_custom_ops.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import contextlib
from typing import TYPE_CHECKING, Optional, Union

import torch

import vllm.envs as envs
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.scalar_type import ScalarType
from vllm.utils import direct_register_custom_op
try:
    from lmslim import quant_ops 
    from lmslim import quant_tools 
except Exception:
    print("INFO: Please install lmslim if you want to infer gptq or awq  or w8a8 model.\n") 
try:
    import lightop
except Exception:
    print("INFO: Please install lightop if you want to infer awq of marlin.\n") 

logger = init_logger(__name__)

if not current_platform.is_tpu() and not current_platform.is_hpu():
    try:
        import vllm._C
    except ImportError as e:
        logger.warning("Failed to import from vllm._C with %r", e)


supports_moe_ops = False
with contextlib.suppress(ImportError):
    import vllm._moe_C  # noqa: F401
    supports_moe_ops = True

if TYPE_CHECKING:

    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


# 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,
    seq_lens: torch.Tensor,
    block_size: int,
    max_seq_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
    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,
) -> None:
    torch.ops._C.paged_attention_v1(
        out, query, key_cache, value_cache, num_kv_heads, scale, block_tables,
        seq_lens, block_size, max_seq_len, alibi_slopes, kv_cache_dtype,
        k_scale, v_scale, tp_rank, blocksparse_local_blocks,
        blocksparse_vert_stride, blocksparse_block_size,
        blocksparse_head_sliding_step)


def paged_attention_v2(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
    block_size: int,
    max_seq_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
    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,
) -> None:
    torch.ops._C.paged_attention_v2(
        out, exp_sum, max_logits, tmp_out, query, key_cache, value_cache,
        num_kv_heads, scale, block_tables, seq_lens, block_size, max_seq_len,
        alibi_slopes, kv_cache_dtype, k_scale, v_scale, tp_rank,
        blocksparse_local_blocks, blocksparse_vert_stride,
        blocksparse_block_size, blocksparse_head_sliding_step)

# 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,
#     query_start_loc: Optional[torch.Tensor],
#     block_size: int,
#     max_seq_len: int,
#     alibi_slopes: Optional[torch.Tensor],
#     kv_cache_dtype: str,
#     k_scale: torch.Tensor,
#     v_scale: torch.Tensor,
#     fp8_out_scale: Optional[torch.Tensor] = None,
# ) -> 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,
#                                       query_start_loc, block_size, max_seq_len,
#                                       alibi_slopes, kv_cache_dtype, k_scale,
#                                       v_scale, fp8_out_scale)


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)


# 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)


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


# pos encoding ops
def rotary_embedding(
    positions: torch.Tensor,
    query: torch.Tensor,
    key: Optional[torch.Tensor],
    head_size: int,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
) -> None:
    torch.ops._C.rotary_embedding(positions, query, key, head_size,
                                  cos_sin_cache, is_neox)


def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
                             key: Optional[torch.Tensor], head_size: int,
                             cos_sin_cache: torch.Tensor, is_neox: bool,
                             rot_dim: int,
                             cos_sin_cache_offsets: torch.Tensor) -> None:
    torch.ops._C.batched_rotary_embedding(positions, query, key, head_size,
                                          cos_sin_cache, is_neox, rot_dim,
                                          cos_sin_cache_offsets)


# layer norm ops
def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
             epsilon: float) -> None:
    # 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)


def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
                       weight: torch.Tensor, epsilon: float) -> None:
    torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)
    

# layer norm ops (opt)
def rms_norm_opt(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
             epsilon: float) -> None:
    torch.ops._C.rms_norm_opt(out, input, weight, epsilon)


def fused_add_rms_norm_opt(input: torch.Tensor, residual: torch.Tensor,
                       weight: torch.Tensor, epsilon: float) -> None:
    torch.ops._C.fused_add_rms_norm_opt(input, residual, weight, epsilon)


