wehub-resource-sync
85 lines
2.6 KiB
Python
85 lines
2.6 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 shutil
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import sys
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import tempfile
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import time
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import unittest
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from parameterized import parameterized_class
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from paddlenlp.utils.downloader import get_path_from_url
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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"], @skip("Skip and wait to fix.")
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# ["qwen"], @skip("Skip and wait to fix.")
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["qwen2"],
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["gpt"],
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],
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)
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class PretrainTest(LLMTest, unittest.TestCase):
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config_path: str = "./tests/fixtures/llm/pretrain.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.dataset_dir = tempfile.mkdtemp()
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self.model_codes_dir = self.root_path
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def tearDown(self) -> None:
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LLMTest.tearDown(self)
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shutil.rmtree(self.dataset_dir)
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def test_pretrain(self):
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pretrain_flag = False
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for key, value in sys.modules.items():
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if "run_pretrain" in key:
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pretrain_flag = True
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break
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if pretrain_flag:
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del sys.modules["run_pretrain"]
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# Run pretrain
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URL = "https://bj.bcebos.com/paddlenlp/models/transformers/llama/data/llama_openwebtext_100k.bin"
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URL2 = "https://bj.bcebos.com/paddlenlp/models/transformers/llama/data/llama_openwebtext_100k.idx"
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get_path_from_url(URL, root_dir=self.dataset_dir)
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time.sleep(5)
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get_path_from_url(URL2, root_dir=self.dataset_dir)
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pretrain_config = load_test_config(self.config_path, "pretrain", self.model_dir)
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pretrain_config["input_dir"] = self.dataset_dir
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pretrain_config["output_dir"] = self.output_dir
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with argv_context_guard(pretrain_config):
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from run_pretrain import main
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main()
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# Now, only work for llama, not gpt or qwen
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if self.model_dir == "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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