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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Dict
import pytest
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",
"infer_dtype": "float16",
}
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)
@pytest.fixture
def fix_valuehead_cpu_loading():
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
AutoModelForCausalLMWithValueHead.post_init = post_init
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=torch.float16, device_map=get_current_device()
)
compare_model(model, ref_model)
@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
def test_valuehead():
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)
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