xc-llm-ascend/vllm_ascend/worker/v2/attn_utils.py

# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/attn_utils.py
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#

from collections.abc import Sequence
from typing import Any, Tuple

import numpy as np
import torch
from vllm.config import VllmConfig
from vllm.v1.kv_cache_interface import EncoderOnlyAttentionSpec, KVCacheConfig

from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
                                         AscendPrefillContextParallelMetadata)
from vllm_ascend.utils import vllm_version_is

if vllm_version_is('0.14.1'):
    from vllm.v1.attention.backends.utils import AttentionMetadataBuilder
else:
    from vllm.v1.attention.backend import AttentionMetadataBuilder

_ATTENTION_MASK_BUILDER = None


def get_attn_mask_builder(device: torch.device):
    """Get attention mask builder which only have one instance."""
    global _ATTENTION_MASK_BUILDER
    if _ATTENTION_MASK_BUILDER is None:
        _ATTENTION_MASK_BUILDER = AttentionMaskBuilder(device)
    return _ATTENTION_MASK_BUILDER


def build_attn_metadata(
    attn_metadata_builders: list[AttentionMetadataBuilder],
    num_reqs: int,
    num_tokens: int,
    query_start_loc_gpu: torch.Tensor,
    query_start_loc_cpu: torch.Tensor,
    seq_lens: torch.Tensor,
    seq_lens_np: np.ndarray,
    num_computed_tokens_cpu: torch.Tensor | None,
    block_tables: Sequence[torch.Tensor],
    slot_mappings: torch.Tensor,
    kv_cache_config: KVCacheConfig,
    positions: torch.Tensor | None = None,
    attn_state: Any | None = None,
    graph_pad_size: int = -1,
    num_input_tokens: int = 0,
    prefill_context_parallel_metadata: AscendPrefillContextParallelMetadata
    | None = None,
) -> dict[str, Any]:
    """Build attention metadata for Ascend NPUs."""
    # TODO(Ronald1995): optimize AscendCommonAttentionMetadata.
    max_query_len = int(query_start_loc_cpu.max())
    seq_lens_cpu = torch.from_numpy(seq_lens_np)
    max_seq_len = int(seq_lens_cpu.max())
    # torch_npu._reshape_and_cache operator requires slot_mappings to
    # be torch.int32.
    slot_mappings = slot_mappings.to(torch.int32)

    attn_metadata: dict[str, Any] = {}
    kv_cache_groups = kv_cache_config.kv_cache_groups
    for i, kv_cache_spec in enumerate(kv_cache_groups):
        block_table = block_tables[i]
        slot_mapping = slot_mappings[i]

        common_attn_metadata = AscendCommonAttentionMetadata(
            query_start_loc=query_start_loc_gpu,
            query_start_loc_cpu=query_start_loc_cpu,
            seq_lens_cpu=seq_lens_cpu[:num_reqs],
            seq_lens=seq_lens[:num_reqs],
            num_reqs=num_reqs,
            num_actual_tokens=num_tokens,
            max_query_len=max_query_len,
            block_table_tensor=block_table,
            slot_mapping=slot_mapping,
            positions=positions,
            attn_state=attn_state,
            graph_pad_size=graph_pad_size,
            num_input_tokens=num_input_tokens,
            prefill_context_parallel_metadata=prefill_context_parallel_metadata,
            max_seq_len=max_seq_len)

        attn_metadata_builder = attn_metadata_builders[i]
        metadata = attn_metadata_builder.build(
            common_prefix_len=0,
            common_attn_metadata=common_attn_metadata,  # type: ignore
        )
        for layer_name in kv_cache_spec.layer_names:
            attn_metadata[layer_name] = metadata
    return attn_metadata


def build_attn_state(
    vllm_config: VllmConfig,
    seq_lens_np: np.ndarray,
    num_reqs,
    num_scheduled_tokens,
    num_valid_tokens,
):
    """Build attention state for npu's attention backend."""
    if vllm_config.model_config.runner_type == "pooling":
        if isinstance(
                vllm_config.kv_cache_config.kv_cache_groups[0].kv_cache_spec,
                EncoderOnlyAttentionSpec,
        ):
            attn_state = AscendAttentionState.PrefillNoCache
        else:
            attn_state = AscendAttentionState.PrefillCacheHit
    elif np.array_equal(seq_lens_np[:num_reqs], num_scheduled_tokens):
        attn_state = AscendAttentionState.PrefillNoCache
    # We assume it is the decode stage, where prefill occurs
    # but only one token is not hit in cache.
    elif np.all(num_scheduled_tokens == 1):
        attn_state = AscendAttentionState.DecodeOnly
        if (vllm_config.speculative_config
                and vllm_config.speculative_config.method == 'mtp'):
            # SpecDecoding now supports seq_len=1 and seq_len=2
            # In Prefilling Decoding Disaggregation scenario, SpecDecoding
            # need to supports seq_len=1
            attn_state = AscendAttentionState.SpecDecoding
    # Speculative decoding.
    elif np.all(num_valid_tokens == 1):
        if (vllm_config.speculative_config
                and vllm_config.speculative_config.method == 'mtp'):
            attn_state = AscendAttentionState.SpecDecoding
        else:
            attn_state = AscendAttentionState.ChunkedPrefill
    # splitfuse
    elif vllm_config.scheduler_config.enable_chunked_prefill:
        attn_state = AscendAttentionState.ChunkedPrefill
    else:
        attn_state = AscendAttentionState.PrefillCacheHit
    return attn_state