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


def apply_repetition_penalties_cuda(
        logits: torch.Tensor, prompt_mask: torch.Tensor,
        output_mask: torch.Tensor, repetition_penalties: torch.Tensor) -> None:
    torch.ops._C.apply_repetition_penalties_(logits, prompt_mask, output_mask,
                                             repetition_penalties)


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

    Args:
        logits: The logits tensor of shape [num_seqs, vocab_size].
        prompt_mask: A boolean tensor indicating which tokens appear in the prompt.
        output_mask: A boolean tensor indicating which tokens appear in the output.
        repetition_penalties: The repetition penalties of shape (num_seqs, ).
    """
    if current_platform.is_cuda() and logits.is_contiguous():
        apply_repetition_penalties_cuda(logits, prompt_mask, output_mask,
                                        repetition_penalties)
    else:
        apply_repetition_penalties_torch(logits, prompt_mask, output_mask,
                                         repetition_penalties)


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:
    """Advance a step on GPU for existing inputs for a multi-step runner"""
    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)

# trans_w16
def trans_w16_gemm(dst: torch.Tensor, src: torch.Tensor,
                row:int, col:int) -> None :
    torch.ops._C.trans_w16_gemm(dst,src,row,col)
    

# 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
) -> tuple[torch.Tensor, torch.Tensor]:
    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


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

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

def awq_dequantize(qweight: torch.Tensor, scales: torch.Tensor,
                   zeros: torch.Tensor, split_k_iters: int, thx: int,
                   thy: int) -> torch.Tensor:
    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)
    return torch.ops._C.awq_dequantize(qweight, scales, zeros, split_k_iters,
                                       thx, thy)


# def awq_gemm(input: torch.Tensor, qweight: torch.Tensor, qzeros: torch.Tensor,
#              scales: torch.Tensor, split_k_iters: int) -> torch.Tensor:
#     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)
#     return torch.ops._C.awq_gemm(input, qweight, qzeros, scales, split_k_iters)


def awq_gemm(input: torch.Tensor, weight: torch.Tensor,
             zeros_and_scales:torch.Tensor,
             m:int,n:int,k:int,
             group_size:int,padding_group:int,splikspace:torch.Tensor,
            splikspacesize:int) -> torch.Tensor:
    return quant_ops.awq_gemm(input,
                              weight,
                              zeros_and_scales,
                              m,
                              n,
                              k,
                              group_size,
                              padding_group,
                              splikspace,
                              splikspacesize)

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

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

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

def dequant_w4_gemm_colmajor(qweight:torch.Tensor,
                                zeros_and_scale:torch.Tensor,
                                k:int,
                                n:int,
                                group_size:int
                             )->torch.Tensor:
    return quant_ops.dequant_w4_gemm_colmajor(qweight,zeros_and_scale,k,n,group_size)


# 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:
    return quant_ops.gptq_gemm(a, b_q_weight, b_gptq_qzeros, b_gptq_scales,
                                  b_g_idx, use_exllama, bit)
    # return torch.ops._C.gptq_gemm(a, b_q_weight, b_gptq_qzeros, b_gptq_scales,
    #                               b_g_idx, use_exllama, bit)


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

    @register_fake("_C::gptq_gemm")
    def _gptq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                        b_gptq_qzeros: torch.Tensor,
                        b_gptq_scales: torch.Tensor, b_g_idx: torch.Tensor,
                        use_exllama: bool, bit: int) -> torch.Tensor:
        return torch.empty((a.size(0), b_q_weight.size(1)),
                           dtype=a.dtype,
                           device=a.device)


def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor,
                 bit: int) -> None:
    quant_ops.gptq_shuffle(q_weight, q_perm, bit)
    # torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)



# 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:
#     return torch.ops._C.marlin_gemm(a, b_q_weight, b_scales, workspace, size_m,
#                                     size_n, size_k)


# # marlin_24
# def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
#                         b_meta: torch.Tensor, b_scales: torch.Tensor,
#                         workspace: torch.Tensor, b_q_type: ScalarType,
#                         size_m: int, size_n: int, size_k: int) -> torch.Tensor:
#     return torch.ops._C.gptq_marlin_24_gemm(a, b_q_weight, b_meta, b_scales,
#                                             workspace, b_q_type.id, size_m,
#                                             size_n, size_k)


# if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):

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

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

#     @register_fake("_C::marlin_qqq_gemm")
#     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,
#                               size_m: torch.SymInt, size_n: torch.SymInt,
#                               size_k: torch.SymInt) -> torch.Tensor:
#         return torch.empty((size_m, size_n),
#                            dtype=torch.float16,
#                            device=a.device)

#     @register_fake("_C::marlin_gemm")
#     def _marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
#                           b_scales: torch.Tensor, workspace: torch.Tensor,
#                           size_m: torch.SymInt, size_n: torch.SymInt,
#                           size_k: torch.SymInt) -> torch.Tensor:
#         return torch.empty((size_m, size_n),
#                            dtype=torch.float16,
#                            device=a.device)

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

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

#     @register_fake("_C::aqlm_gemm")
#     def _aqlm_gemm_fake(input: torch.Tensor, codes: torch.Tensor,
#                         codebooks: torch.Tensor, scales: torch.Tensor,
#                         codebook_partition_sizes: list[int],
#                         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))

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

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

#     @register_fake("_C::machete_prepack_B")
#     def machete_prepack_B_fake(
#             b_q_weight: torch.Tensor, a_type: torch.dtype, b_type: ScalarType,
#             group_scales_type: Optional[torch.dtype]) -> torch.Tensor:
#         return torch.empty_like(b_q_weight,
#                                 memory_format=torch.contiguous_format)


# 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)


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

    @register_fake("_C::ggml_dequantize")
    def _ggml_dequantize_fake(
            W: torch.Tensor,
            quant_type: int,
            m: torch.SymInt,
            n: torch.SymInt,
            dtype: Optional[torch.dtype] = None) -> torch.Tensor:
        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:
        return torch.empty((X.shape[0], row), dtype=X.dtype, device=W.device)

    @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)
        return torch.empty((batch, row), dtype=X.dtype, device=W.device)

    @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)


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)


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


def cutlass_blockwise_scaled_grouped_mm(
    output: torch.Tensor,
    a: torch.Tensor,
    b: torch.Tensor,
    scales_a: torch.Tensor,
    scales_b: torch.Tensor,
    problem_sizes: torch.Tensor,
    expert_offsets: torch.Tensor,
):
    torch.ops._C.cutlass_blockwise_scaled_grouped_mm(output, a, b, scales_a,
                                                     scales_b, problem_sizes,
                                                     expert_offsets)


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


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


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)


def cutlass_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    """
    `cutlass_scaled_mm` implements a fused version of
        `output = torch.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
    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
        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
    """
    assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
    assert bias is None or bias.shape[0] == b.shape[
        1] and bias.dtype == out_dtype

    # m = a.shape[0]
    # n = b.shape[1]

    # cutlass_compatible_b = (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
    # if current_platform.is_rocm() or not cutlass_compatible_b:
    #     from vllm.model_executor.layers.quantization.compressed_tensors.triton_scaled_mm import (  # noqa
    #         triton_scaled_mm)
    #     return triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)

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

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

    # return out
    #return quant_ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)

def rocblas_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:

    return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)

def triton_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
                      bias: Optional[torch.Tensor] = None,
                      best_config:Optional[list] = None) -> torch.Tensor:

    return quant_ops.triton_scaled_mm(a, b,scale_a,scale_b,out_dtype,bias,best_config)

def triton_int8_gemm_helper(m: int,
                             n: int,
                             k: int,
                             per_token_act_quant: bool,
                             per_out_channel_weight_quant: bool,
                             use_bias: bool,
                             out_dtype: type[torch.dtype] = torch.float16,
                             device: str = "cuda:0",
                             best_config:Optional[list] = None,
                             repeat:Optional[int] = 2):
    return quant_tools.triton_int8_gemm_helper(m,n,k,per_token_act_quant,per_out_channel_weight_quant,use_bias,out_dtype,device,best_config,repeat)

def triton_blockint8_gemm_helper(m: int,
                                n: int,
                                k: int,
                                block_size:list=[128,128],
                                use_bias: bool=False,
                                out_dtype: type[torch.dtype] = torch.bfloat16,
                                device: str = "cuda:0",
                                best_config:Optional[dict] = None,
                                repeat:Optional[int] = 2):

    return quant_tools.triton_blockint8_gemm_helper(m,n,k,block_size,use_bias,out_dtype,device,best_config,repeat)


