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https://github.com/wassname/lora-lite.git
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wassname
fdb4c77d6c
- Fetch canonical reference impls for offline review:
* peft_{lora,hra,delora,ia3}_layer.py + peft_lora_{dora,variants}.py
* orig_pissa_init.py (MuLabPKU/PiSSA)
* orig_hra_layer.py (DaShenZi721/HRA)
* orig_delora.py (ExplainableML/DeLoRA author fork)
- Add reference-impl URLs to all 6 variant docstrings
- Document HRA gate=0 dead-grad issue and DoRA detach-omission in their docstrings
- Re-run external review (codex) with refs available -> docs/audit/variants_review_v2.md
Major NEW findings vs paper-only review:
* DeLoRA: scalar W.norm() should be per-input-channel norm(dim=0)
* HRA: PEFT uses symmetric repeated-column init (no dead grad), not zero gate
* IA3: FFN targets need input-side gating, not output, our up_proj advice wrong
* All LoRA-family: cfg.dropout silently ignored (no-op)
* DeLoRA: wnorm should be persistent buffer, not Parameter
HRA and DeLoRA upgraded to BUGGY (from Partial)
60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
# Copyright 2023-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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import os
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from peft import LoraConfig, get_peft_model
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import argparse
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parser = argparse.ArgumentParser(description="Separate the principal singular value and singular vectors from base model")
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parser.add_argument("--base_model_path", type=str, required=True, help="The name or path of the base model.")
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parser.add_argument("--output_dir", type=str, required=True)
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parser.add_argument("--bits", type=str, default="bf16", choices=["bf16", "fp16", "fp32"])
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parser.add_argument("--init_weights", type=str, default="pissa", help="(`['pissa', 'pissa_niter_[number of iters]']`)")
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parser.add_argument("--lora_r", type=int, default=128)
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parser.add_argument("--lora_alpha", type=int, default=128)
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parser.add_argument("--lora_dropout", type=float, default=0)
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parser.add_argument('--target_modules', nargs='+', help='', required=True)
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script_args = parser.parse_args()
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print(script_args)
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model = AutoModelForCausalLM.from_pretrained(
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script_args.base_model_path,
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torch_dtype=(
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torch.float16
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if script_args.bits == "fp16"
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else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
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),
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_path)
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tokenizer.pad_token_id = tokenizer.eos_token_id
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lora_config = LoraConfig(
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r=script_args.lora_r,
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lora_alpha=script_args.lora_alpha,
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init_lora_weights=True if script_args.init_weights=="True" else script_args.init_weights,
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lora_dropout=script_args.lora_dropout,
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target_modules=script_args.target_modules,
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)
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peft_model = get_peft_model(model, lora_config)
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# Save PiSSA modules:
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peft_model.peft_config["default"].init_lora_weights = True
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peft_model.save_pretrained(os.path.join(script_args.output_dir, "pissa_init"))
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# Save residual model:
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peft_model = peft_model.unload()
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peft_model.save_pretrained(script_args.output_dir)
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# Save the tokenizer:
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tokenizer.save_pretrained(script_args.output_dir) |