Refactor example prompts fixture (#29854)

Signed-off-by: nwaughac@gmail.com

tests/conftest.py

@@ -27,7 +27,7 @@ import threading
 from collections.abc import Generator
 from contextlib import nullcontext
 from enum import Enum
-from typing import Any, Callable, TypedDict, TypeVar, cast
+from typing import Any, Callable, TypedDict, TypeVar, cast, TYPE_CHECKING
 
 import numpy as np
 import pytest
@@ -67,6 +67,11 @@ from vllm.transformers_utils.utils import maybe_model_redirect
 from vllm.utils.collection_utils import is_list_of
 from vllm.utils.torch_utils import set_default_torch_num_threads
 
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+    from transformers.generation.utils import GenerateOutput
+
+
 logger = init_logger(__name__)
 
 _TEST_DIR = os.path.dirname(__file__)
@@ -202,10 +207,7 @@ def dynamo_reset():
 
 @pytest.fixture
 def example_prompts() -> list[str]:
-    prompts = []
-    for filename in _TEST_PROMPTS:
-        prompts += _read_prompts(filename)
-    return prompts
+    return [prompt for filename in _TEST_PROMPTS for prompt in _read_prompts(filename)]
 
 
 @pytest.fixture
@@ -224,10 +226,7 @@ class DecoderPromptType(Enum):
 
 @pytest.fixture
 def example_long_prompts() -> list[str]:
-    prompts = []
-    for filename in _LONG_PROMPTS:
-        prompts += _read_prompts(filename)
-    return prompts
+    return [prompt for filename in _LONG_PROMPTS for prompt in _read_prompts(filename)]
 
 
 @pytest.fixture(scope="session")
@@ -353,10 +352,13 @@ class HfRunner:
                 trust_remote_code=trust_remote_code,
             )
         else:
-            model = auto_cls.from_pretrained(
-                model_name,
-                trust_remote_code=trust_remote_code,
-                **model_kwargs,
+            model = cast(
+                nn.Module,
+                auto_cls.from_pretrained(
+                    model_name,
+                    trust_remote_code=trust_remote_code,
+                    **model_kwargs,
+                ),
             )
 
             # in case some unquantized custom models are not in same dtype
@@ -374,10 +376,12 @@ class HfRunner:
             self.model = model
 
         if not skip_tokenizer_init:
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                model_name,
-                dtype=dtype,
-                trust_remote_code=trust_remote_code,
+            self.tokenizer: "PreTrainedTokenizer | PreTrainedTokenizerFast" = (
+                AutoTokenizer.from_pretrained(
+                    model_name,
+                    dtype=dtype,
+                    trust_remote_code=trust_remote_code,
+                )
             )
 
         # don't put this import at the top level
@@ -495,7 +499,7 @@ class HfRunner:
 
         outputs: list[tuple[list[list[int]], list[str]]] = []
         for inputs in all_inputs:
-            output_ids = self.model.generate(
+            output_ids: torch.Tensor = self.model.generate(
                 **self.wrap_device(inputs),
                 use_cache=True,
                 **kwargs,
@@ -505,8 +509,7 @@ class HfRunner:
                 skip_special_tokens=True,
                 clean_up_tokenization_spaces=False,
             )
-            output_ids = output_ids.cpu().tolist()
-            outputs.append((output_ids, output_str))
+            outputs.append((output_ids.cpu().tolist(), output_str))
         return outputs
 
     def generate_greedy(
@@ -574,7 +577,7 @@ class HfRunner:
 
         all_logprobs: list[list[torch.Tensor]] = []
         for inputs in all_inputs:
-            output = self.model.generate(
+            output: "GenerateOutput" = self.model.generate(
                 **self.wrap_device(inputs),
                 use_cache=True,
                 do_sample=False,
@@ -656,7 +659,7 @@ class HfRunner:
         all_output_strs: list[str] = []
 
         for inputs in all_inputs:
-            output = self.model.generate(
+            output: "GenerateOutput" = self.model.generate(
                 **self.wrap_device(inputs),
                 use_cache=True,
                 do_sample=False,