75 lines
3.0 KiB
Python
75 lines
3.0 KiB
Python
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# 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.extras.misc import get_current_device
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from llamafactory.hparams import 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": "lora",
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"lora_target": "all",
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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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def test_checkpointing_enable():
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model_args, _, _, finetuning_args, _ = get_train_args({"disable_gradient_checkpointing": False, **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 module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
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assert getattr(module, "gradient_checkpointing") is True
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def test_checkpointing_disable():
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model_args, _, _, finetuning_args, _ = get_train_args({"disable_gradient_checkpointing": True, **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 module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
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assert getattr(module, "gradient_checkpointing") is False
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def test_upcast_layernorm():
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model_args, _, _, finetuning_args, _ = get_train_args({"upcast_layernorm": True, **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 param.ndim == 1 and "norm" in name:
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assert param.dtype == torch.float32
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def test_upcast_lmhead_output():
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model_args, _, _, finetuning_args, _ = get_train_args({"upcast_lmhead_output": True, **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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inputs = torch.randn((1, 16), dtype=torch.float16, device=get_current_device())
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outputs: "torch.Tensor" = model.lm_head(inputs)
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assert outputs.dtype == torch.float32
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