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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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20
tests/model/test_freeze.py
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@@ -16,8 +16,7 @@ import os
import torch
from llamafactory.hparams import get_infer_args, get_train_args
from llamafactory.model import load_model, load_tokenizer
from llamafactory.train.test_utils import load_infer_model, load_train_model
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
@@ -46,10 +45,7 @@ INFER_ARGS = {
def test_freeze_train_all_modules():
model_args, _, _, finetuning_args, _ = get_train_args({"freeze_trainable_layers": 1, **TRAIN_ARGS})
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
model = load_train_model(freeze_trainable_layers=1, **TRAIN_ARGS)
for name, param in model.named_parameters():
if name.startswith("model.layers.1."):
assert param.requires_grad is True
@@ -60,12 +56,7 @@ def test_freeze_train_all_modules():
def test_freeze_train_extra_modules():
model_args, _, _, finetuning_args, _ = get_train_args(
{"freeze_trainable_layers": 1, "freeze_extra_modules": "embed_tokens,lm_head", **TRAIN_ARGS}
)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
model = load_train_model(freeze_trainable_layers=1, freeze_extra_modules="embed_tokens,lm_head", **TRAIN_ARGS)
for name, param in model.named_parameters():
if name.startswith("model.layers.1.") or any(module in name for module in ["embed_tokens", "lm_head"]):
assert param.requires_grad is True
@@ -76,10 +67,7 @@ def test_freeze_train_extra_modules():
def test_freeze_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)
model = load_infer_model(**INFER_ARGS)
for param in model.parameters():
assert param.requires_grad is False
assert param.dtype == torch.float16