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[V0 Deprecation] Remove VLLM_USE_V1 from tests (#26341)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-10-07 23:42:31 +08:00
committed by GitHub
parent c0a7b89d8e
commit 1e4ecca1d0
51 changed files with 817 additions and 1275 deletions
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106
tests/v1/tpu/test_basic.py
View File

@@ -42,7 +42,6 @@ MAX_NUM_REQS = [16, 1024]
@pytest.mark.parametrize("max_num_seqs", MAX_NUM_REQS)
def test_basic(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
model: str,
max_tokens: int,
tensor_parallel_size: int,
@@ -55,23 +54,20 @@ def test_basic(
)
example_prompts = [prompt]
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
# Note: max_num_batched_tokens == 1024 is needed here to
# actually test chunked prompt
max_num_batched_tokens=1024,
max_model_len=8192,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
output = vllm_outputs[0][1]
with vllm_runner(
model,
# Note: max_num_batched_tokens == 1024 is needed here to
# actually test chunked prompt
max_num_batched_tokens=1024,
max_model_len=8192,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
output = vllm_outputs[0][1]
assert "1024" in output or "0, 1" in output
assert "1024" in output or "0, 1" in output
@pytest.mark.skip(reason="Temporarily disabled due to timeout")
@@ -82,7 +78,6 @@ def test_basic(
@pytest.mark.parametrize("max_num_seqs", [16])
def test_phi3(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
max_tokens: int,
max_num_seqs: int,
) -> None:
@@ -99,18 +94,15 @@ def test_phi3(
# test head dim = 96
model = "microsoft/Phi-3-mini-128k-instruct"
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model, max_num_batched_tokens=256, max_num_seqs=max_num_seqs
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
for output, answer in zip(vllm_outputs, answers):
generated_text = output[1]
assert answer in generated_text
with vllm_runner(
model, max_num_batched_tokens=256, max_num_seqs=max_num_seqs
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
for output, answer in zip(vllm_outputs, answers):
generated_text = output[1]
assert answer in generated_text
TP_SIZE_8 = 8
@@ -123,7 +115,6 @@ TP_SIZE_8 = 8
)
def test_gemma3_27b_with_text_input_and_tp(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
) -> None:
model = "google/gemma-3-27b-it"
max_tokens = 16
@@ -140,21 +131,18 @@ def test_gemma3_27b_with_text_input_and_tp(
" but in rising every time we fall.",
]
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
max_num_batched_tokens=256,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
for output, answer in zip(vllm_outputs, answers):
generated_text = output[1]
assert answer in generated_text
with vllm_runner(
model,
max_num_batched_tokens=256,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
for output, answer in zip(vllm_outputs, answers):
generated_text = output[1]
assert answer in generated_text
@pytest.mark.skipif(
@@ -162,7 +150,6 @@ def test_gemma3_27b_with_text_input_and_tp(
)
def test_w8a8_quantization(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
) -> None:
model = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
max_tokens = 5
@@ -176,18 +163,15 @@ def test_w8a8_quantization(
)
example_prompts = [prompt]
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
max_num_batched_tokens=64,
max_model_len=4096,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
output = vllm_outputs[0][1]
with vllm_runner(
model,
max_num_batched_tokens=64,
max_model_len=4096,
gpu_memory_utilization=0.7,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
output = vllm_outputs[0][1]
assert "1024" in output or "0, 1" in output
assert "1024" in output or "0, 1" in output

76
tests/v1/tpu/test_perf.py
View File

@@ -86,7 +86,6 @@ GPU_UTIL = 0.9
@pytest.mark.parametrize("params", TEST_PARAMS)
def test_perf(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
params: TestParams,
) -> None:
tokenizer = get_tokenizer(
@@ -107,48 +106,45 @@ def test_perf(
)
)
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
sampling_params = SamplingParams(
max_tokens=params.decode_len, temperature=1.0, min_p=0.0
)
sampling_params = SamplingParams(
max_tokens=params.decode_len, temperature=1.0, min_p=0.0
)
with vllm_runner(
params.model,
max_num_batched_tokens=MAX_MODEL_LEN,
max_model_len=MAX_MODEL_LEN,
max_num_seqs=MAX_NUM_SEQS,
gpu_memory_utilization=GPU_UTIL,
enforce_eager=False,
tensor_parallel_size=1,
) as vllm_model:
print(" -- Warmup / Compile")
for i in range(NUM_WARMUPS):
_ = vllm_model.generate(prompts, sampling_params)
with vllm_runner(
params.model,
max_num_batched_tokens=MAX_MODEL_LEN,
max_model_len=MAX_MODEL_LEN,
max_num_seqs=MAX_NUM_SEQS,
gpu_memory_utilization=GPU_UTIL,
enforce_eager=False,
tensor_parallel_size=1,
) as vllm_model:
print(" -- Warmup / Compile")
for i in range(NUM_WARMUPS):
_ = vllm_model.generate(prompts, sampling_params)
print(" -- Benchmarking... ")
times = []
for i in range(NUM_RUNS):
start_time = time.time()
_ = vllm_model.generate(prompts, sampling_params)
times.append(time.time() - start_time)
print(" -- Benchmarking... ")
times = []
for i in range(NUM_RUNS):
start_time = time.time()
_ = vllm_model.generate(prompts, sampling_params)
times.append(time.time() - start_time)
avg_time = sum(times) / len(times)
avg_time = sum(times) / len(times)
print(" -- avg_time = {}".format(avg_time))
print(
" -- expected_avg_time = {} with err_tol = {}".format(
params.expected_avg_time, params.err_tol
)
print(" -- avg_time = {}".format(avg_time))
print(
" -- expected_avg_time = {} with err_tol = {}".format(
params.expected_avg_time, params.err_tol
)
)
diff = avg_time - params.expected_avg_time
ok = diff < params.err_tol
if diff < -params.err_tol:
print(
" !! WARNING !! Performance has improved by {}, "
"it may be necessary to fine-tune the "
"expected_avg_time = {}".format(-diff, params.expected_avg_time)
)
diff = avg_time - params.expected_avg_time
ok = diff < params.err_tol
if diff < -params.err_tol:
print(
" !! WARNING !! Performance has improved by {}, "
"it may be necessary to fine-tune the "
"expected_avg_time = {}".format(-diff, params.expected_avg_time)
)
assert ok, " !! ERROR !! Regression detected"
assert ok, " !! ERROR !! Regression detected"