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:
    """
    :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.
    """
    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
    assert azp is None or azp.numel() == a.shape[0]

    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


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


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

def cutlass_sparse_compress(a: torch.Tensor) \
    -> tuple[torch.Tensor, torch.Tensor]:
    """
    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:
        a (torch.Tensor):
            The input tensor to be compressed. Must have one of the following data types:
            - `torch.int8`
            - `torch.float8_e4m3fn`
            - `torch.bfloat16`
            - `torch.float16`

    Returns:
        tuple[torch.Tensor, torch.Tensor]:
            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
    assert (a.shape[1] % (2 * elemsPerMetaElem) == 0)

    return torch.ops._C.cutlass_sparse_compress(a)


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


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,
                            blockscale_offsets: Optional[torch.Tensor] = None):
    """
    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.
    - blockscale_offsets: Optional argument passed for fp4 moe. Indices that
                          mark at which block scale index each expert begins
                          its computation. The number of block scale rows
                          computed with expert E is blockscale_offsets[E + 1] -
                          blockscale_offsets[E]
    """
    return torch.ops._C.get_cutlass_moe_mm_data(topk_ids, expert_offsets,
                                                problem_sizes1, problem_sizes2,
                                                input_permutation,
                                                output_permutation,
                                                num_experts, n, k,
                                                blockscale_offsets)


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


def get_cutlass_pplx_moe_mm_data(expert_offsets: torch.Tensor,
                                 problem_sizes1: torch.Tensor,
                                 problem_sizes2: torch.Tensor,
                                 expert_num_tokens: torch.Tensor,
                                 num_local_experts: int, padded_m: int, n: int,
                                 k: int):
    """
    Prepare data necessary to perform CUTLASS grouped matrix multiplications
    used in CUTLASS-based fused MoE.

    The function takes in expert_num_tokens (token count per expert) and
    non_zero_expert_idxs (consecutive indices of experts with non-zero token 
    counts) and uses them to compute:
    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation.
    - problem_sizes1, problem_sizes2: MxNxK sizes of each expert's
                                      multiplication in two grouped MMs used in
                                      the fused MoE operation.
    """
    return torch.ops._C.get_cutlass_pplx_moe_mm_data(
        expert_offsets, problem_sizes1, problem_sizes2, expert_num_tokens,
        num_local_experts, padded_m, 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,
                   per_act_token: bool, per_out_ch: bool):
    """
    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.
    """
    return torch.ops._C.cutlass_moe_mm(out_tensors, a_tensors, b_tensors,
                                       a_scales, b_scales, expert_offsets,
                                       problem_sizes, a_strides, b_strides,
                                       c_strides, per_act_token, per_out_ch)


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)


# aqlm
def aqlm_gemm(input: torch.Tensor, codes: torch.Tensor,
              codebooks: torch.Tensor, scales: torch.Tensor,
              codebook_partition_sizes: list[int],
              bias: Optional[torch.Tensor]) -> torch.Tensor:
    return torch.ops._C.aqlm_gemm(input, codes, codebooks, scales,
                                  codebook_partition_sizes, bias)


def aqlm_dequant(codes: torch.Tensor, codebooks: torch.Tensor,
                 codebook_partition_sizes: list[int]) -> torch.Tensor:
    return torch.ops._C.aqlm_dequant(codes, codebooks,
                                     codebook_partition_sizes)


# gptq_marlin
def gptq_marlin_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
                       size_k: int, size_n: int,
                       num_bits: int) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_repack(b_q_weight, perm, size_k, size_n,
                                           num_bits)


