vllm/cacheflow/outputs.py
2023-05-20 13:06:59 -07:00

80 lines
2.7 KiB
Python

from typing import Dict, List, Union
from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
from cacheflow.sequence import SequenceGroup
class CompletionOutput:
def __init__(
self,
text: str,
token_ids: List[int],
cumulative_logprobs: float,
logprobs: List[Dict[int, float]],
) -> None:
self.text = text
self.token_ids = token_ids
self.cumulative_logprobs = cumulative_logprobs
self.logprobs = logprobs
def __repr__(self) -> str:
return (f"CompletionOutput(output={self.text!r}, "
f"token_ids={self.token_ids}, "
f"cumulative_logprobs={self.cumulative_logprobs}, "
f"logprobs={self.logprobs})")
class RequestOutput:
def __init__(
self,
request_id: int,
prompt: str,
prompt_token_ids: List[int],
outputs: List[CompletionOutput],
done: bool = False,
) -> None:
self.request_id = request_id
self.prompt = prompt
self.prompt_token_ids = prompt_token_ids
self.outputs = outputs
self.done = done
@staticmethod
def from_seq_group(
seq_group: SequenceGroup,
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
) -> "RequestOutput":
outputs: List[CompletionOutput] = []
seqs = seq_group.get_seqs()
for seq in seqs:
output_token_ids = seq.data.output_token_ids
output_str = tokenizer.decode(output_token_ids,
skip_special_tokens=True)
seq_logprobs = seq.data.cumulative_logprobs
logprobs = seq.output_logprobs
if seq_group.sampling_params.logprobs == 0:
# NOTE: We need to take care of this case because the sequence
# always has the logprobs of the sampled tokens even if the
# logprobs are not requested.
logprobs = {}
output = CompletionOutput(output_str, output_token_ids,
seq_logprobs, logprobs)
outputs.append(output)
# Every sequence in the sequence group should have the same prompt.
prompt = seqs[0].prompt
prompt_token_ids = seqs[0].data.prompt_token_ids
return RequestOutput(seq_group.request_id, prompt, prompt_token_ids,
outputs, seq_group.is_finished())
def __repr__(self) -> str:
return (f"RequestOutput(request_id={self.request_id}, "
f"prompt={self.prompt!r}, "
f"prompt_token_ids={self.prompt_token_ids}, "
f"outputs={self.outputs}, "
f"done={self.done})")