wehub-resource-sync
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
# Copyright (c) 2023 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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import os
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import sys
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import tempfile
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from pathlib import Path
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import paddle
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from paddlenlp.generation import GenerationConfig
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from paddlenlp.trainer import PdArgumentParser, Trainer, TrainingArguments
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from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer
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sys.path.append(str(Path(__file__).parent.parent.parent))
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from tests.parallel_launch import TestMultipleGpus
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from tests.transformers.test_modeling_common import ids_tensor
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class ShardingStage3Tester(TestMultipleGpus):
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def test_synced_gpus_greedy(self):
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# test this file
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self.run_2gpu(__file__)
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if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained("__internal_testing__/tiny-random-llama")
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model = AutoModelForCausalLM.from_pretrained("__internal_testing__/tiny-random-llama")
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model.config.eos_token_id = -1
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world_size = paddle.distributed.get_world_size()
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with tempfile.TemporaryDirectory() as tempdir:
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args_dict = {
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"sharding": "stage3",
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"sharding_parallel_degree": world_size,
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"fp16": True,
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"fp16_opt_level": "O2",
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"output_dir": os.path.join(tempdir, "output"),
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}
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parser = PdArgumentParser((TrainingArguments,))
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args = parser.parse_dict(args_dict)[0]
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trainer = Trainer(model, args=args, tokenizer=tokenizer)
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trainer.create_optimizer_and_scheduler(num_training_steps=10)
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trainer._wrap_model(trainer.model_wrapped)
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model = trainer.model
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model.eval()
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input_ids = ids_tensor([1, 5], vocab_size=model.config.vocab_size, dtype="int64")
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attention_mask = paddle.ones_like(input_ids, dtype="bool")
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input_kwargs = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"synced_gpus": True,
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}
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generation_config = GenerationConfig(max_length=10 + paddle.distributed.get_rank(), trunc_input=False)
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def test_synced_gpus_greedy():
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with paddle.no_grad():
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generation_config.decode_strategy = "greedy_search"
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model.generate(**input_kwargs, generation_config=generation_config)
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def test_synced_gpus_sample():
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with paddle.no_grad():
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generation_config.decode_strategy = "sampling"
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generation_config.top_k = 8
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model.generate(**input_kwargs, generation_config=generation_config)
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def test_synced_gpus_beam_search():
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with paddle.no_grad():
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generation_config.decode_strategy = "beam_search"
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generation_config.num_beams = 4
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model.generate(**input_kwargs, generation_config=generation_config)
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def test_synced_gpus_group_beam_search():
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with paddle.no_grad():
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generation_config.decode_strategy = "beam_search"
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generation_config.num_beams = 4
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generation_config.num_beam_groups = 2
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model.generate(**input_kwargs, generation_config=generation_config)
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test_synced_gpus_greedy()
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test_synced_gpus_sample()
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test_synced_gpus_beam_search()
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test_synced_gpus_group_beam_search()
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