# 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)


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
    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.gptq_marlin_repack(b_q_weight[e], perm[e],
                                                    size_k, size_n, num_bits)
    return output


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] = lightop.awq_marlin_repack(b_q_weight[e], size_k,
                                                   size_n, num_bits)
    return output


def gptq_marlin_gemm(a: torch.Tensor,
                     c: Optional[torch.Tensor],
                     b_q_weight: torch.Tensor,
                     b_scales: torch.Tensor,
                     global_scale: Optional[torch.Tensor],
                     b_zeros: Optional[torch.Tensor],
                     g_idx: Optional[torch.Tensor],
                     perm: Optional[torch.Tensor],
                     workspace: torch.Tensor,
                     b_q_type: ScalarType,
                     size_m: int,
                     size_n: int,
                     size_k: int,
                     is_k_full: bool = True,
                     use_atomic_add: bool = False,
                     use_fp32_reduce: bool = False,
                     is_zp_float: bool = False) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_gemm(a, c, b_q_weight, b_scales,
                                         global_scale, b_zeros, g_idx, perm,
                                         workspace, b_q_type.id, size_m,
                                         size_n, size_k, is_k_full,
                                         use_atomic_add, use_fp32_reduce,
                                         is_zp_float)


# machete
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,
        out_type: Optional[torch.dtype] = None) -> list[str]:
    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)


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

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


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


# fp4
def scaled_fp4_quant(
        input: torch.Tensor,
        input_global_scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    """
    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:
        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP4 but every
            two values are packed into a uint8 and float8_e4m3 scaling factors
            in the sizzled layout.
    """
    assert not current_platform.is_rocm()
    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


def scaled_fp4_experts_quant(
    input_tensor: torch.Tensor,
    input_global_scale: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Quantize input tensor to FP4 and return quantized tensor and scale, for
    packed MoE Inputs.
    Args:
        input_tensor: The input tensor to be quantized to FP4
        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}.')

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

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

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


# fp8
# def scaled_fp8_quant(
#     input: torch.Tensor,
#     scale: Optional[torch.Tensor] = None,
#     num_token_padding: Optional[int] = None,
#     scale_ub: Optional[torch.Tensor] = None,
#     use_per_token_if_dynamic: bool = False,
#     output: Optional[torch.Tensor] = None,
# ) -> tuple[torch.Tensor, torch.Tensor]:
#     """
#     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
#     optional padding of the output tensors for downstream kernels that
#     will benefit from padding.

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

#     Returns:
#         tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
#             scaling factor.
#     """
#     # This code assumes batch_dim and num_tokens are flattened
#     assert (input.ndim == 2)
#     shape: Union[tuple[int, int], torch.Size] = input.shape
#     # For ROCm on MI300, the output fp8 dtype is torch.float_e3m3fnuz
#     out_dtype: torch.dtype = current_platform.fp8_dtype()
#     if num_token_padding:
#         shape = (max(num_token_padding, input.shape[0]), shape[1])
#     if output is None:
#         output = torch.empty(shape, device=input.device, dtype=out_dtype)
#     else:
#         assert num_token_padding is None, \
#             "padding not supported if output passed in"
#         assert output.dtype == out_dtype

#     if scale is None:
#         if use_per_token_if_dynamic:
#             scale = torch.empty((shape[0], 1),
#                                 device=input.device,
#                                 dtype=torch.float32)
#             torch.ops._C.dynamic_per_token_scaled_fp8_quant(
#                 output, input.contiguous(), scale, scale_ub)
#         else:
#             scale = torch.zeros(1, device=input.device, dtype=torch.float32)
#             torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
#     else:
#         assert scale.numel() == 1, f"{scale.shape}"
#         torch.ops._C.static_scaled_fp8_quant(output, input, scale)

#     return output, scale


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

    Returns:
        tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] :
            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)


# int8
def scaled_int8_quant(
    input: torch.Tensor,
    scale: Optional[torch.Tensor] = None,
    azp: Optional[torch.Tensor] = None,
    symmetric: bool = True
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
    """
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.

