[Bugfix] eagle and eagle3 spec decode failures and enable e2e test (#2979)
### What this PR does / why we need it?
- Fix the bug https://github.com/vllm-project/vllm-ascend/issues/2978
- Enable e2e test,
- Adapt to scenarios where Speculative tokens are greater than 2,
- Fix the bug that causes Eagle3 inference failures under high
concurrency and improve the acceptance rate of draft models, by
https://github.com/vllm-project/vllm-ascend/pull/2794
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
CI passed with new added/existing test.
Co-authored-by: hukongyi
[hukongyi@cmbchina.com](mailto:hukongyi@cmbchina.com)
Co-authored-by: guanyuzhu
[zhuguanyu@huawei.com](mailto:zhuguanyu@huawei.com)
Co-authored-by: liumail680
[liumail680@163.com](mailto:liumail680@163.com)
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
---------
Signed-off-by: Icey <1790571317@qq.com>
This commit is contained in:
@@ -1,6 +1,7 @@
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import os
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import random
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from typing import Any
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@@ -9,6 +10,8 @@ from vllm import LLM, SamplingParams
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from tests.e2e.conftest import VllmRunner
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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@pytest.fixture
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def test_prompts():
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@@ -99,7 +102,6 @@ def test_ngram_correctness(
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assert matches > int(0.7 * len(ref_outputs))
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@pytest.mark.skipif(True, reason="oom in CI, fix me")
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@pytest.mark.parametrize("use_eagle3", [False, True], ids=["eagle", "eagle3"])
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def test_eagle_correctness(
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test_prompts: list[list[dict[str, Any]]],
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@@ -111,8 +113,6 @@ def test_eagle_correctness(
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Compare the outputs of a original LLM and a speculative LLM
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should be the same when using eagle speculative decoding.
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'''
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if not use_eagle3:
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pytest.skip("Not current support for the test.")
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ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
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ref_outputs = ref_llm.chat(test_prompts, sampling_config)
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@@ -121,7 +121,6 @@ def test_eagle_correctness(
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spec_model_name = eagle3_model_name() if use_eagle3 else eagle_model_name()
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with VllmRunner(
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model_name,
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trust_remote_code=True,
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enable_chunked_prefill=True,
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max_num_seqs=1,
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max_num_batched_tokens=2048,
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