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79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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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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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.models.llama import (
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaModel,
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)
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class TeleFLMModel(LlamaModel):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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layer_type: type[nn.Module] = LlamaDecoderLayer,
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):
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super().__init__(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)
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"""
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This implementation is based on the µScaling paper presented at
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the ICLR 2025 Workshop:
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NanoLM: An Affordable LLM Study Benchmark \
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via Accurate Loss Prediction across Scales
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by Yiqun Yao et al.
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Available at: https://openreview.net/forum?id=IwaPYg1SCA
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arXiv preprint: https://arxiv.org/abs/2304.06875
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"""
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self.use_mup = self.config.use_mup
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if self.use_mup:
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self.input_mult = self.config.input_mult
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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embedding = self.embed_tokens(input_ids)
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if self.use_mup:
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embedding = embedding * self.input_mult
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return embedding
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class TeleFLMForCausalLM(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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# mup
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self.use_mup = self.config.use_mup
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if self.use_mup:
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self.mup_scale_factor = self.config.mup_scale_factor
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self.output_mult = self.config.output_mult / self.mup_scale_factor
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logit_scale = self.output_mult
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self.logits_processor = LogitsProcessor(
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self.config.vocab_size, scale=logit_scale
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)
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