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

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

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


# 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)


# gguf
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)


def ggml_mul_mat_vec_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
) -> torch.Tensor:
    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,
) -> torch.Tensor:
    return torch.ops._C.ggml_mul_mat_a8(W, X, quant_type, row)


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_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)


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


# mamba
def causal_conv1d_fwd(x: torch.Tensor, weight: torch.Tensor,
                      bias_: Optional[torch.Tensor],
                      conv_states: Optional[torch.Tensor],
                      query_start_loc: Optional[torch.Tensor],
                      cache_indices: Optional[torch.Tensor],
                      has_initial_state: Optional[torch.Tensor],
                      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):
    torch.ops._C.selective_scan_fwd(u, delta, A, B, C, D_, z_, delta_bias_,
                                    delta_softplus, query_start_loc,
                                    cache_indices, has_initial_state,
                                    ssm_states, pad_slot_id)


# 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


# moe
def moe_sum(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum(input, output)
def moe_sum_opt1(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum_opt1(input, output)

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:
    torch.ops._moe_C.moe_align_block_size(topk_ids, num_experts, block_size,
                                          sorted_token_ids, experts_ids,
                                          num_tokens_post_pad)


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:
    if not current_platform.is_cuda():
        raise NotImplementedError(
            "The optimized moe_wna16_gemm kernel is only "
            "available on CUDA platforms")
    torch.ops._moe_C.moe_wna16_gemm(input, output, b_qweight, b_scales,
                                    b_qzeros, topk_weights, sorted_token_ids,
                                    experts_ids, num_tokens_post_pad, top_k,
                                    BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K,
                                    bit)


def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
                 token_expert_indices: torch.Tensor,
                 gating_output: torch.Tensor) -> None:
    torch.ops._moe_C.topk_softmax(topk_weights, topk_ids, token_expert_indices,
                                  gating_output)


def moe_wna16_marlin_gemm(input: torch.Tensor, output: Optional[torch.Tensor],
                          b_qweight: torch.Tensor, b_scales: torch.Tensor,
                          global_scale: Optional[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.ops._moe_C.moe_wna16_marlin_gemm(
        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)


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

    @register_fake("_moe_C::marlin_gemm_moe")
    def marlin_gemm_moe_fake(a: torch.Tensor, b_q_weights: torch.Tensor,
                             sorted_ids: torch.Tensor,
                             topk_weights: torch.Tensor,
                             topk_ids: torch.Tensor, b_scales: torch.Tensor,
                             b_zero_points: torch.Tensor, g_idx: torch.Tensor,
                             perm: torch.Tensor, workspace: torch.Tensor,
                             b_q_type: ScalarType, size_m: torch.SymInt,
                             size_n: torch.SymInt, size_k: torch.SymInt,
                             is_k_full: bool, num_experts: int, topk: int,
                             moe_block_size: int, replicate_input: bool,
                             apply_weights: bool) -> torch.Tensor:
        return torch.empty((size_m, topk, size_n),
                           dtype=a.dtype,
                           device=a.device)

    @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)


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,
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache(key, value, key_cache,
                                             value_cache, slot_mapping,
                                             kv_cache_dtype, k_scale, v_scale)


def reshape_and_cache_cuda(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache_cuda(key, value, key_cache, 
                                                  value_cache, slot_mapping, 
                                                  kv_cache_dtype, k_scale, v_scale)


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,
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache_flash(key, value, key_cache,
                                                   value_cache, slot_mapping,
                                                   kv_cache_dtype, k_scale,
                                                   v_scale)


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)


def copy_blocks(key_caches: list[torch.Tensor],
                value_caches: list[torch.Tensor],
                block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)


def copy_blocks_mla(kv_caches: list[torch.Tensor],
                    block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.copy_blocks_mla(kv_caches, block_mapping)


def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
                block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.swap_blocks(src, dst, block_mapping)


