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test_lora.py
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test_lora.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import annotations
import os
import sys
import unittest
import paddle
from parameterized import parameterized_class
from tests.testing_utils import argv_context_guard, load_test_config
from .testing_utils import LLMTest
@parameterized_class(
["model_dir"],
[
["llama"],
# ["chatglm"], @skip("Skip and wait to fix.")
# ["chatglm2"], @skip("Skip and wait to fix.")
# ["bloom"], @skip("Skip and wait to fix.")
["qwen"],
["baichuan"],
],
)
class LoraTest(LLMTest, unittest.TestCase):
config_path: str = "./tests/fixtures/llm/lora.yaml"
model_dir: str = None
def setUp(self) -> None:
LLMTest.setUp(self)
self.model_codes_dir = os.path.join(self.root_path, self.model_dir)
sys.path.insert(0, self.model_codes_dir)
def tearDown(self) -> None:
LLMTest.tearDown(self)
sys.path.remove(self.model_codes_dir)
def test_lora(self):
self.disable_static()
paddle.set_default_dtype("float32")
lora_config = load_test_config(self.config_path, "lora", self.model_dir)
lora_config["output_dir"] = self.output_dir
lora_config["dataset_name_or_path"] = self.data_dir
# use_quick_lora
lora_config["use_quick_lora"] = True
with argv_context_guard(lora_config):
from run_finetune import main
main()
# merge weights
merge_lora_weights_config = {
"lora_path": lora_config["output_dir"],
"model_name_or_path": lora_config["model_name_or_path"],
"output_path": lora_config["output_dir"],
}
with argv_context_guard(merge_lora_weights_config):
from tools.merge_lora_params import merge
merge()
# TODO(wj-Mcat): disable chatglm2 test temporarily
if self.model_dir not in ["qwen", "baichuan", "chatglm2"]:
self.run_predictor({"inference_model": True})
self.run_predictor({"inference_model": False})
def test_rslora_plus(self):
self.disable_static()
paddle.set_default_dtype("float32")
lora_config = load_test_config(self.config_path, "rslora_plus", self.model_dir)
lora_config["output_dir"] = self.output_dir
lora_config["dataset_name_or_path"] = self.data_dir
with argv_context_guard(lora_config):
from run_finetune import main
main()
# merge weights
merge_lora_weights_config = {
"lora_path": lora_config["output_dir"],
"model_name_or_path": lora_config["model_name_or_path"],
"output_path": lora_config["output_dir"],
}
with argv_context_guard(merge_lora_weights_config):
from tools.merge_lora_params import merge
merge()
# TODO(wj-Mcat): disable chatglm2 test temporarily
if self.model_dir not in ["qwen", "baichuan", "chatglm2"]:
self.run_predictor({"inference_model": True})
self.run_predictor({"inference_model": False})
# @parameterized_class(
# ["model_dir"],
# [
# ["llama"],
# ["qwen"],
# ],
# )
# class LoraChatTemplateTest(LLMTest, unittest.TestCase):
# config_path: str = "./tests/fixtures/llm/lora.yaml"
# model_dir: str = None
# def setUp(self) -> None:
# LLMTest.setUp(self)
# self.model_codes_dir = os.path.join(self.root_path, self.model_dir)
# sys.path.insert(0, self.model_codes_dir)
# self.rounds_data_dir = tempfile.mkdtemp()
# shutil.copyfile(
# os.path.join(self.data_dir, "train.json"),
# os.path.join(self.rounds_data_dir, "train.json"),
# )
# shutil.copyfile(
# os.path.join(self.data_dir, "dev.json"),
# os.path.join(self.rounds_data_dir, "dev.json"),
# )
# self.create_multi_turns_data(os.path.join(self.rounds_data_dir, "train.json"))
# self.create_multi_turns_data(os.path.join(self.rounds_data_dir, "dev.json"))
# def create_multi_turns_data(self, file: str):
# result = []
# with open(file, "r", encoding="utf-8") as f:
# for line in f:
# data = json.loads(line)
# data["src"] = [data["src"]] * 3
# data["tgt"] = [data["tgt"]] * 3
# result.append(data)
# with open(file, "w", encoding="utf-8") as f:
# for data in result:
# line = json.dumps(line)
# f.write(line + "\n")
# def tearDown(self) -> None:
# LLMTest.tearDown(self)
# sys.path.remove(self.model_codes_dir)
# def test_lora(self):
# self.disable_static()
# paddle.set_default_dtype("float32")
# lora_config = load_test_config(self.config_path, "lora", self.model_dir)
# lora_config["dataset_name_or_path"] = self.rounds_data_dir
# lora_config["chat_template"] = "./tests/fixtures/chat_template.json"
# lora_config["output_dir"] = self.output_dir
# with argv_context_guard(lora_config):
# from run_finetune import main
# main()
# # merge weights
# merge_lora_weights_config = {
# "model_name_or_path": lora_config["model_name_or_path"],
# "lora_path": lora_config["output_dir"],
# "merge_model_path": lora_config["output_dir"],
# }
# with argv_context_guard(merge_lora_weights_config):
# from tools.merge_lora_params import merge
# merge()
# if self.model_dir not in ["chatglm2", "qwen", "baichuan"]:
# self.run_predictor({"inference_model": True})
# self.run_predictor({"inference_model": False})