tests/generation/test_streamers.py

# coding=utf-8
# Copyright 2023 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM


@require_torch
class StreamerTester(unittest.TestCase):
    def test_text_streamer_matches_non_streaming(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        greedy_text = tokenizer.decode(greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        # The greedy text should be printed to stdout, except for the final "\n" in the streamer
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, greedy_text)

    def test_iterator_streamer_matches_non_streaming(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        greedy_text = tokenizer.decode(greedy_ids[0])

        streamer = TextIteratorStreamer(tokenizer)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
        streamer_text = ""
        for new_text in streamer:
            streamer_text += new_text

        self.assertEqual(streamer_text, greedy_text)

    def test_text_streamer_skip_prompt(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        new_greedy_ids = greedy_ids[:, input_ids.shape[1] :]
        new_greedy_text = tokenizer.decode(new_greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_prompt=True)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        # The greedy text should be printed to stdout, except for the final "\n" in the streamer
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, new_greedy_text)

    def test_text_streamer_decode_kwargs(self):
        # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
        # with actual models -- the dummy models' tokenizers are not aligned with their models, and
        # `skip_special_tokens=True` has no effect on them
        tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
        model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = torch.ones((1, 5), device=torch_device).long() * model.config.bos_token_id
        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_special_tokens=True)
            model.generate(input_ids, max_new_tokens=1, do_sample=False, streamer=streamer)

        # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
        # re-tokenized, must only contain one token
        streamer_text = cs.out[:-1]  # Remove the final "\n"
        streamer_text_tokenized = tokenizer(streamer_text, return_tensors="pt")
        self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1))

    def test_iterator_streamer_timeout(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        streamer = TextIteratorStreamer(tokenizer, timeout=0.001)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()

        # The streamer will timeout after 0.001 seconds, so an exception will be raised
        with self.assertRaises(Empty):
            streamer_text = ""
            for new_text in streamer:
                streamer_text += new_text