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

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from typing import Any

import numpy as np
import torch
from vllm.config import VllmConfig
from vllm.config.model import ModelDType
from vllm.v1.attention.backends.utils import AttentionMetadataBuilder
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)

_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_cpu: torch.Tensor,
    num_computed_tokens_cpu: torch.Tensor,
    block_tables: Sequence[torch.Tensor],
    slot_mappings: torch.Tensor,
    kv_cache_config: KVCacheConfig,
    decode_token_per_req: int,
    actual_seq_lengths_q: list[int],
    positions: torch.Tensor | None = None,
    attn_mask: torch.Tensor
    | None = None,
    spec_attn_mask: 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())

    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_computed_tokens_cpu=num_computed_tokens_cpu,
            num_reqs=num_reqs,
            num_actual_tokens=num_tokens,
            max_query_len=max_query_len,
            decode_token_per_req=decode_token_per_req,
            block_table_tensor=block_table,
            slot_mapping=slot_mapping,
            actual_seq_lengths_q=actual_seq_lengths_q,
            positions=positions,
            attn_mask=attn_mask,
            spec_attn_mask=spec_attn_mask,
            attn_state=attn_state,
            graph_pad_size=graph_pad_size,
            num_input_tokens=num_input_tokens,
            prefill_context_parallel_metadata=prefill_context_parallel_metadata,
        )

        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


def make_attention_mask(
    vllm_config: VllmConfig,
    attn_state: AscendAttentionState,
    dtype: ModelDType | torch.dtype,
    device: torch.device,
) -> torch.Tensor:
    """make attention mask for npu's attention backend."""
    attn_mask_builder = get_attn_mask_builder(device)
    # pcp situation.
    if attn_mask_builder is None:
        raise ValueError("Attn mask builder is None")
    # Pooling situation.
    if vllm_config.model_config.runner_type == "pooling":
        return attn_mask_builder.get_attn_mask(2048, torch.bool)

    # TODO(Ronald1995) cosidering pcp.
    if vllm_config.model_config.use_mla:
        # mla prefill
        if attn_state != AscendAttentionState.DecodeOnly:
            return attn_mask_builder.get_mla_mask(dtype)
    return attn_mask_builder.get_splitfuse_attn_mask()