## What this PR does / why we need it?
pick-from:https://github.com/vllm-project/vllm-ascend/pull/7452
### Problem
Embedding models produce inconsistent outputs when prefix caching is
enabled vs disabled.
### Root Cause
The attention router condition was too broad:
- All `model_runner_type == "pooling"` → `_forward_encoder_attention()`
→ uses `npu_fusion_attention`
- **But `npu_fusion_attention` does NOT support prefix caching**
- Result: Numerical mismatch when KV cache is managed by prefix caching
### Solution
Refine the router condition to check causality:
**Before**:
```
if attn_metadata.model_runner_type == "pooling":
→ npu_fusion_attention (no prefix caching support)
```
**After**:
```
if attn_metadata.model_runner_type == "pooling" and not attn_metadata.causal:
→ npu_fusion_attention (for true encoders)
else:
→ npu_fused_infer_attention_score (prefix caching support)
```
### Changes Made
1. **Fixed router condition** (`vllm_ascend/attention/attention_v1.py`
L968)
- Added `and not attn_metadata.causal` check
- Effect: Non-causal embeddings now use correct operator
2. **Simplified encoder attention**
(`vllm_ascend/attention/attention_v1.py` L864-877)
- Removed redundant causal branch (encoders never use causal mask)
- Reduced from 34 lines to 14 lines
3. **Added test** (`tests/e2e/singlecard/pooling/test_embedding.py`)
- Validates embedding outputs with/without prefix caching are consistent
## Does this PR introduce _any_ user-facing change?
### Functional Changes
✅ **Yes** - Bug fix: Embedding models now produce consistent outputs
with prefix caching
### API Changes
❌ **No** - All public APIs unchanged
### Configuration Changes
❌ **No** - No new configuration required
### Backward Compatibility
✅ **Fully compatible** - Only fixes incorrect behavior
## How was this patch tested?
### New Test
Added `test_embed_models_using_prefix_caching_correctness()`:
- Tests: `Qwen3-Embedding-0.6B`
- Validates numerical consistency between runs with/without prefix
caching
- Uses long sequences to activate prefix caching
- Tolerance: 1e-2
- vLLM version: v0.18.0
Signed-off-by: underfituu <hzhucong@163.com>
### What this PR does / why we need it?
1. For all parts of the current test module involving the millisecond
download model, add the `local_file_only` parameter to specify offline
mode; this ensures that CI will not fail due to network instability.
2. Install modelscope from a fixed commit until it next release
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
check if the env or arg `local_files_only` works
1) set the env:
```shell
export HF_HUB_OFFLINE=1
```
2) run the script
```python
from transformers import PretrainedConfig
import huggingface_hub
from modelscope.utils.hf_util import patch_hub
patch_hub()
model="Qwen/Qwen3-0.6B"
kwargs = {}
config_dict, _ = PretrainedConfig.get_config_dict(
model,
trust_remote_code=True,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
**kwargs,
)
print(config_dict)
```
it works well:
```shell
2026-03-06 06:40:12,546 - modelscope - WARNING - We can not confirm the cached file is for revision: master
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
{'architectures': ['Qwen3ForCausalLM'], 'attention_bias': False, 'attention_dropout': 0.0, 'bos_token_id': 151643, 'eos_token_id': 151645, 'head_dim': 128, 'hidden_act': 'silu', 'hidden_size': 1024, 'initializer_range': 0.02, 'intermediate_size': 3072, 'max_position_embeddings': 40960, 'max_window_layers': 28, 'model_type': 'qwen3', 'num_attention_heads': 16, 'num_hidden_layers': 28, 'num_key_value_heads': 8, 'rms_norm_eps': 1e-06, 'rope_scaling': None, 'rope_theta': 1000000, 'sliding_window': None, 'tie_word_embeddings': True, 'torch_dtype': 'bfloat16', 'transformers_version': '4.51.0', 'use_cache': True, 'use_sliding_window': False, 'vocab_size': 151936, '_commit_hash': None}
```
3) test the model repo does not cached locally when the env
`HF_HUB_OFFLINE`==True
```python
from transformers import PretrainedConfig
import huggingface_hub
from modelscope.utils.hf_util import patch_hub
patch_hub()
model="FireRedTeam/FireRed-OCR"
kwargs = {}
config_dict, _ = PretrainedConfig.get_config_dict(
model,
trust_remote_code=True,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
**kwargs,
)
print(config_dict)
```
and the result is as expected:
```shell
File "/workspace/demo.py", line 12, in <module>
config_dict, _ = PretrainedConfig.get_config_dict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/utils/hf_util/patcher.py", line 189, in patch_get_config_dict
model_dir = get_model_dir(pretrained_model_name_or_path,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/utils/hf_util/patcher.py", line 164, in get_model_dir
model_dir = snapshot_download(
^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/hub/snapshot_download.py", line 137, in snapshot_download
return _snapshot_download(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/hub/snapshot_download.py", line 283, in _snapshot_download
raise ValueError(
ValueError: Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable look-ups and downloads online, set 'local_files_only' to False
```
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Add cudagraph_capture_sizes for E2E CI test.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: menogrey <1299267905@qq.com>
