91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
# Copyright 2024 the LlamaFactory 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 os
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import torch
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from peft import LoraModel, PeftModel
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from transformers import AutoModelForCausalLM
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from llamafactory.extras.misc import get_current_device
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from llamafactory.hparams import get_infer_args, get_train_args
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from llamafactory.model import load_model, load_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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TINY_LLAMA_PISSA = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-pissa")
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"stage": "sft",
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"do_train": True,
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"finetuning_type": "lora",
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"pissa_init": True,
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"pissa_iter": -1,
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"dataset": "llamafactory/tiny-supervised-dataset",
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"dataset_dir": "ONLINE",
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"template": "llama3",
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"cutoff_len": 1024,
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"overwrite_cache": True,
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"output_dir": "dummy_dir",
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"overwrite_output_dir": True,
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"fp16": True,
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}
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INFER_ARGS = {
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"model_name_or_path": TINY_LLAMA_PISSA,
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"adapter_name_or_path": TINY_LLAMA_PISSA,
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"adapter_folder": "pissa_init",
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"finetuning_type": "lora",
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"template": "llama3",
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"infer_dtype": "float16",
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}
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def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
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state_dict_a = model_a.state_dict()
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state_dict_b = model_b.state_dict()
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assert set(state_dict_a.keys()) == set(state_dict_b.keys())
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for name in state_dict_a.keys():
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assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5)
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def test_pissa_init():
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model_args, _, _, finetuning_args, _ = get_train_args(TRAIN_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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base_model = AutoModelForCausalLM.from_pretrained(
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TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
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)
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ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init", is_trainable=True)
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for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
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param.data = param.data.to(torch.float32)
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compare_model(model, ref_model)
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def test_pissa_inference():
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model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
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base_model = AutoModelForCausalLM.from_pretrained(
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TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
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)
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ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init")
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ref_model = ref_model.merge_and_unload()
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compare_model(model, ref_model)
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