File size: 2,094 Bytes
ffe291b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | """The ``decision`` pipeline for Decision 2.0 models (``trust_remote_code=True``).
``pipeline("decision", model=repo, trust_remote_code=True)`` loads the model with ``AutoModel`` and answers
``{"state": ..., "questions": {...}}`` requests (or ``state=..., questions=...`` keywords, or a list of
requests) with the model's ``system_one`` response. The model batches the questions of one request itself.
"""
from transformers import Pipeline
_UNSET = object()
class Decision2Pipeline(Pipeline):
_load_tokenizer = False
_load_processor = False
_load_image_processor = False
_load_feature_extractor = False
_load_video_processor = False
def _sanitize_parameters(self, **kwargs):
if kwargs:
raise TypeError(
f"The decision pipeline takes no parameters: {sorted(kwargs)}"
)
return {}, {}, {}
def __call__(self, inputs=None, *, state=_UNSET, questions=_UNSET, **kwargs):
if state is not _UNSET or questions is not _UNSET:
if inputs is not None:
raise TypeError("Pass one request, or state= and questions=")
inputs = {
"state": None if state is _UNSET else state,
"questions": None if questions is _UNSET else questions,
}
if kwargs.get("batch_size") not in (None, 1):
raise ValueError(
"The decision pipeline runs one request at a time (batch_size=1)"
)
return super().__call__(inputs, **kwargs)
def preprocess(self, inputs):
if not isinstance(inputs, dict) or set(inputs) != {"state", "questions"}:
raise ValueError(
'A decision request is {"state": ..., "questions": {<id>: <question>, ...}}'
)
return {"state": inputs["state"], "questions": inputs["questions"]}
def _forward(self, model_inputs):
return self.model.system_one(
state=model_inputs["state"], questions=model_inputs["questions"]
)
def postprocess(self, model_outputs):
return model_outputs
|