64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
import os
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import torch
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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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"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_lora_all_modules():
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model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "all", **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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linear_modules = set()
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for name, param in model.named_parameters():
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if any(module in name for module in ["lora_A", "lora_B"]):
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linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-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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assert linear_modules == {"q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"}
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def test_lora_extra_modules():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{"lora_target": "all", "additional_target": "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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extra_modules = set()
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for name, param in model.named_parameters():
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if any(module in name for module in ["lora_A", "lora_B"]):
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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elif "modules_to_save" in name:
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extra_modules.add(name.split(".modules_to_save", maxsplit=1)[0].split(".")[-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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assert extra_modules == {"embed_tokens", "lm_head"}
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