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add unittest

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Former-commit-id: 8a1f0c5f922989e08a19c65de0b2c4afd2a5771f
This commit is contained in:
hiyouga
2024-07-19 01:06:27 +08:00
parent 4c1513a845
commit 994b9089e9
16 changed files with 436 additions and 260 deletions
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41
tests/model/test_pissa.py
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@@ -14,13 +14,7 @@
import os
import torch
from peft import LoraModel, PeftModel
from transformers import AutoModelForCausalLM
from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_infer_args, get_train_args
from llamafactory.model import load_model, load_tokenizer
from llamafactory.train.test_utils import compare_model, load_infer_model, load_reference_model, load_train_model
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
@@ -54,37 +48,14 @@ INFER_ARGS = {
}
def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
state_dict_a = model_a.state_dict()
state_dict_b = model_b.state_dict()
assert set(state_dict_a.keys()) == set(state_dict_b.keys())
for name in state_dict_a.keys():
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5)
def test_pissa_init():
model_args, _, _, finetuning_args, _ = get_train_args(TRAIN_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init", is_trainable=True)
for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
param.data = param.data.to(torch.float32)
def test_pissa_train():
model = load_train_model(**TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=True)
compare_model(model, ref_model)
def test_pissa_inference():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init")
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=False)
ref_model = ref_model.merge_and_unload()
compare_model(model, ref_model)