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- Two acpx external reviews (codex + opencode): * docs/audit/variants_review.md: per-variant paper-vs-impl audit * docs/audit/design_review.md: peft EVA / baukit / antipasto3 vs lora-lite * docs/audit/SUMMARY.md: aggregate verdicts + 3 risks + 5 follow-ups - docs/refs/: peft_eva.py, peft_eva_finetuning.py, baukit_nethook.py, antipasto3_svd_adapter.py for offline reference Findings: LoRA clean; PiSSA/DoRA/IA3/HRA/DeLoRA have documented partial deviations. Top risks: init/grad tradeoffs hidden by coarse tests; qwen probe lacks strict identity tol; IA3 target placement untested.
97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
# Copyright 2024-present the HuggingFace Inc. 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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import torch
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
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from utils import DataCollator, TokenizerMetaMath
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from peft import EvaConfig, LoraConfig, get_peft_model, initialize_lora_eva_weights
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DEVICE = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
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# config
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model_name = "meta-llama/Llama-3.1-8B"
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max_seq_len = 512
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rank = 16
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alpha = 1
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rho = 2.0
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target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"]
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svd_batch_size = 4 # can be different from the batch size used in finetuning
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batch_size = 4
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learning_rate = 5e-4
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gradient_accumulation_steps = 8
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num_epochs = 1
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output_dir = "outputs"
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bf16 = True
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# load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# load dataset
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dataset = load_dataset("meta-math/MetaMathQA")
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dataset = dataset.map(
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TokenizerMetaMath(model_name),
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batched=True,
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remove_columns=dataset["train"].column_names,
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)
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dataset.set_format(type="torch")
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# data collator
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data_collator = DataCollator(tokenizer.eos_token_id, max_length=max_seq_len)
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# dataloader
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dataloader = DataLoader(
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dataset["train"],
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batch_size=svd_batch_size,
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collate_fn=data_collator,
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)
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# setup peft config
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eva_config = EvaConfig(rho=rho)
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peft_config = LoraConfig(
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r=rank, lora_alpha=alpha, target_modules=target_modules, init_lora_weights="eva", eva_config=eva_config
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)
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# move model to accelerator
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model = model.to(DEVICE)
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# to optimize memory usage during eva initialization, set low_cpu_mem_usage=True
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peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
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initialize_lora_eva_weights(peft_model, dataloader)
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# setup training arguments
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training_args = TrainingArguments(
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per_device_train_batch_size=batch_size,
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learning_rate=learning_rate,
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gradient_accumulation_steps=gradient_accumulation_steps,
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num_train_epochs=num_epochs,
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output_dir=output_dir,
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remove_unused_columns=False,
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bf16=bf16,
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)
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# continue with standard finetuning
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trainer = Trainer(
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model=peft_model,
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args=training_args,
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train_dataset=dataset["train"],
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data_collator=data_collator,
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)
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trainer.train()
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