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66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Team Inc.
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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 clone 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 unittest
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from threading import Thread
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from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
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from transformers.testing_utils import CaptureStdout, require_torch, torch_device
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from ..test_modeling_common import ids_tensor
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if is_torch_available():
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from transformers import AutoModelForCausalLM
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@require_torch
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class StreamerTester(unittest.TestCase):
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def test_text_streamer_matches_non_streaming(self):
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
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model.config.eos_token_id = -1
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input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
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greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
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greedy_text = tokenizer.decode(greedy_ids[0])
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with CaptureStdout() as cs:
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streamer = TextStreamer(tokenizer)
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model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
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# The greedy text should be printed to stdout, except for the final "\n" in the streamer
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streamer_text = cs.out[:-1]
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self.assertEqual(streamer_text, greedy_text)
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def test_iterator_streamer_matches_non_streaming(self):
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
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model.config.eos_token_id = -1
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input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
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greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
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greedy_text = tokenizer.decode(greedy_ids[0])
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streamer = TextIteratorStreamer(tokenizer)
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generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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streamer_text = ""
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for new_text in streamer:
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streamer_text += new_text
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self.assertEqual(streamer_text, greedy_text)
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