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[Bugfix] Fix LoRA test (#18518)

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Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
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Jee Jee Li 2025-05-22 12:48:53 +08:00 committed by GitHub
parent 51797775c3
commit db5a29ba19
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2 changed files with 66 additions and 58 deletions
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2
tests/lora/test_lora_functions.py
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@ -69,7 +69,7 @@ def test_lora_functions_sync():
run_check(llm.add_lora, make_lora_request(12), [12, 9, 10, 11])
run_check(llm.add_lora, make_lora_request(13), [12, 13, 10, 11])
# Remove all LoRAs
# Remove all LoRAs.
run_check(llm.remove_lora, 13, [12, 10, 11])
run_check(llm.remove_lora, 12, [10, 11])
run_check(llm.remove_lora, 11, [10])

122
tests/v1/sample/test_topk_topp_sampler.py
View File

@ -16,31 +16,40 @@ VOCAB_SIZE = 128 * 1024
FLASHINFER_ENABLED = current_platform.is_cuda() and is_flashinfer_available
@pytest.fixture(autouse=True)
def reset_default_device():
"""
Explicitly set the default device, which can affect subsequent tests.
Adding this fixture helps avoid this problem.
"""
original_device = torch.get_default_device()
yield
torch.set_default_device(original_device)
def test_topk_impl_equivalance():
with torch.device(DEVICE):
generator = Generator(device=DEVICE).manual_seed(33)
torch.set_default_device(DEVICE)
generator = Generator(device=DEVICE).manual_seed(33)
logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)
logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)
# Random top-k values between 1 and 9.
k = torch.randint(1, 10, (BATCH_SIZE, ), generator=generator)
# Random top-k values between 1 and 9.
k = torch.randint(1, 10, (BATCH_SIZE, ), generator=generator)
# Set k=vocab_size for ~50% of requests in the batch (top-k disabled).
k.masked_fill_(
torch.randint(0,
2, (BATCH_SIZE, ),
generator=generator,
dtype=bool), VOCAB_SIZE)
# Set k=vocab_size for ~50% of requests in the batch (top-k disabled).
k.masked_fill_(
torch.randint(0, 2, (BATCH_SIZE, ), generator=generator, dtype=bool),
VOCAB_SIZE)
# Top-k only implementation
result1 = apply_top_k_top_p(logits=logits.clone(), k=k, p=None)
# Top-k only implementation
result1 = apply_top_k_top_p(logits=logits.clone(), k=k, p=None)
# Top-p + top-k
no_op_top_p = torch.tensor([1.0])
result2 = apply_top_k_top_p(logits=logits.clone(), k=k, p=no_op_top_p)
# Top-p + top-k
no_op_top_p = torch.tensor([1.0])
result2 = apply_top_k_top_p(logits=logits.clone(), k=k, p=no_op_top_p)
assert torch.allclose(result1, result2)
assert torch.allclose(result1, result2)
def test_flashinfer_sampler():
@ -58,50 +67,49 @@ def test_flashinfer_sampler():
pytest.skip(
"FlashInfer not installed or not available on this platform.")
with torch.device(DEVICE):
generator = Generator(device=DEVICE).manual_seed(42)
torch.set_default_device(DEVICE)
generator = Generator(device=DEVICE).manual_seed(42)
# Generate random logits
logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)
# Generate random logits
logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)
# Generate various top-k and top-p values
k_values = torch.randint(1, 1000, (BATCH_SIZE, ), generator=generator)
p_values = torch.rand(
(BATCH_SIZE, ),
generator=generator) * 0.5 + 0.5 # range in [0.5, 1.0]
# Generate various top-k and top-p values
k_values = torch.randint(1, 1000, (BATCH_SIZE, ), generator=generator)
p_values = torch.rand(
(BATCH_SIZE, ), generator=generator) * 0.5 + 0.5 # range in [0.5, 1.0]
# Sometimes disable top-k (k=vocab_size)
k_values.masked_fill_(
torch.randint(0,
2, (BATCH_SIZE, ),
generator=generator,
dtype=torch.bool), VOCAB_SIZE)
# Sometimes disable top-k (k=vocab_size)
k_values.masked_fill_(
torch.randint(0,
2, (BATCH_SIZE, ),
generator=generator,
dtype=torch.bool), VOCAB_SIZE)
# Sometimes disable top-p (p=1.0)
p_values.masked_fill_(
torch.randint(0,
2, (BATCH_SIZE, ),
generator=generator,
dtype=torch.bool), 1.0)
# Sometimes disable top-p (p=1.0)
p_values.masked_fill_(
torch.randint(0,
2, (BATCH_SIZE, ),
generator=generator,
dtype=torch.bool), 1.0)
python_logits = apply_top_k_top_p(
logits=logits.clone(),
k=k_values,
p=p_values,
)
python_probs = torch.softmax(python_logits, dim=-1)
python_logits = apply_top_k_top_p(
logits=logits.clone(),
k=k_values,
p=p_values,
)
python_probs = torch.softmax(python_logits, dim=-1)
# FlashInfer only exposed renorm interfaces for probs so convert first
flashinfer_probs = torch.softmax(logits.clone(), dim=-1)
flashinfer_probs = top_k_renorm_probs(
probs=flashinfer_probs,
top_k=k_values,
)
flashinfer_probs = top_p_renorm_probs(
probs=flashinfer_probs,
top_p=p_values,
)
# FlashInfer only exposed renorm interfaces for probs so convert first
flashinfer_probs = torch.softmax(logits.clone(), dim=-1)
flashinfer_probs = top_k_renorm_probs(
probs=flashinfer_probs,
top_k=k_values,
)
flashinfer_probs = top_p_renorm_probs(
probs=flashinfer_probs,
top_p=p_values,
)
# Compare the results
assert torch.allclose(python_probs, flashinfer_probs, atol=2e-2), \
"FlashInfer and Python sampling implementations do not match!"
# Compare the results
assert torch.allclose(python_probs, flashinfer_probs, atol=2e-2), \
"FlashInfer and Python sampling implementations do not match!"