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* Reorganize example folder * Continue reorganization * Change requirements for tests * Final cleanup * Finish regroup with tests all passing * Copyright * Requirements and readme * Make a full link for the documentation * Address review comments * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Add symlink * Reorg again * Apply suggestions from code review Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> * Adapt title * Update to new strucutre * Remove test * Update READMEs Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
# Copyright 2020 The HuggingFace Team. 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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# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
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import os
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import sys
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from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
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from .utils import load_json
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class TestSummarizationDistillerMultiGPU(TestCasePlus):
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@classmethod
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def setUpClass(cls):
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return cls
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@slow
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@require_torch_gpu
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def test_distributed_eval(self):
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output_dir = self.get_auto_remove_tmp_dir()
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args = f"""
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--model_name Helsinki-NLP/opus-mt-en-ro
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--save_dir {output_dir}
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--data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
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--num_beams 2
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--task translation
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""".split()
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# we want this test to run even if there is only one GPU, but if there are more we use them all
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n_gpu = get_gpu_count()
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distributed_args = f"""
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-m torch.distributed.launch
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--nproc_per_node={n_gpu}
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{self.test_file_dir}/run_distributed_eval.py
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""".split()
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cmd = [sys.executable] + distributed_args + args
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execute_subprocess_async(cmd, env=self.get_env())
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metrics_save_path = os.path.join(output_dir, "test_bleu.json")
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metrics = load_json(metrics_save_path)
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# print(metrics)
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self.assertGreaterEqual(metrics["bleu"], 25)
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