[Tests] conftest: Extending VllmRunner and HfRunner to accept token_ids as input (#26295)

Signed-off-by: Yannick Schnider <[email protected]>
Signed-off-by: Yannick Schnider <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Author: yannicks1

tests/conftest.py

@@ -57,7 +57,7 @@
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
 from vllm.transformers_utils.utils import maybe_model_redirect
-from vllm.utils import set_default_torch_num_threads
+from vllm.utils import is_list_of, set_default_torch_num_threads
 
 logger = init_logger(__name__)
 
@@ -406,11 +406,11 @@ def _init(
 
     def get_inputs(
         self,
-        prompts: list[str],
+        prompts: Union[list[str], list[list[int]]],
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,
         audios: Optional[PromptAudioInput] = None,
-    ) -> list[Union[BatchFeature, BatchEncoding]]:
+    ) -> list[Union[BatchFeature, BatchEncoding, dict[str, torch.Tensor]]]:
         if images is not None:
             assert len(prompts) == len(images)
 
@@ -420,31 +420,48 @@ def get_inputs(
         if audios is not None:
             assert len(prompts) == len(audios)
 
-        all_inputs: list[Union[BatchFeature, BatchEncoding]] = []
+        all_inputs: list[
+            Union[BatchFeature, BatchEncoding, dict[str, torch.Tensor]]
+        ] = []
         for i, prompt in enumerate(prompts):
-            processor_kwargs: dict[str, Any] = {
-                "text": prompt,
-                "return_tensors": "pt",
-            }
-            if images is not None and (image := images[i]) is not None:
-                processor_kwargs["images"] = image
-            if videos is not None and (video := videos[i]) is not None:
-                processor_kwargs["videos"] = video
-            if audios is not None and (audio_inputs := audios[i]) is not None:
-                # HACK - not all processors take sampling_rate; we should
-                # clean this up in the future.
-                if len(audio_inputs) == 2:
-                    audio, sr = audio_inputs
-                    processor_kwargs["audio"] = audio
-                    processor_kwargs["sampling_rate"] = sr
-                else:
-                    processor_kwargs["audio"] = audio_inputs
-
-            inputs = self.processor(**processor_kwargs)
-            if isinstance(inputs, BatchFeature):
-                inputs = inputs.to(dtype=self.dtype)
-
-            all_inputs.append(inputs)
+            if isinstance(prompt, str):
+                processor_kwargs: dict[str, Any] = {
+                    "text": prompt,
+                    "return_tensors": "pt",
+                }
+                if images is not None and (image := images[i]) is not None:
+                    processor_kwargs["images"] = image
+                if videos is not None and (video := videos[i]) is not None:
+                    processor_kwargs["videos"] = video
+                if audios is not None and (audio_inputs := audios[i]) is not None:
+                    # HACK - not all processors take sampling_rate; we should
+                    # clean this up in the future.
+                    if len(audio_inputs) == 2:
+                        audio, sr = audio_inputs
+                        processor_kwargs["audio"] = audio
+                        processor_kwargs["sampling_rate"] = sr
+                    else:
+                        processor_kwargs["audio"] = audio_inputs
+
+                inputs = self.processor(**processor_kwargs)
+                if isinstance(inputs, BatchFeature):
+                    inputs = inputs.to(dtype=self.dtype)
+                all_inputs.append(inputs)
+            else:
+                # check that prompt is (batched) list of integers (token ids)
+                if not is_list_of(prompt, typ=int, check="all"):
+                    raise ValueError(
+                        "Prompt must be a list of ints corresponding to the prompt token ids."
+                    )
+                # check that no multimodal input is provided
+                if images or videos or audios:
+                    raise ValueError(
+                        "When providing prompt token ids multimodal inputs are not supported."
+                    )
+                input_dict = {
+                    "input_ids": torch.tensor(prompt, dtype=torch.long).unsqueeze(0),
+                }
+                all_inputs.append(input_dict)
 
         return all_inputs
 
@@ -477,7 +494,7 @@ def classify(self, prompts: list[str]) -> list[str]:
 
