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simplify the returned value from generate_beam_search
> includes cleaning up wrap_device, generate_prompt_perplexity and get_image_asset Signed-off-by: Chukwuma Nwaugha <nwaughac@gmail.com>
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@ -58,8 +58,7 @@ from vllm.distributed import (
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initialize_model_parallel,
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initialize_model_parallel,
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)
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)
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from vllm.logger import init_logger
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from vllm.logger import init_logger
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from vllm.logprobs import Logprob
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from vllm.logprobs import LogprobsOnePosition
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from vllm.multimodal.base import MediaWithBytes
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from vllm.multimodal.utils import fetch_image
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from vllm.multimodal.utils import fetch_image
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from vllm.outputs import RequestOutput
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import BeamSearchParams
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from vllm.sampling_params import BeamSearchParams
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@ -93,8 +92,7 @@ PromptVideoInput = _PromptMultiModalInput[np.ndarray]
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def _read_prompts(filename: str) -> list[str]:
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def _read_prompts(filename: str) -> list[str]:
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with open(filename) as f:
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with open(filename) as f:
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prompts = f.readlines()
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return f.readlines()
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return prompts
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class ImageAssetPrompts(TypedDict):
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class ImageAssetPrompts(TypedDict):
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@ -267,7 +265,7 @@ class HfRunner:
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if isinstance(x, dict):
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if isinstance(x, dict):
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return {k: self.wrap_device(v, device) for k, v in x.items()}
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return {k: self.wrap_device(v, device) for k, v in x.items()}
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if hasattr(x, "device") and x.device.type == device:
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if hasattr(x.device, "type") and x.device.type == device:
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return x
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return x
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return x.to(device)
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return x.to(device)
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@ -993,8 +991,8 @@ class VllmRunner:
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perplexities = []
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perplexities = []
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for output in outputs:
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for output in outputs:
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output = cast(TokensTextLogprobsPromptLogprobs, output)
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assert isinstance(output, TokensTextLogprobsPromptLogprobs)
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token_datas = cast(list[dict[int, Logprob] | None], output[3])
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token_datas = cast(list[LogprobsOnePosition | None], output[3])
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assert token_datas[0] is None
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assert token_datas[0] is None
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token_log_probs = []
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token_log_probs = []
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for token_data in token_datas[1:]:
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for token_data in token_datas[1:]:
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@ -1025,12 +1023,11 @@ class VllmRunner:
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BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens),
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BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens),
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concurrency_limit=concurrency_limit,
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concurrency_limit=concurrency_limit,
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)
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)
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returned_outputs = []
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for output in outputs:
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return [
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token_ids = [x.tokens for x in output.sequences]
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([x.tokens for x in output.sequences], [x.text for x in output.sequences])
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texts = [x.text for x in output.sequences]
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for output in outputs
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returned_outputs.append((token_ids, texts))
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]
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return returned_outputs
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def classify(self, prompts: list[str]) -> list[list[float]]:
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def classify(self, prompts: list[str]) -> list[list[float]]:
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req_outputs = self.llm.classify(prompts)
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req_outputs = self.llm.classify(prompts)
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@ -1405,11 +1402,7 @@ class LocalAssetServer:
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return f"{self.base_url}/{name}"
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return f"{self.base_url}/{name}"
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def get_image_asset(self, name: str) -> Image.Image:
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def get_image_asset(self, name: str) -> Image.Image:
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image = fetch_image(self.url_for(name))
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return fetch_image(self.url_for(name))
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# Unwrap MediaWithBytes if present
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if isinstance(image, MediaWithBytes):
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image = image.media
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return image
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@pytest.fixture(scope="session")
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@pytest.fixture(scope="session")
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