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vllm-project--vllm/tests/v1/spec_decode/test_llm_base_proposer_sampling.py
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chore: import upstream snapshot with attribution
2026-07-13 12:55:37 +08:00

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Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import torch
from vllm.platforms import current_platform
from vllm.utils.torch_utils import set_random_seed
from vllm.v1.sample.logits_processor import LogitsProcessors
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.llm_base_proposer import (
compute_probs_and_sample_next_token,
)
DEVICE_TYPE = current_platform.device_type
def _seed_default_generator(seed: int) -> None:
set_random_seed(seed)
def _make_sampling_metadata(batch_size: int) -> SamplingMetadata:
return SamplingMetadata(
temperature=torch.ones(batch_size, dtype=torch.float32, device=DEVICE_TYPE),
all_greedy=False,
all_random=True,
top_p=None,
top_k=None,
generators={},
max_num_logprobs=None,
no_penalties=True,
prompt_token_ids=None,
frequency_penalties=torch.empty(0, device=DEVICE_TYPE),
presence_penalties=torch.empty(0, device=DEVICE_TYPE),
repetition_penalties=torch.empty(0, device=DEVICE_TYPE),
output_token_ids=[[] for _ in range(batch_size)],
spec_token_ids=[[] for _ in range(batch_size)],
allowed_token_ids_mask=None,
bad_words_token_ids={},
logitsprocs=LogitsProcessors(),
)
def test_compute_probs_and_sample_next_token_uses_fp64_exponential_race():
batch_size = 4
vocab_size = 32
generator = torch.Generator(device=DEVICE_TYPE).manual_seed(11)
logits = torch.randn(
batch_size,
vocab_size,
dtype=torch.float32,
device=DEVICE_TYPE,
generator=generator,
)
metadata = _make_sampling_metadata(batch_size)
_seed_default_generator(12345)
probs = logits.softmax(dim=-1, dtype=torch.float32)
q = torch.empty(probs.shape, dtype=torch.float64, device=probs.device)
q.exponential_()
expected_ids = q.reciprocal_().mul_(probs).argmax(dim=-1).view(-1)
_seed_default_generator(12345)
actual_ids, actual_probs = compute_probs_and_sample_next_token(
logits.clone(),
metadata,
use_fp64_gumbel=True,
)
assert torch.equal(actual_ids, expected_ids)
assert torch.allclose(actual_probs, probs)