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polish code with new pre-commit rule (#2923)

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Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
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50
test/layers/test_sampler.py
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@@ -21,26 +21,19 @@ from fastdeploy.model_executor.layers.sample.sampler import Sampler
def _create_fake_logits(batch_size: int, vocab_size: int) -> paddle.Tensor:
fake_logits = paddle.full(shape=[batch_size, vocab_size],
fill_value=1e-2,
dtype="float32")
fake_logits = paddle.full(shape=[batch_size, vocab_size], fill_value=1e-2, dtype="float32")
return fake_logits
def _create_penalty_tensor(batch_size: int,
penalty_value: float) -> paddle.Tensor:
return paddle.full(shape=[batch_size, 1],
fill_value=penalty_value,
dtype="float32")
def _create_penalty_tensor(batch_size: int, penalty_value: float) -> paddle.Tensor:
return paddle.full(shape=[batch_size, 1], fill_value=penalty_value, dtype="float32")
def _create_tokens_tensor(
batch_size: int,
max_seq_len: int,
) -> paddle.Tensor:
pre_token_ids = paddle.full(shape=[batch_size, max_seq_len],
fill_value=-1,
dtype="int64")
pre_token_ids = paddle.full(shape=[batch_size, max_seq_len], fill_value=-1, dtype="int64")
return pre_token_ids
@@ -51,34 +44,18 @@ def _create_default_sampling_metadata(
) -> SamplingMetadata:
fake_sampling_metadata = SamplingMetadata(
temperature=paddle.full(shape=[batch_size, 1],
fill_value=0.9,
dtype="float32"),
top_p=paddle.full(shape=[batch_size, 1],
fill_value=0.7,
dtype="float32"),
prompt_ids=paddle.full(shape=[batch_size, max_seq_len],
fill_value=0,
dtype="int64"),
prompt_lens=paddle.full(shape=[batch_size, 1],
fill_value=5,
dtype="int64"),
step_idx=paddle.full(shape=[batch_size, 1],
fill_value=0,
dtype="int64"),
temperature=paddle.full(shape=[batch_size, 1], fill_value=0.9, dtype="float32"),
top_p=paddle.full(shape=[batch_size, 1], fill_value=0.7, dtype="float32"),
prompt_ids=paddle.full(shape=[batch_size, max_seq_len], fill_value=0, dtype="int64"),
prompt_lens=paddle.full(shape=[batch_size, 1], fill_value=5, dtype="int64"),
step_idx=paddle.full(shape=[batch_size, 1], fill_value=0, dtype="int64"),
pre_token_ids=_create_tokens_tensor(batch_size, max_seq_len),
frequency_penalties=_create_penalty_tensor(batch_size, 0.0),
presence_penalties=_create_penalty_tensor(batch_size, 0.0),
repetition_penalties=_create_penalty_tensor(batch_size, 1.0),
min_dec_lens=paddle.full(shape=[batch_size, 1],
fill_value=min_seq_len,
dtype="int64"),
bad_words_token_ids=paddle.full(shape=[batch_size],
fill_value=-1,
dtype="int64"),
eos_token_ids=paddle.full(shape=[batch_size],
fill_value=-2,
dtype="int64"),
min_dec_lens=paddle.full(shape=[batch_size, 1], fill_value=min_seq_len, dtype="int64"),
bad_words_token_ids=paddle.full(shape=[batch_size], fill_value=-1, dtype="int64"),
eos_token_ids=paddle.full(shape=[batch_size], fill_value=-2, dtype="int64"),
)
return fake_sampling_metadata
@@ -91,8 +68,7 @@ def test_sampler():
sampler = Sampler()
logits = _create_fake_logits(batch_size, vocab_size)
sampling_metadata = _create_default_sampling_metadata(
batch_size, min_seq_len, max_seq_len)
sampling_metadata = _create_default_sampling_metadata(batch_size, min_seq_len, max_seq_len)
next_tokens = sampler(logits, sampling_metadata)
print(next_tokens)