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[TPU][V1] Enable Top-P (#16843)

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Signed-off-by: NickLucche <nlucches@redhat.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
This commit is contained in:
Nicolò Lucchesi 2025-04-22 02:46:07 +02:00 committed by GitHub
parent 986537f1c3
commit fa3bba2a53
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GPG Key ID: B5690EEEBB952194
2 changed files with 9 additions and 10 deletions
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7
tests/v1/tpu/test_sampler.py
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@ -42,7 +42,7 @@ def test_sampler_different(model_name: str):
sampling_params = SamplingParams(temperature=0.3, seed=42) sampling_params = SamplingParams(temperature=0.3, seed=42)
output2 = llm.generate(prompts, sampling_params) output2 = llm.generate(prompts, sampling_params)
# Batch-case with TopK # Batch-case with TopK/P
for B in [4, 16]: for B in [4, 16]:
p = prompts * B p = prompts * B
sampling_params = [ sampling_params = [
@ -51,9 +51,10 @@ def test_sampler_different(model_name: str):
min_p=0.8, min_p=0.8,
max_tokens=64, max_tokens=64,
# Vary number of ks # Vary number of ks
top_k=random.randint(4, 12)) for _ in range(B) top_k=random.randint(4, 12),
top_p=random.random()) for _ in range(B)
] ]
# Make sure first two reqs have the same K # Make sure first two reqs have the same K/P
sampling_params[0] = sampling_params[1] sampling_params[0] = sampling_params[1]
output = llm.generate(p, sampling_params) output = llm.generate(p, sampling_params)
assert output[0].outputs[0].text == output[1].outputs[0].text assert output[0].outputs[0].text == output[1].outputs[0].text

12
vllm/v1/sample/tpu/metadata.py
View File

@ -11,7 +11,7 @@ DEFAULT_SAMPLING_PARAMS = dict(
min_p=0.0, min_p=0.0,
# strictly disabled for now # strictly disabled for now
top_k=0, top_k=0,
# top_p=0.0, top_p=1.0,
# frequency_penalties=0.0, # frequency_penalties=0.0,
# presence_penalties=0.0, # presence_penalties=0.0,
# repetition_penalties=0.0, # repetition_penalties=0.0,
@ -26,11 +26,9 @@ class TPUSupportedSamplingMetadata:
temperature: torch.Tensor = None temperature: torch.Tensor = None
min_p: torch.Tensor = None min_p: torch.Tensor = None
# Still too slow on forward_native!
top_k: torch.Tensor = None top_k: torch.Tensor = None
top_p: torch.Tensor = None top_p: torch.Tensor = None
# Greedy sampling flag for compiling single xla graph.
all_greedy: bool = True all_greedy: bool = True
# unsupported, you need to return an extra tensor of static size BxV # unsupported, you need to return an extra tensor of static size BxV
@ -103,9 +101,8 @@ class TPUSupportedSamplingMetadata:
DEFAULT_SAMPLING_PARAMS["min_p"]) DEFAULT_SAMPLING_PARAMS["min_p"])
fill_slice(input_batch.top_k_cpu_tensor, fill_slice(input_batch.top_k_cpu_tensor,
DEFAULT_SAMPLING_PARAMS["top_k"]) DEFAULT_SAMPLING_PARAMS["top_k"])
# TODO Temporarily disabled until sampling options are enabled fill_slice(input_batch.top_p_cpu_tensor,
# fill_slice(input_batch.top_p_cpu_tensor, DEFAULT_SAMPLING_PARAMS["top_p"])
# DEFAULT_SAMPLING_PARAMS["top_p"])
# Slice persistent device tensors to a fixed pre-compiled padded shape. # Slice persistent device tensors to a fixed pre-compiled padded shape.
return cls( return cls(
@ -113,7 +110,8 @@ class TPUSupportedSamplingMetadata:
to(xla_device), to(xla_device),
all_greedy=input_batch.all_greedy, all_greedy=input_batch.all_greedy,
# TODO enable more and avoid returning None values # TODO enable more and avoid returning None values
top_p=None, # input_batch.top_p[:padded_num_reqs], top_p=input_batch.top_p_cpu_tensor[:padded_num_reqs].to(
xla_device),
top_k=input_batch.top_k_cpu_tensor[:padded_num_reqs].to( top_k=input_batch.top_k_cpu_tensor[:padded_num_reqs].to(
xla_device), xla_device),
min_p=input_batch.min_p_cpu_tensor[:padded_num_reqs].to( min_p=input_batch.min_p_cpu_tensor[:padded_num_reqs].to(