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[Attention] Update tests to remove deprecated env vars (#30563)

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Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>
This commit is contained in:
Matthew Bonanni
2025-12-17 12:49:59 -05:00
committed by GitHub
parent 9ca8cb38fd
commit 7eb6cb6c18
34 changed files with 580 additions and 447 deletions
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85
tests/basic_correctness/test_basic_correctness.py
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@@ -67,7 +67,6 @@ def _fix_prompt_embed_outputs(
@pytest.mark.parametrize("model_executor", ["uni", "mp"])
@pytest.mark.parametrize("enable_prompt_embeds", [True, False])
def test_models(
monkeypatch: pytest.MonkeyPatch,
hf_runner,
model: str,
backend: str,
@@ -77,48 +76,46 @@ def test_models(
model_executor: str,
enable_prompt_embeds: bool,
) -> None:
with monkeypatch.context() as m:
m.setenv("VLLM_ATTENTION_BACKEND", backend)
# 5042 tokens for gemma2
# gemma2 has alternating sliding window size of 4096
# we need a prompt with more than 4096 tokens to test the sliding window
prompt = (
"The following numbers of the sequence "
+ ", ".join(str(i) for i in range(1024))
+ " are:"
)
example_prompts = [prompt]
# 5042 tokens for gemma2
# gemma2 has alternating sliding window size of 4096
# we need a prompt with more than 4096 tokens to test the sliding window
prompt = (
"The following numbers of the sequence "
+ ", ".join(str(i) for i in range(1024))
+ " are:"
)
example_prompts = [prompt]
with hf_runner(model) as hf_model:
hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
if enable_prompt_embeds:
with torch.no_grad():
prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)
with hf_runner(model) as hf_model:
hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
if enable_prompt_embeds:
with torch.no_grad():
prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)
with VllmRunner(
model,
max_model_len=8192,
enforce_eager=enforce_eager,
enable_prompt_embeds=enable_prompt_embeds,
gpu_memory_utilization=0.7,
async_scheduling=async_scheduling,
distributed_executor_backend=model_executor,
attention_config={"backend": backend},
) as vllm_model:
if enable_prompt_embeds:
vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
vllm_outputs = _fix_prompt_embed_outputs(
vllm_outputs, hf_model, example_prompts
)
else:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
with VllmRunner(
model,
max_model_len=8192,
enforce_eager=enforce_eager,
enable_prompt_embeds=enable_prompt_embeds,
gpu_memory_utilization=0.7,
async_scheduling=async_scheduling,
distributed_executor_backend=model_executor,
) as vllm_model:
if enable_prompt_embeds:
vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
vllm_outputs = _fix_prompt_embed_outputs(
vllm_outputs, hf_model, example_prompts
)
else:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
check_outputs_equal(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
check_outputs_equal(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@multi_gpu_test(num_gpus=2)
@@ -161,12 +158,6 @@ def test_models_distributed(
): # noqa
pytest.skip("enable_prompt_embeds does not work with ray compiled dag.")
if attention_backend:
monkeypatch_context.setenv(
"VLLM_ATTENTION_BACKEND",
attention_backend,
)
for k, v in extra_env.items():
monkeypatch_context.setenv(k, v)
@@ -178,6 +169,7 @@ def test_models_distributed(
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method
# (the default method).
attention_config = {"backend": attention_backend} if attention_backend else None
with vllm_runner(
model,
dtype=dtype,
@@ -185,6 +177,7 @@ def test_models_distributed(
distributed_executor_backend=distributed_executor_backend,
enable_prompt_embeds=enable_prompt_embeds,
gpu_memory_utilization=0.7,
attention_config=attention_config,
) as vllm_model:
if enable_prompt_embeds:
with hf_runner(model, dtype=dtype) as hf_model: