mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
9c500015c5
* pipeline generation defaults * add max_new_tokens=20 in test pipelines * pop all kwargs that are used to parameterize generation config * add class attr that tell us whether a pipeline calls generate * tmp commit * pt text gen pipeline tests passing * remove failing tf tests * fix text gen pipeline mixin test corner case * update text_to_audio pipeline tests * trigger tests * a few more tests * skips * some more audio tests * not slow * broken * lower severity of generation mode errors * fix all asr pipeline tests * nit * skip * image to text pipeline tests * text2test pipeline * last pipelines * fix flaky * PR comments * handle generate attrs more carefully in models that cant generate * same as above
543 lines
20 KiB
Python
543 lines
20 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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import unittest
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from transformers import (
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MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TextGenerationPipeline,
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logging,
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pipeline,
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)
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from transformers.testing_utils import (
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CaptureLogger,
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is_pipeline_test,
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require_accelerate,
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require_torch,
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require_torch_accelerator,
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require_torch_or_tf,
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torch_device,
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)
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from .test_pipelines_common import ANY
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@is_pipeline_test
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@require_torch_or_tf
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class TextGenerationPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
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tf_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
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@require_torch
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def test_small_model_pt(self):
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text_generator = pipeline(
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task="text-generation",
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model="hf-internal-testing/tiny-random-LlamaForCausalLM",
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framework="pt",
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max_new_tokens=10,
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)
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# Using `do_sample=False` to force deterministic output
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outputs = text_generator("This is a test", do_sample=False)
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self.assertEqual(outputs, [{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}])
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outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
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[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
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],
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)
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outputs = text_generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True)
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self.assertEqual(
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outputs,
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[
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{"generated_token_ids": ANY(list)},
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{"generated_token_ids": ANY(list)},
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],
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)
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@require_torch
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def test_small_chat_model_pt(self):
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text_generator = pipeline(
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task="text-generation",
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model="hf-internal-testing/tiny-gpt2-with-chatml-template",
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framework="pt",
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)
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# Using `do_sample=False` to force deterministic output
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chat1 = [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a test"},
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]
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chat2 = [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a second test"},
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]
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outputs = text_generator(chat1, do_sample=False, max_new_tokens=10)
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expected_chat1 = chat1 + [
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{
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"role": "assistant",
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"content": " factors factors factors factors factors factors factors factors factors factors",
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}
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]
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self.assertEqual(
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outputs,
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[
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{"generated_text": expected_chat1},
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],
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)
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outputs = text_generator([chat1, chat2], do_sample=False, max_new_tokens=10)
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expected_chat2 = chat2 + [
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{
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"role": "assistant",
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"content": " stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
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}
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]
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self.assertEqual(
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outputs,
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[
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[{"generated_text": expected_chat1}],
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[{"generated_text": expected_chat2}],
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],
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)
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@require_torch
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def test_small_chat_model_continue_final_message(self):
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# Here we check that passing a chat that ends in an assistant message is handled correctly
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# by continuing the final message rather than starting a new one
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text_generator = pipeline(
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task="text-generation",
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model="hf-internal-testing/tiny-gpt2-with-chatml-template",
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framework="pt",
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)
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# Using `do_sample=False` to force deterministic output
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chat1 = [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a test"},
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{"role": "assistant", "content": "This is"},
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]
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outputs = text_generator(chat1, do_sample=False, max_new_tokens=10)
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# Assert that we continued the last message and there isn't a sneaky <|im_end|>
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self.assertEqual(
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outputs,
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[
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{
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"generated_text": [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a test"},
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{
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"role": "assistant",
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"content": "This is stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
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},
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]
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}
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],
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)
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@require_torch
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def test_small_chat_model_continue_final_message_override(self):
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# Here we check that passing a chat that ends in an assistant message is handled correctly
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# by continuing the final message rather than starting a new one
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text_generator = pipeline(
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task="text-generation",
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model="hf-internal-testing/tiny-gpt2-with-chatml-template",
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framework="pt",
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)
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# Using `do_sample=False` to force deterministic output
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chat1 = [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a test"},
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]
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outputs = text_generator(chat1, do_sample=False, max_new_tokens=10, continue_final_message=True)
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# Assert that we continued the last message and there isn't a sneaky <|im_end|>
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self.assertEqual(
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outputs,
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[
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{
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"generated_text": [
