Fix some ci issue and refactor modelrunner (#2445)

### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with existing test.

- vLLM version: v0.10.0
- vLLM main:
4d9c61993a

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>

vllm_ascend/attention/utils.py

@@ -0,0 +1,95 @@
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+
+
+@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"""
+
+    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_cpu: 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
+
+
+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()
+    assert torch.all(query_lens[first_prefill:] >= decode_threshold)
+    assert torch.all(query_lens[:first_prefill] <= decode_threshold)
+    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)