d3de7333dc
## 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>
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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import pytest
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from modelscope import snapshot_download # type: ignore[import-untyped]
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import huggingface_hub
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from tests.e2e.conftest import HfRunner, VllmRunner
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from tests.e2e.utils import check_embeddings_close
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MODELS = [
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"Qwen/Qwen3-Embedding-0.6B", # lasttoken
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"intfloat/multilingual-e5-small" # mean_tokens
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]
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@pytest.mark.parametrize("model", MODELS)
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def test_embed_models_correctness(model: str):
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queries = ['What is the capital of China?', 'Explain gravity']
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model_name = snapshot_download(model, local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,)
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with VllmRunner(
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model_name,
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runner="pooling",
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max_model_len=None,
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cudagraph_capture_sizes=[4],
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) as vllm_runner:
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vllm_outputs = vllm_runner.embed(queries)
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with HfRunner(
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model_name,
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dtype="float32",
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is_sentence_transformer=True,
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) as hf_runner:
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hf_outputs = hf_runner.encode(queries)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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def test_causal_embed_models_using_prefix_caching_correctness():
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# This test is to verify the correctness of prefix caching for embedding models.
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# We compare the outputs of vLLM with and without prefix caching enabled, and check if they are close enough.
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# We set the input query to be very long to make sure prefix caching is triggered.
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queries = ['What is the capital of China?' * 256, 'Explain gravity']
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model_name = snapshot_download("Qwen/Qwen3-Embedding-0.6B", local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,)
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with VllmRunner(
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model_name,
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runner="pooling",
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max_model_len=None,
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cudagraph_capture_sizes=[4],
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enable_prefix_caching=True,
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) as vllm_runner_using_caching:
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vllm_outputs_without_caching = vllm_runner_using_caching.embed(queries)
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vllm_outputs_with_caching = vllm_runner_using_caching.embed(queries)
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check_embeddings_close(
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embeddings_0_lst=vllm_outputs_without_caching,
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embeddings_1_lst=vllm_outputs_with_caching,
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name_0="without_caching",
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name_1="with_caching",
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tol=1e-2,
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)
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def test_bge_m3_correctness():
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queries = ['What is the capital of China?', 'Explain gravity']
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model_name = snapshot_download("BAAI/bge-m3", local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,)
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with VllmRunner(
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model_name,
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runner="pooling",
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cudagraph_capture_sizes=[4],
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) as vllm_aclgraph_runner:
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vllm_aclgraph_outputs = vllm_aclgraph_runner.embed(queries)
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with VllmRunner(
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model_name,
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runner="pooling",
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enforce_eager=True,
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) as vllm_runner:
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vllm_eager_outputs = vllm_runner.embed(queries)
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with HfRunner(
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model_name,
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dtype="float32",
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is_sentence_transformer=True,
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) as hf_runner:
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hf_outputs = hf_runner.encode(queries)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_eager_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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check_embeddings_close(
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embeddings_0_lst=vllm_eager_outputs,
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embeddings_1_lst=vllm_aclgraph_outputs,
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name_0="eager",
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name_1="aclgraph",
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tol=1e-2,
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)
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