216 lines
8.2 KiB
Python
216 lines
8.2 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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"""Wrapper around `transformers` models for pooling tasks."""
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import torch
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from transformers import AutoModelForSequenceClassification
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from vllm.attention import Attention, AttentionType
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.pooler import (
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ClassifierPooler,
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CLSPool,
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DispatchPooler,
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Pooler,
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)
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from vllm.sequence import IntermediateTensors
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from .interfaces_base import VllmModelForPooling
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from .transformers import TransformersBase, can_enable_torch_compile
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from .transformers_moe import TransformersMoEBase
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from .utils import WeightsMapper
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class TransformersPoolingBase(TransformersBase, VllmModelForPooling):
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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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# Add `model.` prefix for base model checkpoints
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"": "model.",
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# Remove `model.` prefix if it was already there
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"model.model.": "model.",
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# Classifier/scoring heads will be adjacent to `model`
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"model.score": "classifier",
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"model.classifier": "classifier",
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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 create_attention_instances(
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self, attn_type: AttentionType = AttentionType.DECODER
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) -> dict[int, Attention]:
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# TODO(hmellor): Better way to detect encoder models
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# In encoder models, the attention layers will have `is_causal=False`
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is_encoder = lambda m: not getattr(m, "is_causal", True)
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# vLLM does not support encoder-decoder models, so if any encoder layer
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# is found, we assume the whole model is an encoder model
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if any(is_encoder(m) for m in self.model.modules()):
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attn_type = AttentionType.ENCODER_ONLY
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# Check minimum transformers version for encoder models support
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if attn_type == AttentionType.ENCODER_ONLY:
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self.check_version("4.57.0.dev0", "encoder models support")
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return super().create_attention_instances(attn_type)
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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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@support_torch_compile(enable_if=can_enable_torch_compile)
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class TransformersEmbeddingModel(TransformersPoolingBase):
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default_pooling_type = "CLS"
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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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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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self.pooler = DispatchPooler(
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{
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"token_embed": Pooler.for_token_embed(pooler_config),
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"embed": Pooler.for_embed(pooler_config),
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}
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)
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@support_torch_compile(enable_if=can_enable_torch_compile)
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class TransformersForSequenceClassification(TransformersPoolingBase):
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default_pooling_type = "CLS"
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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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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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# Certain information about the the model and classifier can only be
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# inferred from the `ForSequenceClassification` class. Therefore, we
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# instantiate it on the "meta" device to avoid allocating GPU memory.
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with torch.device("meta"):
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seq_cls_model = AutoModelForSequenceClassification.from_config(
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self.config,
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dtype=self.model_config.dtype,
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trust_remote_code=self.model_config.trust_remote_code,
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)
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# When used for sequence classification, some models have their
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# pooling layers removed. Make sure this is reflected in vLLM.
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for module in seq_cls_model.modules():
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if hasattr(module, "pooler") and module.pooler is None:
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self.model.pooler = None
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break
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if self.model.pooler is not None:
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raise ValueError(
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"Sequence classification models with pooling layers are not "
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"supported yet in the Transformers backend."
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)
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# Unlike `lm_head`, `classifier` is not always `nn.Linear`.
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self.classifier = seq_cls_model.classifier
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self.init_parameters(self.classifier, dtype=self.model_config.head_dtype)
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class ClassifierWithReshape(self.classifier.__class__):
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"""CLSPool has already been applied in `pooling`.
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Add dim to match expected input shape of `classifier.forward`."""
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def forward(self, *args, **kwargs):
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if len(args) > 0:
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args = (args[0].unsqueeze(1), *args[1:])
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return super().forward(*args, **kwargs)
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self.classifier.__class__ = ClassifierWithReshape
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self.pooler = DispatchPooler(
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{
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"token_classify": Pooler.for_token_classify(
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pooler_config, classifier=self.classifier
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),
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"classify": ClassifierPooler(
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pooling=CLSPool(), classifier=self.classifier, act_fn="classify"
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),
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"score": ClassifierPooler(
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pooling=CLSPool(), classifier=self.classifier, act_fn="score"
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),
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}
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)
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@support_torch_compile(enable_if=can_enable_torch_compile)
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class TransformersMoEEmbeddingModel(TransformersMoEBase, TransformersEmbeddingModel):
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pass
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@support_torch_compile(enable_if=can_enable_torch_compile)
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class TransformersMoEForSequenceClassification(
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TransformersMoEBase, TransformersForSequenceClassification
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):
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pass
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