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Fix some ci issue and refactor modelrunner (#2445)

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### 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>
This commit is contained in:
Mengqing Cao
2025-08-20 09:01:04 +08:00
committed by GitHub
parent 955411611c
commit 1327f9be1c
28 changed files with 1612 additions and 1020 deletions
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67
vllm_ascend/attention/attention_v1.py
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@@ -20,14 +20,17 @@ from enum import Enum
from typing import List, Optional, Tuple, Type
import torch
import torch.nn as nn
import torch_npu
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer, AttentionType)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.config import VllmConfig
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.utils import direct_register_custom_op
from vllm.utils import cdiv, direct_register_custom_op
from vllm.v1.core.sched.output import SchedulerOutput
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.ops.attention import vanilla_chunked_prefill
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, aligned_16, is_310p,
nd_to_nz_2d, nd_to_nz_spec)
@@ -157,35 +160,49 @@ class AscendMetadata:
class AscendAttentionMetadataBuilder:
def __init__(self, runner):
self.runner = runner
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
):
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.device = device
self.max_num_blocks_per_req = cdiv(self.model_config.max_model_len,
vllm_config.cache_config.block_size)
def reorder_batch(self, input_batch: "InputBatch",
scheduler_output: "SchedulerOutput") -> bool:
return False
def build(self,
num_reqs,
num_actual_tokens,
max_query_len,
enable_dbo_across_dp: bool = False,
is_only_prefill: bool = False,
*args,
**kwargs):
def build(
self,
common_attn_metadata: AscendCommonAttentionMetadata,
model: nn.Module,
):
num_reqs = common_attn_metadata.num_reqs
num_actual_tokens = common_attn_metadata.num_actual_tokens
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
num_reqs
+ 1]
block_table = self.runner.input_batch.block_table[0].get_device_tensor(
)
block_table[:num_reqs, :self.runner.max_num_blocks_per_req] = (
block_table = common_attn_metadata.block_table_tensor
block_table[:num_reqs, :self.max_num_blocks_per_req] = (
block_table[:num_reqs])
query_lens = self.runner.query_lens
seq_lens = self.runner.seq_lens_cpu[:num_reqs]
slot_mapping = self.runner.slot_mapping_cpu[:num_actual_tokens].to(
self.runner.device, non_blocking=True)
attn_mask = self.runner.attn_mask
attn_state = self.runner.attn_state
query_start_loc_cpu = self.runner.query_start_loc_cpu[:num_reqs + 1]
query_start_loc = query_start_loc_cpu.to(self.runner.device,
query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
slot_mapping = common_attn_metadata.slot_mapping_cpu[:
num_actual_tokens].to(
self.device,
non_blocking=
True)
attn_mask = common_attn_metadata.attn_mask
attn_state = common_attn_metadata.attn_state
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
num_reqs
+ 1]
query_start_loc = query_start_loc_cpu.to(self.device,
non_blocking=True)
if is_310p():
@@ -204,12 +221,12 @@ class AscendAttentionMetadataBuilder:
query_start_loc=query_start_loc,
query_lens=query_lens,
seq_lens=seq_lens,
max_query_len=max_query_len,
max_query_len=common_attn_metadata.max_query_len,
slot_mapping=slot_mapping,
attn_mask=attn_mask,
attn_state=attn_state,
enable_dbo_across_dp=enable_dbo_across_dp,
is_only_prefill=is_only_prefill)
enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp,
is_only_prefill=common_attn_metadata.is_only_prefill)
return attn_metadata