aphrodite-engine/aphrodite/_custom_ops.py

import contextlib
from typing import List, Optional, Tuple, Type

import torch

try:
    import aphrodite._C
except ImportError as e:
    from loguru import logger
    logger.warning("Failed to import from vllm._C with %r", e)

with contextlib.suppress(ImportError):
    import aphrodite._moe_C

with contextlib.suppress(ImportError):
    # ruff: noqa: F401
    import aphrodite._punica_C


def is_custom_op_supported(op_name: str) -> bool:
    op, overloads = torch._C._jit_get_operation(op_name)
    return op is not None


# activation ops
def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.silu_and_mul(out, x)


def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_and_mul(out, x)


def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_tanh_and_mul(out, x)


def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_fast(out, x)


def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
    torch.ops._C.gelu_new(out, x)


# 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,
    kv_scale: float,
    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,
        kv_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,
    kv_scale: float,
    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, kv_scale, tp_rank,
        blocksparse_local_blocks, blocksparse_vert_stride,
        blocksparse_block_size, blocksparse_head_sliding_step)


# 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:
    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: 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:
    torch.ops._C.rms_norm(out, input, 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)


# 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:
    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:
    return torch.ops._C.awq_gemm(input, qweight, qzeros, scales, split_k_iters)


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


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


# squeezellm
def squeezellm_gemm(vec: torch.Tensor, mat: torch.Tensor, mul: torch.Tensor,
                    lookup_table: torch.Tensor) -> None:
    torch.ops._C.squeezellm_gemm(vec, mat, mul, lookup_table)


# 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, num_bits: int, 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, num_bits, size_m,
                                            size_n, size_k)


# cutlass
def cutlass_scaled_mm_dq(a: torch.Tensor, b: torch.Tensor,
                         scale_a: torch.Tensor, scale_b: torch.Tensor,
                         out_dtype: Type[torch.dtype]) -> torch.Tensor:
    assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
    assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)

    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_dq(out, a, b, scale_a, scale_b)

    return out


# aqlm
def aqlm_gemm(input: torch.Tensor, codes: torch.Tensor,
              codebooks: torch.Tensor, scales: torch.Tensor,
              codebook_partition_sizes: torch.Tensor,
              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: torch.Tensor) -> 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)


def gptq_marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                     b_scales: torch.Tensor, g_idx: torch.Tensor,
                     perm: torch.Tensor, workspace: torch.Tensor,
                     num_bits: int, size_m: int, size_n: int, size_k: int,
                     is_k_full: bool) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_gemm(a, b_q_weight, b_scales, g_idx, perm,
                                         workspace, num_bits, size_m, size_n,
                                         size_k, is_k_full)


# fp8
def scaled_fp8_quant(
    input: torch.Tensor,
    scale: Optional[torch.Tensor] = None,
    batch_dim_padding: Optional[int] = 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 tensor 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
        batch_dim_padding: If specified, pad the first dimension
            of the output to at least this value.

    Returns:
        Tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
            scaling factor.
    """
    if batch_dim_padding:
        shape = (max(batch_dim_padding, input.shape[0]), *input.shape[1:])
        output = torch.empty(shape,
                             device=input.device,
                             dtype=torch.float8_e4m3fn)
    else:
        output = torch.empty_like(input, dtype=torch.float8_e4m3fn)
    if scale is None:
        scale = torch.zeros(1, device=input.device, dtype=torch.float32)
        torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
    else:
        torch.ops._C.static_scaled_fp8_quant(output, input, scale)
    return output, scale


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

    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.

    Returns:
      Tuple[Torch.Tensor, Torch.Tensor] : Output int8 tensor and scales.
    """
    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
        torch.ops._C.static_scaled_int8_quant(output, input, scale)
        return output, scale

    # dynamic-per-token quantization.
    input_scales = torch.empty((input.numel() // input.shape[-1], 1),
                               device=input.device,
                               dtype=torch.float32)
    torch.ops._C.dynamic_scaled_int8_quant(output, input, input_scales)
    return output, input_scales


# quip#
def quip_gemv(
    A: torch.Tensor,
    B: torch.Tensor,
    CB: torch.Tensor,
) -> torch.Tensor:
    return torch.ops._C.quip_gemv(A, B, CB)


def quip_decompress(
    YIs: torch.Tensor,
    CB: torch.Tensor,
    Y: torch.Tensor,
) -> torch.Tensor:
    return torch.ops._C.quip_decompress(YIs, CB, Y)


# moe
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._C.moe_align_block_size(topk_ids, num_experts, block_size,
                                      sorted_token_ids, experts_ids,
                                      num_tokens_post_pad)


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


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,
    kv_scale: float,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache(key, value, key_cache,
                                             value_cache, slot_mapping,
                                             kv_cache_dtype, kv_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,
) -> None:
    torch.ops._C_cache_ops.reshape_and_cache_flash(key, value, key_cache,
                                                   value_cache, slot_mapping,
                                                   kv_cache_dtype)


def copy_blocks(key_caches: torch.Tensor, value_caches: torch.Tensor,
                block_mapping: torch.Tensor) -> None:
    torch.ops._C_cache_ops.copy_blocks(key_caches, value_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 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(meta: torch.Tensor, rank_data: torch.Tensor,
                   handles: List[str], offsets: List[int], rank: int,
                   full_nvlink: bool) -> int:
    return torch.ops._C_custom_ar.init_custom_ar(meta, rank_data, handles,
                                                 offsets, rank, full_nvlink)


def should_custom_ar(inp: torch.Tensor, max_size: int, world_size: int,
                     full_nvlink: bool) -> bool:
    return torch.ops._C_custom_ar.should_custom_ar(inp, max_size, world_size,
                                                   full_nvlink)


def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
    torch.ops._C_custom_ar.all_reduce_reg(fa, inp, out)


def all_reduce_unreg(fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor,
                     out: torch.Tensor) -> None:
    torch.ops._C_custom_ar.all_reduce_unreg(fa, inp, reg_buffer, out)


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, t: torch.Tensor, handles: List[str],
                    offsets: List[int]) -> None:
    return torch.ops._C_custom_ar.register_buffer(fa, t, handles, offsets)


def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[str], List[int]]:
    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


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


# punica
def dispatch_bgmv(
    y: torch.Tensor,
    x: torch.Tensor,
    w_t_all: torch.Tensor,
    indicies: torch.Tensor,
    layer_idx: int,
    scale: float,
) -> None:
    torch.ops._punica_C.dispatch_bgmv(y, x, w_t_all, indicies, layer_idx,
                                      scale)


def dispatch_bgmv_low_level(
    y: torch.Tensor,
    x: torch.Tensor,
    w_t_all: torch.Tensor,
    indicies: torch.Tensor,
    layer_idx: int,
    scale: float,
    h_in: int,
    h_out: int,
    y_offset: int,
) -> None:
    torch.ops._punica_C.dispatch_bgmv_low_level(
        y,
        x,
        w_t_all,
        indicies,
        layer_idx,
        scale,
        h_in,
        h_out,
        y_offset,
    )