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{"role": "system", "content": "This is a system message."},
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{
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"role": "user",
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"content": "This is a test stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
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},
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]
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}
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],
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)
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@require_torch
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def test_small_chat_model_with_dataset_pt(self):
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from torch.utils.data import Dataset
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from transformers.pipelines.pt_utils import KeyDataset
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class MyDataset(Dataset):
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data = [
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[
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a test"},
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],
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]
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def __len__(self):
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return 1
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def __getitem__(self, i):
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return {"text": self.data[i]}
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text_generator = pipeline(
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task="text-generation",
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model="hf-internal-testing/tiny-gpt2-with-chatml-template",
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framework="pt",
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)
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dataset = MyDataset()
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key_dataset = KeyDataset(dataset, "text")
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for outputs in text_generator(key_dataset, do_sample=False, max_new_tokens=10):
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expected_chat = dataset.data[0] + [
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{
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"role": "assistant",
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"content": " factors factors factors factors factors factors factors factors factors factors",
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}
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]
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self.assertEqual(
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outputs,
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[
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{"generated_text": expected_chat},
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],
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)
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@require_torch
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def test_small_chat_model_with_iterator_pt(self):
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from transformers.pipelines.pt_utils import PipelineIterator
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text_generator = pipeline(
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task="text-generation",
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model="hf-internal-testing/tiny-gpt2-with-chatml-template",
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framework="pt",
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)
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# Using `do_sample=False` to force deterministic output
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chat1 = [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a test"},
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]
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chat2 = [
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{"role": "system", "content": "This is a system message."},
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{"role": "user", "content": "This is a second test"},
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]
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expected_chat1 = chat1 + [
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{
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"role": "assistant",
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"content": " factors factors factors factors factors factors factors factors factors factors",
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}
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]
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expected_chat2 = chat2 + [
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{
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"role": "assistant",
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"content": " stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
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}
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]
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def data():
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yield from [chat1, chat2]
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outputs = text_generator(data(), do_sample=False, max_new_tokens=10)
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assert isinstance(outputs, PipelineIterator)
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outputs = list(outputs)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": expected_chat1}],
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[{"generated_text": expected_chat2}],
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],
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)
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def get_test_pipeline(
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self,
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model,
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tokenizer=None,
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image_processor=None,
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feature_extractor=None,
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processor=None,
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torch_dtype="float32",
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):
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text_generator = TextGenerationPipeline(
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model=model,
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tokenizer=tokenizer,
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feature_extractor=feature_extractor,
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image_processor=image_processor,
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processor=processor,
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torch_dtype=torch_dtype,
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max_new_tokens=5,
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)
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return text_generator, ["This is a test", "Another test"]
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def test_stop_sequence_stopping_criteria(self):
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prompt = """Hello I believe in"""
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text_generator = pipeline(
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"text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=5, do_sample=False
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)
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output = text_generator(prompt)
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self.assertEqual(
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output,
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[{"generated_text": "Hello I believe in fe fe fe fe fe"}],
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)
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output = text_generator(prompt, stop_sequence=" fe")
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self.assertEqual(output, [{"generated_text": "Hello I believe in fe"}])
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def run_pipeline_test(self, text_generator, _):
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model = text_generator.model
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tokenizer = text_generator.tokenizer
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outputs = text_generator("This is a test")
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
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outputs = text_generator("This is a test", return_full_text=False)
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertNotIn("This is a test", outputs[0]["generated_text"])
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text_generator = pipeline(
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task="text-generation", model=model, tokenizer=tokenizer, return_full_text=False, max_new_tokens=5
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)
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outputs = text_generator("This is a test")
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertNotIn("This is a test", outputs[0]["generated_text"])
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outputs = text_generator("This is a test", return_full_text=True)
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
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outputs = text_generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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],
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)
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if text_generator.tokenizer.pad_token is not None:
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outputs = text_generator(
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["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
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)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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],
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)
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with self.assertRaises(ValueError):
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outputs = text_generator("test", return_full_text=True, return_text=True)
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with self.assertRaises(ValueError):
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outputs = text_generator("test", return_full_text=True, return_tensors=True)
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with self.assertRaises(ValueError):
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outputs = text_generator("test", return_text=True, return_tensors=True)
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# Empty prompt is slightly special
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# it requires BOS token to exist.