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


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)


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
def init_custom_ar(ipc_tensors: list[torch.Tensor], rank_data: torch.Tensor,
                   rank: int, fully_connected: bool) -> int:
    return torch.ops._C_custom_ar.init_custom_ar(ipc_tensors, rank_data, rank,
                                                 fully_connected)


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)


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


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


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


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


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


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)


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

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

# quick all reduce
def init_custom_qr(rank: int,
                   world_size: int,
                   qr_max_size: Optional[int] = None) -> int:
    return torch.ops._C_custom_ar.init_custom_qr(rank, world_size, qr_max_size)


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


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


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


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


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


def get_flash_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_heads_per_head_k: int,
    num_heads_k: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    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,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    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


# 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


def moe_fused_gate(
    input_tensor,
    bias,
    num_expert_group,
    topk_group,
    topk,
    n_share_experts_fusion=0,
    routed_scaling_factor=0,
):
    # This fused kernel function is used to select topk expert in a hierarchical 2-layer fashion
    # it split group of expert into num_expert_group, and use top2 expert weight sum in each group
    # as the group weight to select exerpt groups and then select topk experts within the selected groups
    # the #experts is decided by the input tensor shape and we currently only support power of 2 #experts
    # and #experts should be divisible by num_expert_group. #expert/num_expert_group <= 32 is limitted for now.
    # for non-supported case, we suggestion to use the biased_grouped_topk func in sglang.srt.layers.moe.topk
    # n_share_experts_fusion: if > 0, the last expert will be replaced with a round-robin shared expert
    # routed_scaling_factor: if > 0, the last expert will be scaled by this factor
    return torch.ops._moe_C.moe_fused_gate(
        input_tensor,
        bias,
        num_expert_group,
        topk_group,
        topk,
        n_share_experts_fusion,
        routed_scaling_factor,
    )

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

    @register_fake("_moe_C::moe_fused_gate")
    def moe_fused_gate_fake(
        input_tensor: torch.Tensor,
        bias: torch.Tensor,
        num_expert_group: int,
        topk_group: int,
        topk: int,
        n_share_experts_fusion: int,
        routed_scaling_factor: int,
    ):
        return torch.empty((input_tensor.size(0), topk),
                           dtype=input_tensor.dtype,
                           device=input_tensor.device), \
                    torch.empty((input_tensor.size(0), topk),
                           dtype=input_tensor.dtype,
                           device=input_tensor.device)


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

    @register_fake("_C::weight_packed_linear")
    def weight_packed_linear_fake(mat1: torch.Tensor, mat2: torch.Tensor,
                                  bias: Optional[torch.Tensor],
                                  is_vnni: bool) -> torch.Tensor:
        return torch.empty((mat1.size(0), mat2.size(0)),
                           dtype=mat1.dtype,
                           device=mat2.device)


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

    @register_fake("_C::fused_experts_cpu")
    def fused_experts_cpu_fake(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        inplace: bool,
        use_int8_w8a8: bool,
        use_fp8_w8a16: bool,
        w1_scale: Optional[torch.Tensor],
        w2_scale: Optional[torch.Tensor],
        block_size: Optional[list[int]],
        a1_scale: Optional[torch.Tensor],
        a2_scale: Optional[torch.Tensor],
        is_vnni: bool,
    ) -> torch.Tensor:
        return torch.empty_like(hidden_states)


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

    @register_fake("_C::int8_scaled_mm_with_quant")
    def int8_scaled_mm_with_quant_fake(
        mat1: torch.Tensor,
        mat2: torch.Tensor,
        scales2: torch.Tensor,
        bias: Optional[torch.Tensor],
        out_dtype: torch.dtype,
        is_vnni: bool,
    ) -> torch.Tensor:
        M = mat1.size(0)
        N = mat2.size(0)
        return torch.empty((M, N), dtype=out_dtype)

direct_register_custom_op(
    op_name="awq_gemm",
    op_func=awq_gemm,
    mutates_args=[],
    fake_impl=awq_gemm_fake,
)