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[0.7.3][5/N][CI/UT]add spec decode e2e UT && [BUGFIX]fix chunk prefill bug #560

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Merged
merged 1 commit into from
Apr 21, 2025
Merged

[0.7.3][5/N][CI/UT]add spec decode e2e UT && [BUGFIX]fix chunk prefill bug #560

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5 changes: 3 additions & 2 deletions .github/workflows/vllm_ascend_test.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -148,8 +148,9 @@ jobs:
- name: Run vllm-project/vllm-ascend key feature test
if: steps.filter.outputs.speculative_tests_changed
run: |
pytest -sv tests/spec_decode/e2e/test_mtp_correctness.py
pytest -sv tests/spec_decode --ignore=tests/spec_decode/e2e/test_mtp_correctness.py
pytest -sv tests/spec_decode/e2e/test_mtp_correctness.py # it needs a clean process
pytest -sv tests/spec_decode/e2e/test_multistep_correctness.py # it needs a clean process
pytest -sv tests/spec_decode --ignore=tests/spec_decode/e2e/test_mtp_correctness.py --ignore=tests/spec_decode/e2e/test_multistep_correctness.py

- name: Run vllm-project/vllm test
run: |
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56 changes: 32 additions & 24 deletions tests/spec_decode/e2e/test_eagle_correctness.py
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Expand Up @@ -350,13 +350,13 @@ def test_eagle_disable_queue(vllm_runner, common_llm_kwargs,
"dtype": "float16",

# Main model
"model_name": "meta-llama/Llama-2-7b-chat-hf"
"model_name": "vllm-ascend/Llama-2-7b-chat-hf"
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
{
"speculative_model": "yuhuili/EAGLE-llama2-chat-7B",
"speculative_model": "vllm-ascend/EAGLE-llama2-chat-7B",
"num_speculative_tokens": MAX_SPEC_TOKENS,
},
])
Expand All @@ -368,21 +368,25 @@ def test_eagle_disable_queue(vllm_runner, common_llm_kwargs,
])
@pytest.mark.parametrize("batch_size", [1, 5])
@pytest.mark.parametrize("seed", [1])
def test_llama2_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
def test_llama2_eagle_e2e_greedy_correctness(monkeypatch: pytest.MonkeyPatch,
vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs, batch_size: int,
output_len: int, seed: int):

run_equality_correctness_test(vllm_runner,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
output_len,
seed,
temperature=0.0)
# TODO: it is a wrong way to use modelscope.
with monkeypatch.context() as m:
m.setenv("VLLM_USE_MODELSCOPE", "True")
run_equality_correctness_test(vllm_runner,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
output_len,
seed,
temperature=0.0)


@pytest.mark.skipif(True, reason="Open it when CI could use modelscope")
Expand All @@ -399,13 +403,13 @@ def test_llama2_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
"dtype": "float16",

# Main model
"model_name": "meta-llama/Meta-Llama-3-8B-Instruct"
"model_name": "vllm-ascend/Meta-Llama-3-8B-Instruct"
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
{
"speculative_model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
"speculative_model": "vllm-ascend/EAGLE-LLaMA3-Instruct-8B",
"num_speculative_tokens": MAX_SPEC_TOKENS,
},
])
Expand All @@ -417,21 +421,25 @@ def test_llama2_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
])
@pytest.mark.parametrize("batch_size", [1, 5])
@pytest.mark.parametrize("seed", [1])
def test_llama3_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
def test_llama3_eagle_e2e_greedy_correctness(monkeypatch: pytest.MonkeyPatch,
vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs, batch_size: int,
output_len: int, seed: int):

run_equality_correctness_test(vllm_runner,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
output_len,
seed,
temperature=0.0)
# TODO: it is a wrong way to use modelscope.
with monkeypatch.context() as m:
m.setenv("VLLM_USE_MODELSCOPE", "True")
run_equality_correctness_test(vllm_runner,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
output_len,
seed,
temperature=0.0)


@pytest.mark.parametrize(
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