80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from typing import Dict
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import pytest
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import torch
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from transformers import AutoModelForCausalLM
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from trl import AutoModelForCausalLMWithValueHead
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from llamafactory.extras.misc import get_current_device
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from llamafactory.hparams import get_infer_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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TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
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INFER_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"template": "llama3",
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"infer_dtype": "float16",
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}
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def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
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state_dict_a = model_a.state_dict()
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state_dict_b = model_b.state_dict()
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assert set(state_dict_a.keys()) == set(state_dict_b.keys())
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for name in state_dict_a.keys():
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assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
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@pytest.fixture
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def fix_valuehead_cpu_loading():
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def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
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state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
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self.v_head.load_state_dict(state_dict, strict=False)
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del state_dict
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AutoModelForCausalLMWithValueHead.post_init = post_init
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def test_base():
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model_args, _, finetuning_args, _ = get_infer_args(INFER_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=False)
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ref_model = AutoModelForCausalLM.from_pretrained(
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TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
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)
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compare_model(model, ref_model)
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@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
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def test_valuehead():
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model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(
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tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False, add_valuehead=True
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)
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ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(
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TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
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)
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compare_model(model, ref_model)
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