xc-llm-ascend/vllm_ascend/attention/utils.py

from dataclasses import dataclass
from typing import Any, List, Optional

import torch
import torch.nn.functional as F
from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group,
                                          is_v1_kv_transfer_group)
from vllm.forward_context import ForwardContext, get_forward_context


@dataclass
# class AscendCommonLongSequenceMetadata:
class AscendPrefillContextParallelMetadata:
    pcp_allgather_restore_idx: torch.Tensor = None

    num_actual_tokens_pcp_padded: Optional[int] = None

    num_computed_tokens_of_pcp_dcp: Optional[list[Optional[list[Optional[
        list[int]]]]]] = None

    q_head_idx_tensor: torch.Tensor = None

    q_tail_idx_tensor: torch.Tensor = None

    kv_with_q_head_nomask_idx_tensor: torch.Tensor = None

    kv_with_q_head_mask_idx_tensor: torch.Tensor = None

    kv_with_q_tail_nomask_idx_tensor: torch.Tensor = None

    kv_with_q_tail_mask_idx_tensor: torch.Tensor = None

    attn_mask_seqlens: torch.Tensor = None

    head_attn_nomask_seqlens: torch.Tensor = None

    tail_attn_nomask_seqlens: torch.Tensor = None

    q_full_idx: torch.Tensor = None

    pcp_prefill_mask: torch.Tensor = None


@dataclass
class AscendCommonAttentionMetadata:
    """
    Per-batch attention metadata, shared across layers and backends.
    AttentionMetadataBuilder instances use it to construct per-layer metadata.
    
    For many of the tensors we keep both GPU and CPU versions.
    """

    query_start_loc: torch.Tensor
    query_start_loc_cpu: torch.Tensor
    """(batch_size + 1,), the start location of each request in query Tensor"""

    seq_lens_cpu: torch.Tensor
    """(batch_size,), the length of each request including both computed tokens
    and newly scheduled tokens"""

    seq_lens: torch.Tensor
    """same to seq_lens_cpu, for compatibility with some new attn metadata
    (such as GDN)."""

    num_computed_tokens_cpu: torch.Tensor
    """(batch_size,), the number of computed tokens for each request"""

    num_reqs: int
    """Number of requests"""
    num_actual_tokens: int
    """Total number of tokens in batch"""

    max_query_len: int
    """Max token number of request in batch"""

    decode_token_per_req: int
    """decode token number per request"""

    block_table_tensor: torch.Tensor

    slot_mapping: torch.Tensor

    actual_seq_lengths_q: list[int]

    positions: torch.Tensor = None

    attn_mask: torch.Tensor = None

    spec_attn_mask: torch.Tensor = None

    attn_state: Any = None

    enable_dbo_across_dp: bool = False

    is_only_prefill: bool = False

    graph_pad_size: int = -1

    # num_input_tokens refers to total number of tokens including
    # padding tokens. It is used to handle some padding operations.
    num_input_tokens: int = 0

    # NOTE: This is a temporary solution for rotary embedding in MLA
    cos: torch.Tensor = None
    sin: torch.Tensor = None

    prefill_context_parallel_metadata: Optional[
        AscendPrefillContextParallelMetadata] = None


def split_decodes_and_prefills(
    common_attn_metadata: AscendCommonAttentionMetadata,
    decode_threshold: int = 1,
) -> tuple[int, int, int, int]:
    """
    Assuming a reordered batch, finds the boundary between prefill and decode
    requests.

    Args:
        common_attn_metadata: AscendCommonAttentionMetadata object containing the
            batch metadata.
        decode_threshold: The maximum query length to be considered a decode.

    Returns:
        num_decodes: The number of decode requests.
        num_prefills: The number of prefill requests.
        num_decode_tokens: The number of tokens in the decode requests.
        num_prefill_tokens: The number of tokens in the prefill requests.
    """
    max_query_len = common_attn_metadata.max_query_len
    num_reqs = common_attn_metadata.num_reqs
    num_tokens = common_attn_metadata.num_actual_tokens
    query_start_loc = common_attn_metadata.query_start_loc_cpu

    if max_query_len <= decode_threshold:
        return num_reqs, 0, num_tokens, 0

    query_lens = query_start_loc[1:] - query_start_loc[:-1]
    is_prefill = query_lens > decode_threshold
    if not torch.any(is_prefill):
        return num_reqs, 0, num_tokens, 0

    first_prefill = is_prefill.int().argmax(dim=-1).item()
    num_decodes = first_prefill
    num_prefills = num_reqs - num_decodes
    num_decode_tokens = query_start_loc[first_prefill].item()
    num_prefill_tokens = num_tokens - num_decode_tokens
    return (num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens)


def wait_for_kv_layer_from_connector(layer_name: str):
    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
        return

    connector = get_kv_transfer_group()

    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if attn_metadata is None:
        return
    # TODO: assert ascendMetadata
    connector.wait_for_layer_load(layer_name)


def maybe_save_kv_layer_to_connector(
    layer_name: str,
    kv_cache_layer: List[torch.Tensor],
):
    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
        return

    connector = get_kv_transfer_group()

    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if attn_metadata is None:
        return
    # TODO: assert ascendMetadata
    connector.save_kv_layer(layer_name, kv_cache_layer, attn_metadata)


def round_up(val: int, align: int) -> int:
    if align == 0:
        return 0
    return -(val // -align) * align


def trans_rope_weight(weight, rope_dim):
    if rope_dim == 0:
        return weight.contiguous()
    nope_part = weight[..., :-rope_dim, :]
    rope_part = weight[..., -rope_dim:, :]
    reordered_rope_part = torch.cat(
        (rope_part[..., ::2, :], rope_part[..., 1::2, :]), dim=-2)
    return torch.cat((nope_part, reordered_rope_part), dim=-2).contiguous()


def transdata(nd_mat, block_size: tuple = (16, 16)):
    r = round_up(nd_mat.shape[0], block_size[0])
    c = round_up(nd_mat.shape[1], block_size[1])
    r_pad = r - nd_mat.shape[0]
    c_pad = c - nd_mat.shape[1]
    nd_mat = F.pad(nd_mat, (0, r_pad, 0, c_pad))
    nz_mat = torch.permute(
        torch.reshape(
            nd_mat,
            (r // block_size[0], block_size[0], c // block_size[1],
             block_size[1]),
        ),
        [2, 0, 1, 3],
    )
    nz_mat = torch.reshape(
        nz_mat,
        (nz_mat.shape[0], nz_mat.shape[1] * nz_mat.shape[2], nz_mat.shape[3]))
    return nz_mat