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91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Transformers backend mixin for legacy models."""
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from typing import TYPE_CHECKING
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import torch
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from vllm.model_executor.models.utils import WeightsMapper
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from vllm.sequence import IntermediateTensors
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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class LegacyMixin:
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hf_to_vllm_mapper = WeightsMapper(
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# These are applied in order, so the order matters!
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orig_to_new_prefix={
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# Handle BERT-like models
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"roberta": "model",
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"bert": "model",
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},
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orig_to_new_suffix={
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# Replace legacy suffixes used for norms
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".gamma": ".weight",
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".beta": ".bias",
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},
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)
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def __init__(self, *, vllm_config: "VllmConfig", prefix: str = ""):
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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# Skip unsupported/unwanted output embeddings layers
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self.skip_prefixes.extend(
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[
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"model.lm_head.",
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"model.predictions.",
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"model.qa_outputs.",
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"model.embeddings_project.",
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"model.discriminator_predictions.",
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]
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)
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# Some encoder models have the position_ids buffer in the checkpoint.
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# vLLM will always pass position_ids as an argument, so we skip loading
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# the buffer if it exists
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self.skip_substrs.append("position_ids")
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# Some encoder models have the bias of the final classifier layer
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# in the checkpoint. vLLM does not use this bias, so we skip loading
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# it if it exists
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self.skip_substrs.append("score.bias")
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# roberta-like models an extra padding in positions.
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# FIXME(Isotr0py): This is quite hacky for roberta edge case,
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# we should find a better way to handle this.
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self.is_roberta = "roberta" in self.text_config.model_type
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self.padding_idx = self.text_config.pad_token_id
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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if self.is_roberta:
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# RoBERTa-specific positions padding
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positions += self.padding_idx + 1
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return super().forward(
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input_ids=input_ids,
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positions=positions,
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intermediate_tensors=intermediate_tensors,
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inputs_embeds=inputs_embeds,
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)
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