299
def copy_blocks(key_caches: list[torch.Tensor],
                value_caches: list[torch.Tensor],
1300
                block_mapping: torch.Tensor) -> None:
1301
    torch.ops._C_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
1302
1303


1304
def copy_blocks_mla(kv_caches: list[torch.Tensor],
1305
1306
1307
1308
                    block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.copy_blocks_mla(kv_caches, block_mapping)


1309
def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
1310
                block_mapping: torch.Tensor) -> None:
1311
    torch.ops._C_cache_ops.swap_blocks(src, dst, block_mapping)
1312
1313


1314
1315
1316
1317
def convert_fp8(output: torch.Tensor,
                input: torch.Tensor,
                scale: float = 1.0,
                kv_dtype: str = "fp8") -> None:
1318
1319
1320
    torch.ops._C_cache_ops.convert_fp8(output, input, scale, kv_dtype)


1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
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)


1331
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1333
1334
1335
1336
1337
1338
1339
1340
1341
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
1342
def init_custom_ar(ipc_tensors: list[torch.Tensor], rank_data: torch.Tensor,
1343
                   rank: int, fully_connected: bool) -> int:
1344
    return torch.ops._C_custom_ar.init_custom_ar(ipc_tensors, rank_data, rank,
1345
                                                 fully_connected)
1346
1347


1348
1349
1350
1351
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)
1352

1353
1354
1355
1356
1357
1358
1359
1360
1361

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


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


1362
def register_buffer(fa: int, ipc_tensors: list[int]) -> None:
1363
    return torch.ops._C_custom_ar.register_buffer(fa, ipc_tensors)
1364
1365


1366
def get_graph_buffer_ipc_meta(fa: int) -> tuple[list[int], list[int]]:
1367
1368
1369
    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


1370
1371
def register_graph_buffers(fa: int, handles: list[list[int]],
                           offsets: list[list[int]]) -> None:
1372
    torch.ops._C_custom_ar.register_graph_buffers(fa, handles, offsets)
1373
1374


1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
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)


1387
1388
1389
1390
def get_flash_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_heads_per_head_k: int,
    num_heads_k: int,
1391
) -> tuple[torch.Tensor, torch.Tensor]:
1392
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1400
1401
1402
1403
1404
1405
1406
1407
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1409
1410
1411
1412
1413
1414
1415
1416
    """
    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,
1417
) -> tuple[torch.Tensor, torch.Tensor]:
1418
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1420
1421
1422
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1426
1427
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1429
1430
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1435
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1437
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1442
1443
1444
1445
1446
1447
1448
    """
    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