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40b69e33e7
Signed-off-by: zhangyue <zhangyue66@baidu.com> Signed-off-by: Roger Wang <hey@rogerw.io> Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Signed-off-by: zhangyue66 <zhangyue66@baidu.com> Co-authored-by: Roger Wang <hey@rogerw.io> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
54 lines
2.2 KiB
Python
54 lines
2.2 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 2025 The Baidu team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only Erine model compatible with HuggingFace weights."""
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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.models.llama import LlamaForCausalLM
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from .utils import PPMissingLayer
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@support_torch_compile(
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# set dynamic_arg_dims to support mrope
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dynamic_arg_dims={
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"input_ids": 0,
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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}
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)
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class Ernie4_5ForCausalLM(LlamaForCausalLM):
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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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# Hack Llama model to fit HF format Ernie4.5 dense implementation
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# Attention difference between Ernie and Llama:
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# 1. rotary_dim and no Neox style.
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# 2. There is no bias for o_proj in attention
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for layer in self.model.layers:
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if not isinstance(layer, PPMissingLayer):
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layer.self_attn.rotary_emb.is_neox_style = False
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layer.self_attn.o_proj.bias = None
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layer.self_attn.o_proj.skip_bias_add = True
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