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add tests

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Former-commit-id: 484634ee9c982e82e919ff67d507e0210345182d
This commit is contained in:
hiyouga
2024-06-15 19:51:20 +08:00
parent 308abfec6c
commit 7f90b0cd20
8 changed files with 166 additions and 14 deletions
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30
tests/model/test_base.py
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@@ -13,16 +13,21 @@
# limitations under the License.
import os
from typing import Dict
import torch
from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead
from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_model, load_tokenizer
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA,
"template": "llama3",
@@ -38,9 +43,32 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
self.v_head.load_state_dict(state_dict, strict=False)
del state_dict
def test_base():
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)
ref_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
ref_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
)
compare_model(model, ref_model)
def test_valuehead():
AutoModelForCausalLMWithValueHead.post_init = post_init # patch for CPU test
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, add_valuehead=True
)
ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(
TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
)
compare_model(model, ref_model)