105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
# Adapted from
|
|
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
|
|
# Copyright 2023 The vLLM team.
|
|
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
|
# and OPT implementations in this library. It has been modified from its
|
|
# original forms to accommodate minor architectural differences compared
|
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
|
#
|
|
# 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.
|
|
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/teleflm.py
|
|
|
|
from typing import List, Optional, Tuple, Union
|
|
|
|
import torch
|
|
from transformers import LlamaConfig
|
|
|
|
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
|
|
from sglang.srt.models.llama import LlamaForCausalLM, LlamaModel
|
|
|
|
|
|
class TeleFLMModel(LlamaModel):
|
|
"""
|
|
This implementation is based on the µScaling paper presented at
|
|
the ICLR 2025 Workshop:
|
|
NanoLM: An Affordable LLM Study Benchmark \
|
|
via Accurate Loss Prediction across Scales
|
|
by Yiqun Yao et al.
|
|
Available at: https://openreview.net/forum?id=IwaPYg1SCA
|
|
arXiv preprint: https://arxiv.org/abs/2304.06875
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: LlamaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__(config, quant_config=quant_config, prefix=prefix)
|
|
self.use_mup = getattr(self.config, "use_mup", False)
|
|
if self.use_mup:
|
|
self.input_mult = self.config.input_mult
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]], PPProxyTensors]:
|
|
if self.pp_group.is_first_rank and input_embeds is None:
|
|
input_embeds = self.embed_tokens(input_ids)
|
|
if self.use_mup:
|
|
input_embeds = input_embeds * self.input_mult
|
|
|
|
return super().forward(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
forward_batch=forward_batch,
|
|
input_embeds=input_embeds,
|
|
pp_proxy_tensors=pp_proxy_tensors,
|
|
)
|
|
|
|
|
|
class TeleFLMForCausalLM(LlamaForCausalLM):
|
|
def __init__(
|
|
self,
|
|
config: LlamaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__(config, quant_config=quant_config, prefix=prefix)
|
|
self.use_mup = getattr(self.config, "use_mup", False)
|
|
if self.use_mup:
|
|
self.mup_scale_factor = self.config.mup_scale_factor
|
|
self.output_mult = self.config.output_mult / self.mup_scale_factor
|
|
self.logits_processor.logit_scale = self.output_mult
|
|
|
|
def _init_model(
|
|
self,
|
|
config: LlamaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
return TeleFLMModel(config, quant_config=quant_config, prefix=prefix)
|
|
|
|
|
|
EntryClass = TeleFLMForCausalLM
|