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