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sglang/python/sglang/srt/sampling/penaltylib/penalizers/presence_penalty.py

80 lines
2.5 KiB
Python

import typing
import torch
from ..orchestrator import _BatchedPenalizer, _TokenIDs
class BatchedPresencePenalizer(_BatchedPenalizer):
"""
Presence penalizer penalizes tokens based on their presence in the output.
"""
presence_penalties: torch.Tensor = None
cumulated_presence_penalties: torch.Tensor = None
def _is_required(self) -> bool:
return any(
req.sampling_params.presence_penalty != 0.0
for req in self.orchestrator.reqs()
)
def _prepare(self):
self.cumulated_presence_penalties = (
torch.tensor(
data=[0.0 for _ in self.orchestrator.reqs()],
dtype=torch.float32,
device=self.orchestrator.device,
)
.unsqueeze_(1)
.repeat(1, self.orchestrator.vocab_size)
)
self.presence_penalties = (
torch.tensor(
data=[
req.sampling_params.presence_penalty
for req in self.orchestrator.reqs()
],
dtype=torch.float32,
device=self.orchestrator.device,
)
.unsqueeze_(1)
.expand_as(self.cumulated_presence_penalties)
)
def _teardown(self):
del self.presence_penalties
del self.cumulated_presence_penalties
self.presence_penalties = None
self.cumulated_presence_penalties = None
def _cumulate_input_tokens(self, input_ids: _TokenIDs):
pass
def _cumulate_output_tokens(self, output_ids: _TokenIDs):
mask = output_ids.occurrence_count() > 0
self.cumulated_presence_penalties[mask] = self.presence_penalties[mask]
def _apply(self, logits: torch.Tensor) -> torch.Tensor:
logits -= self.cumulated_presence_penalties
return logits
def _filter(
self, indices_to_keep: typing.List[int], indices_tensor_to_keep: torch.Tensor
):
self.presence_penalties = self.presence_penalties[indices_tensor_to_keep]
self.cumulated_presence_penalties = self.cumulated_presence_penalties[
indices_tensor_to_keep
]
def _merge(self, their: "BatchedPresencePenalizer"):
self.presence_penalties = torch.cat(
[self.presence_penalties, their.presence_penalties], dim=0
)
self.cumulated_presence_penalties = torch.cat(
[self.cumulated_presence_penalties, their.cumulated_presence_penalties],
dim=0,
)