wehub-resource-sync
113 lines
5.0 KiB
Python
113 lines
5.0 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 unittest
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from queue import Empty
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from threading import Thread
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import paddle
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from paddlenlp.generation import TextIteratorStreamer, TextStreamer
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from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer
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from paddlenlp.transformers.utils import CaptureStd
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from tests.testing_utils import slow
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from tests.transformers.test_modeling_common import ids_tensor
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class StreamerTester(unittest.TestCase):
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def get_inputs(self, model):
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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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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"decode_strategy": "greedy_search",
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"max_length": 10,
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}
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def test_text_streamer_matches_non_streaming(self):
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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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input_kwargs = self.get_inputs(model)
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greedy_ids = model.generate(**input_kwargs)
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greedy_text = tokenizer.decode(greedy_ids[0][0])
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with CaptureStd(out=True, err=False, replay=True) as cs:
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streamer = TextStreamer(tokenizer)
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model.generate(**input_kwargs, 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("__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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input_kwargs = self.get_inputs(model)
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greedy_ids = model.generate(**input_kwargs)
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greedy_text = tokenizer.decode(greedy_ids[0][0])
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streamer = TextIteratorStreamer(tokenizer)
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generation_kwargs = {**input_kwargs, "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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@slow
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def test_text_streamer_decode_kwargs(self):
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# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
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# with actual models -- the dummy models' tokenizers are not aligned with their models, and
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# `skip_special_tokens=True` has no effect on them
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tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
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model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m")
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model.config.eos_token_id = -1
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input_ids = paddle.ones([1, 5], dtype="int64") * model.config.bos_token_id
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attention_mask = paddle.ones_like(input_ids, dtype="bool")
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with CaptureStd(out=True, err=False, replay=True) as cs:
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streamer = TextStreamer(tokenizer, skip_special_tokens=True)
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model.generate(input_ids, attention_mask=attention_mask, max_length=1, do_sample=False, streamer=streamer)
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# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
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# re-tokenized, must only contain one token
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streamer_text = cs.out[:-1] # Remove the final "\n"
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streamer_text_tokenized = tokenizer(streamer_text, return_tensors="pd")
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self.assertEqual(streamer_text_tokenized.input_ids.shape, [1, 1])
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def test_iterator_streamer_timeout(self):
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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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input_kwargs = self.get_inputs(model)
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streamer = TextIteratorStreamer(tokenizer, timeout=0.001)
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generation_kwargs = {**input_kwargs, "streamer": streamer}
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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# The streamer will timeout after 0.001 seconds, so an exception will be raised
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with self.assertRaises(Empty):
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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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