86 lines
3.0 KiB
Python
86 lines
3.0 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 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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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": "freeze",
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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,
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"finetuning_type": "freeze",
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"template": "llama3",
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"infer_dtype": "float16",
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}
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def test_freeze_train_all_modules():
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model_args, _, _, finetuning_args, _ = get_train_args({"freeze_trainable_layers": 1, **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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for name, param in model.named_parameters():
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if name.startswith("model.layers.1."):
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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else:
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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def test_freeze_train_extra_modules():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{"freeze_trainable_layers": 1, "freeze_extra_modules": "embed_tokens,lm_head", **TRAIN_ARGS}
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)
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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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for name, param in model.named_parameters():
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if name.startswith("model.layers.1.") or any(module in name for module in ["embed_tokens", "lm_head"]):
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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else:
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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def test_freeze_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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for param in model.parameters():
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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