Taneem IbrahimandHarry Mellor
e960ead0aa
Signed-off-by: Taneem Ibrahim <taneem.ibrahim@gmail.com> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
# Copyright 2024 The vLLM team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Transformers modeling backend mixin for legacy models."""
|
|
|
|
from typing import TYPE_CHECKING
|
|
|
|
import torch
|
|
|
|
from vllm.model_executor.models.utils import WeightsMapper
|
|
from vllm.sequence import IntermediateTensors
|
|
|
|
from .base import Base
|
|
|
|
if TYPE_CHECKING:
|
|
from vllm.config import VllmConfig
|
|
|
|
|
|
class LegacyMixin(Base):
|
|
def __init__(self, *, vllm_config: "VllmConfig", prefix: str = ""):
|
|
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
self.hf_to_vllm_mapper |= WeightsMapper(
|
|
# Drop unsupported/unwanted output embeddings layers.
|
|
orig_to_new_prefix={
|
|
"model.lm_head.": None,
|
|
"model.predictions.": None,
|
|
"model.qa_outputs.": None,
|
|
"model.embeddings_project.": None,
|
|
"model.discriminator_predictions.": None,
|
|
},
|
|
orig_to_new_substr={
|
|
# Some encoder models have the position_ids buffer in the checkpoint.
|
|
# vLLM always passes position_ids as an argument, so drop the buffer.
|
|
"position_ids": None,
|
|
# Some encoder models have the bias of the final classifier layer in
|
|
# the checkpoint. vLLM does not use this bias, so drop it.
|
|
"score.bias": None,
|
|
},
|
|
)
|
|
|
|
# roberta-like models an extra padding in positions.
|
|
# FIXME(Isotr0py): This is quite hacky for roberta edge case,
|
|
# we should find a better way to handle this.
|
|
self.is_roberta = "roberta" in self.text_config.model_type
|
|
self.padding_idx = self.text_config.pad_token_id
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
if self.is_roberta:
|
|
# RoBERTa positions start at padding_idx + 1.
|
|
# Non-in-place add to avoid mutating the persistent GPU buffer --
|
|
# in-place += would accumulate on CUDA graph padding slots.
|
|
positions = positions + self.padding_idx + 1
|
|
return super().forward(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
**kwargs,
|
|
)
|