### What this PR does / why we need it?
efect e2e ci test：
1. tests/e2e/singlecard/pooling/test_embedding.py: remove the eager
parameter and rename test case
2. tests/e2e/singlecard/pooling/test_scoring.py: Rename test cases
3. tests/e2e/singlecard/pooling/test_classification.py: Rename test case
4. tests/e2e/singlecard/test_quantization.py: remove the eager parameter
and chage model to vllm-ascend/Qwen2.5-0.6B-W8A8 and Rename test case
5. tests/e2e/multicard/test_shared_expert_dp.py: Rename test cases
6. tests/e2e/singlecard/test_sampler.py: Rename test cases
7. tests/e2e/singlecard/test_aclgraph_accuracy.py: Rename test cases
8. tests/e2e/multicard/test_offline_inference_distributed.py: Rename
test cases and remove the eager parameter
9. tests/e2e/multicard/long_sequence/test_accuracy.py: Rename test cases
and remove the eager parameter
10. tests/e2e/multicard/long_sequence/test_basic.py: Rename test cases
and remove the eager parameter
11.tests/e2e/multicard/test_expert_parallel.py:remove the eager
parameter
12.tests/e2e/multicard/test_full_graph_mode.py:remove the eager
parameter
13.tests/e2e/multicard/test_ilama_lora_tp2.py:remove the eager parameter
14.tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py:remove
the eager parameter
15.tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py:remove the
eager parameter
16.tests/e2e/singlecard/test_aclgraph_accuracy.py:remove the eager
parameter
17.tests/e2e/singlecard/test_camem.py:remove the eager parameter
18.tests/e2e/singlecard/test_ilama_lora.py:remove the eager parameter
19.tests/e2e/singlecard/test_multistream_overlap_shared_expert.py:remove
the eager parameter
20.tests/e2e/singlecard/test_vlm.py:remove the eager parameter
21.tests/e2e/singlecard/test_xli:remove the eager parameter
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
Refactor the e2e testcases.
- tests/e2e/multicard/test_weight_loader.py: Remove the unused code.
- tests/e2e/singlecard/multi-modal/test_internvl.py: Move to accuracy
test.
- tests/e2e/singlecard/test_aclgraph.py: Rename the file.
- tests/e2e/singlecard/test_embedding_aclgraph.py : Combine with
tests/e2e/singlecard/test_bge_model.py
- tests/e2e/singlecard/test_completion_with_prompt_embeds.py: Delete
eager mode and modify model to Qwen3-0.6B
- tests/e2e/singlecard/test_quantization.py: Modify model to
Qwen3-0.6B-W8A8
- tests/e2e/singlecard/test_vlm.py: Modify model to Qwen3-VL-8B
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: menogrey <1299267905@qq.com>
### What this PR does / why we need it?
Support pooling models (like `bge-reranker-v2-m3`) in vllm-ascend, this
pr covered the three model types of embed (cls_token, mean_token,
lasttoken).
After this
[commit](17373dcd93),
vllm has provided support for adapting pooling models on the v1 engine.
This PR includes corresponding adaptations on the vllm-ascend side.
Fixes#1960
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: lianyibo <lianyibo1@kunlunit.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
