_custom_ops.py

from typing import Dict, Optional, Tuple

import torch

try:
    from vllm._C import cache_ops as vllm_cache_ops
    from vllm._C import ops as vllm_ops
except ImportError:
    pass


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


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


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


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


def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
    vllm_ops.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,
    context_lens: torch.Tensor,
    block_size: int,
    max_context_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
    kv_scale: float,
) -> None:
    vllm_ops.paged_attention_v1(out, query, key_cache, value_cache,
                                num_kv_heads, scale, block_tables,
                                context_lens, block_size, max_context_len,
                                alibi_slopes, kv_cache_dtype, kv_scale)


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,
    context_lens: torch.Tensor,
    block_size: int,
    max_context_len: int,
    alibi_slopes: Optional[torch.Tensor],
    kv_cache_dtype: str,
    kv_scale: float,
) -> None:
    vllm_ops.paged_attention_v2(out, exp_sum, max_logits, tmp_out, query,
                                key_cache, value_cache, num_kv_heads, scale,
                                block_tables, context_lens, block_size,
                                max_context_len, alibi_slopes, kv_cache_dtype,
                                kv_scale)


# 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:
    vllm_ops.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:
    vllm_ops.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:
    vllm_ops.rms_norm(out, input, weight, epsilon)


def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
                       weight: torch.Tensor, epsilon: float) -> None:
    vllm_ops.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 vllm_ops.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 vllm_ops.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 vllm_ops.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:
    vllm_ops.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:
    vllm_ops.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 vllm_ops.marlin_gemm(a, b_q_weight, b_scales, workspace, size_m,
                                size_n, size_k)


# fp8
def scaled_fp8_quant(input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    scale = torch.zeros(1, device=input.device, dtype=torch.float32)
    output = torch.empty_like(input, dtype=torch.float8_e4m3fn)
    vllm_ops.scaled_fp8_quant(output, input, scale)
    return output, scale


# 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:
    vllm_ops.moe_align_block_size(topk_ids, num_experts, block_size,
                                  sorted_token_ids, experts_ids,
                                  num_tokens_post_pad)


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:
    vllm_cache_ops.reshape_and_cache(key, value, key_cache, value_cache,
                                     slot_mapping, kv_cache_dtype, kv_scale)


def copy_blocks(key_caches: torch.Tensor, value_caches: torch.Tensor,
                block_mapping: torch.Tensor) -> None:
    vllm_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)


def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
                block_mapping: Dict[int, int]) -> None:
    vllm_cache_ops.swap_blocks(src, dst, block_mapping)


def convert_fp8(output: torch.Tensor, input: torch.Tensor) -> None:
    vllm_cache_ops.convert_fp8(output, input)


#TODO: cuda_utils, custom_ar