     def generate(
         self,
-        prompts: list[str],
+        prompts: Union[list[str], list[list[int]]],
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,
         audios: Optional[PromptAudioInput] = None,
@@ -505,7 +522,7 @@ def generate(
 
     def generate_greedy(
         self,
-        prompts: list[str],
+        prompts: Union[list[str], list[list[int]]],
         max_tokens: int,
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,
@@ -807,7 +824,7 @@ def get_inputs(
 
     def generate(
         self,
-        prompts: Union[list[str], list[torch.Tensor]],
+        prompts: Union[list[str], list[torch.Tensor], list[list[int]]],
         sampling_params: SamplingParams,
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,
@@ -877,7 +894,7 @@ def generate_w_logprobs(
 
     def generate_greedy(
         self,
-        prompts: Union[list[str], list[torch.Tensor]],
+        prompts: Union[list[str], list[torch.Tensor], list[list[int]]],
         max_tokens: int,
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,

tests/v1/e2e/test_context_length.py

@@ -23,13 +23,10 @@
 """
 
 import pytest
-import torch
-from transformers import AutoModelForCausalLM
 
+from tests.conftest import HfRunner, VllmRunner
 from tests.models.utils import check_outputs_equal
 from tests.utils import create_new_process_for_each_test
-from vllm import LLM, SamplingParams
-from vllm.inputs import TokensPrompt
 
 
 @create_new_process_for_each_test()
@@ -43,6 +40,8 @@
 )
 def test_max_context_length(
     model: str,
+    vllm_runner: type[VllmRunner],
+    hf_runner: type[HfRunner],
     prompt_len: int,
     max_tokens: int,
 ) -> None:
@@ -57,42 +56,26 @@ def test_max_context_length(
     # Construct a prompt of size prompt_len
     prompt_ids = [[43] * prompt_len]
 
-    # Generate max_tokens new tokens deterministically.
-    sampling_params = [
-        SamplingParams(max_tokens=max_tokens, temperature=0.0, ignore_eos=True)
-    ]
-
     # --- vLLM generation ---
-    llm = LLM(
-        model=model,
-        tokenizer=model,
+    with vllm_runner(
+        model_name=model,
+        tokenizer_name=model,
         max_model_len=2048,
         max_num_seqs=1,
         tensor_parallel_size=1,
-    )
-
-    vllm_token_prompts = [TokensPrompt(prompt_token_ids=prompt_ids[0])]
-    vllm_results = llm.generate(vllm_token_prompts, sampling_params)
-
-    vllm_output_ids = vllm_results[0].outputs[0].token_ids
-
-    # --- HuggingFace generation ---
-    with torch.no_grad():
-        hf_model = AutoModelForCausalLM.from_pretrained(model)
-
-        # HF expects a tensor of input ids shaped (batch, seq_len).
-        hf_input_tokens = torch.tensor(prompt_ids[0]).unsqueeze(0)
-
+    ) as vllm_model:
         # Generate max_tokens new tokens deterministically.
-        hf_generated = hf_model.generate(
-            hf_input_tokens,
-            do_sample=False,
-            min_new_tokens=max_tokens,
-            max_new_tokens=max_tokens,
-        )
+        vllm_outputs = vllm_model.generate_greedy(prompt_ids, max_tokens)
 
-        # HF returns the prompt + generated tokens. Slice off the prompt.
-        hf_output_ids = hf_generated.cpu().tolist()[0][len(prompt_ids[0]) :]
+    # --- HuggingFace generation ---
+    with hf_runner(
+        model_name=model,
+    ) as hf_model:
+        hf_outputs = hf_model.generate_greedy(prompt_ids, max_tokens)
+
+    # vLLM and HF runners return prompt + generated tokens. Slice off the prompt.
+    vllm_output_ids = vllm_outputs[0][0][prompt_len:]
+    hf_output_ids = hf_outputs[0][0][prompt_len:]
 
     # check that exactly max_tokens tokens were generated with vLLM and HF
     assert len(vllm_output_ids) == len(hf_output_ids) == max_tokens