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Update deprecated Python 3.8 typing (#13971)

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This commit is contained in:
Harry Mellor
2025-03-03 01:34:51 +00:00
committed by GitHub
parent bf33700ecd
commit cf069aa8aa
300 changed files with 2294 additions and 2347 deletions
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9
tests/spec_decode/e2e/conftest.py
View File

@@ -1,7 +1,8 @@
# SPDX-License-Identifier: Apache-2.0
from collections.abc import Sequence
from itertools import cycle
from typing import List, Optional, Sequence, Tuple, Union
from typing import Optional, Union
import pytest
import torch
@@ -64,9 +65,9 @@ def maybe_assert_ngram_worker(llm):
def get_output_from_llm_generator(
llm_generator, prompts,
sampling_params) -> Tuple[List[str], List[List[int]], float]:
tokens: List[str] = []
token_ids: List[List[int]] = []
sampling_params) -> tuple[list[str], list[list[int]], float]:
tokens: list[str] = []
token_ids: list[list[int]] = []
acceptance_rate: float = -1.0
for llm in llm_generator():
maybe_assert_ngram_worker(llm)

4
tests/spec_decode/test_batch_expansion.py
View File

@@ -1,7 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
from typing import List
import pytest
import torch
@@ -42,7 +40,7 @@ def test_get_token_ids_to_score(k: int):
device='cuda',
)
expected_output: List[List[int]] = [
expected_output: list[list[int]] = [
[],
]
for i in range(proposal_token_ids.shape[0]):

15
tests/spec_decode/test_multi_step_worker.py
View File

@@ -1,7 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
import random
from typing import Dict, List
from unittest.mock import MagicMock
import pytest
@@ -221,7 +220,7 @@ def test_same_output_for_multi_step():
# Run single-step repeatedly.
zero_kv_cache(worker.cache_engine)
single_step_output: List[SamplerOutput] = []
single_step_output: list[SamplerOutput] = []
continuations = [[1] for _ in prompts]
set_random_seed(seed)
@@ -243,15 +242,15 @@ def test_same_output_for_multi_step():
continuations[i].append(seq_group_output.samples[0].output_token)
# Get token ids and logprobs for comparison.
multi_step_output_logprobs: List[List[Dict[int,
multi_step_output_logprobs: list[list[dict[int,
Logprob]]] = [[]
for _ in prompts]
single_step_output_logprobs: List[List[Dict[int,
single_step_output_logprobs: list[list[dict[int,
Logprob]]] = [[]
for _ in prompts]
multi_step_output_token_ids: List[List[int]] = [[] for _ in prompts]
single_step_output_token_ids: List[List[int]] = [[] for _ in prompts]
multi_step_output_token_ids: list[list[int]] = [[] for _ in prompts]
single_step_output_token_ids: list[list[int]] = [[] for _ in prompts]
for i, _ in enumerate(prompts):
for multi_step, single_step in zip(multi_step_output,
single_step_output):
@@ -336,7 +335,7 @@ def test_multi_step_with_batch_expansion_correct_output():
# will simulate the bonus token case with the second token
# being the bonus token.
zero_kv_cache(worker.cache_engine)
single_step_output: List[SamplerOutput] = []
single_step_output: list[SamplerOutput] = []
set_random_seed(seed)
for _ in range(num_steps):
seq_group_metadata_list = create_seq_group_metadata_from_prompts(
@@ -430,7 +429,7 @@ def test_multi_step_with_batch_expansion_incorrect_output():
# will simulate the bonus token case with the second token
# being the bonus token.
zero_kv_cache(worker.cache_engine)
single_step_output: List[SamplerOutput] = []
single_step_output: list[SamplerOutput] = []
set_random_seed(seed)
for _ in range(num_steps):
seq_group_metadata_list = create_seq_group_metadata_from_prompts(

3
tests/spec_decode/test_scorer.py
View File

@@ -1,7 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
import random
from typing import List
import pytest
import torch
@@ -15,7 +14,7 @@ from vllm.worker.worker import Worker
from .utils import create_batch, create_worker
def create_proposal(propose_lens: List[int], vocab_size: int,
def create_proposal(propose_lens: list[int], vocab_size: int,
device: str) -> SpeculativeProposals:
batch_size = len(propose_lens)
max_propose_len = max(propose_lens)

11
tests/spec_decode/test_spec_decode_worker.py
View File

@@ -3,7 +3,6 @@
import random
from collections import defaultdict
from types import SimpleNamespace
from typing import Dict, List, Set
from unittest.mock import MagicMock
import pytest
@@ -123,7 +122,7 @@ def test_batch_expansion_correctly_calls_target_model(
seq_group_metadata_list=seq_group_metadata_list,
num_lookahead_slots=k))
seen_contexts: List[List[int]] = []
seen_contexts: list[list[int]] = []
call_args_list = target_worker.execute_model.call_args_list
assert len(call_args_list) == 1
@@ -136,7 +135,7 @@ def test_batch_expansion_correctly_calls_target_model(
for seq_data in seq_group_metadata.seq_data.values():
seen_contexts.append(seq_data.get_token_ids())
expected_seen_contexts: List[List[int]] = []
expected_seen_contexts: list[list[int]] = []
for prompt, prev_generated, draft_tokens in zip(
prompts, prev_output_tokens, proposal_token_ids.tolist()):
@@ -338,11 +337,11 @@ def test_correctly_formats_output(k: int, batch_size: int,
next(iter(seq_group_metadata.seq_data.keys()))
for seq_group_metadata in seq_group_metadata_list
]
actual_output_by_seq: Dict[int, List[SequenceOutput]] = {
actual_output_by_seq: dict[int, list[SequenceOutput]] = {
seq_id: []
for seq_id in seq_ids
}
expected_output_by_seq: Dict[int, List[SequenceOutput]] = {
expected_output_by_seq: dict[int, list[SequenceOutput]] = {
seq_id: []
for seq_id in seq_ids
}
@@ -728,7 +727,7 @@ def test_populate_seq_ids_with_bonus_tokens():
size=(batch_size, (k + 1)),
dtype=torch.int64,
device='cuda')
expected_request_id_seq_ids_mapping: Dict[str, Set[int]] = defaultdict(set)
expected_request_id_seq_ids_mapping: dict[str, set[int]] = defaultdict(set)
for seq_group_metadata in seq_group_metadata_list:
for seq_id in seq_group_metadata.seq_data:
expected_request_id_seq_ids_mapping[

33
tests/spec_decode/utils.py
View File

@@ -1,9 +1,8 @@
# SPDX-License-Identifier: Apache-2.0
from collections.abc import Sequence as GenericSequence
from itertools import count
from typing import Callable, Dict, List, Optional
from typing import Sequence as GenericSequence
from typing import TypeVar, Union
from typing import Callable, Optional, TypeVar, Union
from unittest.mock import MagicMock
import torch
@@ -44,7 +43,7 @@ def mock_worker(cls=None,
return worker
def patch_execute_model_with_seeds(worker: Worker, rand_seeds: List[int]):
def patch_execute_model_with_seeds(worker: Worker, rand_seeds: list[int]):
seed_iter = iter(rand_seeds)
original_execute_model = worker.execute_model
@@ -56,7 +55,7 @@ def patch_execute_model_with_seeds(worker: Worker, rand_seeds: List[int]):
return new_execute_model
def zero_kv_cache(cache_engine: List[CacheEngine]):
def zero_kv_cache(cache_engine: list[CacheEngine]):
assert cache_engine[0].gpu_cache
for key_blocks, value_blocks in cache_engine[0].gpu_cache:
key_blocks.zero_()
@@ -106,13 +105,13 @@ def create_worker(cls: Callable[..., T],
def create_seq_group_metadata_from_prompts(
prompts: List[List[int]],
prompts: list[list[int]],
num_gpu_blocks: int,
block_size: int,
final_prompt_lens: List[int],
continuations: Optional[List[List[int]]] = None,
seq_ids: Optional[List[int]] = None,
) -> List[SequenceGroupMetadata]:
final_prompt_lens: list[int],
continuations: Optional[list[list[int]]] = None,
seq_ids: Optional[list[int]] = None,
) -> list[SequenceGroupMetadata]:
if continuations is None:
continuations = [[] for _ in prompts]
@@ -149,11 +148,11 @@ def create_seq_group_metadata_from_prompts(
def create_chunked_seq_group_metadata_from_prompt(
prompt: List[int],
prompt: list[int],
num_gpu_blocks: int,
chunk_size: int,
block_size: int,
seq_id: Optional[int] = None) -> List[SequenceGroupMetadata]:
seq_id: Optional[int] = None) -> list[SequenceGroupMetadata]:
if seq_id is None:
seq_id = 0
@@ -184,8 +183,8 @@ def create_chunked_seq_group_metadata_from_prompt(
def assert_logprobs_dict_allclose(
actual_logprobs: List[Dict[int, Logprob]],
expected_logprobs: List[Dict[int, Logprob]]) -> None:
actual_logprobs: list[dict[int, Logprob]],
expected_logprobs: list[dict[int, Logprob]]) -> None:
for single_step_actual_logprobs, single_step_expected_logprobs in zip(
actual_logprobs, expected_logprobs):
assert set(single_step_actual_logprobs.keys()) == set(
@@ -202,7 +201,7 @@ def create_sampler_output_list(
token_ids: torch.Tensor,
probs: GenericSequence[Optional[torch.Tensor]],
logprobs: GenericSequence[Optional[torch.Tensor]],
seq_ids: Optional[List[int]] = None) -> List[SamplerOutput]:
seq_ids: Optional[list[int]] = None) -> list[SamplerOutput]:
num_steps, batch_size = token_ids.shape
token_ids_by_step = token_ids.tolist()
@@ -231,9 +230,9 @@ def create_sampler_output_list(
def create_batch(batch_size,
k,
prompt_len: Union[int, List[int]] = 10,
prompt_len: Union[int, list[int]] = 10,
prev_output_token_len: int = 10,
seq_ids: Optional[List[int]] = None,
seq_ids: Optional[list[int]] = None,
num_gpu_blocks: Optional[int] = None,
block_size: Optional[int] = None,
prefill_chunk_size: Optional[int] = None):