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# Special case for Pegasus which will always append EOS so will
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# work even without BOS.
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if (
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text_generator.tokenizer.bos_token_id is not None
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or "Pegasus" in tokenizer.__class__.__name__
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or "Git" in model.__class__.__name__
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):
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outputs = text_generator("")
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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else:
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with self.assertRaises((ValueError, AssertionError)):
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outputs = text_generator("", add_special_tokens=False)
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if text_generator.framework == "tf":
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# TF generation does not support max_new_tokens, and it's impossible
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# to control long generation with only max_length without
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# fancy calculation, dismissing tests for now.
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self.skipTest(reason="TF generation does not support max_new_tokens")
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# We don't care about infinite range models.
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# They already work.
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# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
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EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS = [
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"RwkvForCausalLM",
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"XGLMForCausalLM",
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"GPTNeoXForCausalLM",
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"GPTNeoXJapaneseForCausalLM",
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"FuyuForCausalLM",
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"LlamaForCausalLM",
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]
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if (
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tokenizer.model_max_length < 10000
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and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
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):
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# Handling of large generations
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if str(text_generator.device) == "cpu":
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with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
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text_generator("This is a test" * 500, max_new_tokens=5)
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outputs = text_generator("This is a test" * 500, handle_long_generation="hole", max_new_tokens=5)
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# Hole strategy cannot work
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if str(text_generator.device) == "cpu":
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with self.assertRaises(ValueError):
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text_generator(
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"This is a test" * 500,
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handle_long_generation="hole",
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max_new_tokens=tokenizer.model_max_length + 10,
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)
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@require_torch
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@require_accelerate
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@require_torch_accelerator
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def test_small_model_pt_bloom_accelerate(self):
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import torch
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# Classic `model_kwargs`
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pipe = pipeline(
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model="hf-internal-testing/tiny-random-bloom",
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model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloat16},
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max_new_tokens=5,
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do_sample=False,
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)
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self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
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out = pipe("This is a test")
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self.assertEqual(
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out,
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[{"generated_text": ("This is a test test test test test test")}],
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)
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# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
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pipe = pipeline(
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model="hf-internal-testing/tiny-random-bloom",
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device_map="auto",
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torch_dtype=torch.bfloat16,
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max_new_tokens=5,
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do_sample=False,
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)
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self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
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out = pipe("This is a test")
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self.assertEqual(
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out,
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[{"generated_text": ("This is a test test test test test test")}],
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)
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# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
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pipe = pipeline(
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model="hf-internal-testing/tiny-random-bloom", device_map="auto", max_new_tokens=5, do_sample=False
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)
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self.assertEqual(pipe.model.lm_head.weight.dtype, torch.float32)
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out = pipe("This is a test")
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self.assertEqual(
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out,
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[{"generated_text": ("This is a test test test test test test")}],
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)
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@require_torch
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@require_torch_accelerator
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def test_small_model_fp16(self):
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import torch
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pipe = pipeline(
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model="hf-internal-testing/tiny-random-bloom",
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device=torch_device,
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torch_dtype=torch.float16,
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max_new_tokens=3,
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)
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pipe("This is a test")
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@require_torch
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@require_accelerate
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@require_torch_accelerator
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def test_pipeline_accelerate_top_p(self):
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import torch
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pipe = pipeline(
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model="hf-internal-testing/tiny-random-bloom",
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device_map=torch_device,
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torch_dtype=torch.float16,
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max_new_tokens=3,
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)
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pipe("This is a test", do_sample=True, top_p=0.5)
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def test_pipeline_length_setting_warning(self):
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prompt = """Hello world"""
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text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=5)
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if text_generator.model.framework == "tf":
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logger = logging.get_logger("transformers.generation.tf_utils")
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else:
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logger = logging.get_logger("transformers.generation.utils")
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logger_msg = "Both `max_new_tokens`" # The beginning of the message to be checked in this test
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# Both are set by the user -> log warning
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with CaptureLogger(logger) as cl:
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_ = text_generator(prompt, max_length=10, max_new_tokens=1)
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self.assertIn(logger_msg, cl.out)
|
||
|
||
# The user only sets one -> no warning
|
||
with CaptureLogger(logger) as cl:
|
||
_ = text_generator(prompt, max_new_tokens=1)
|
||
self.assertNotIn(logger_msg, cl.out)
|
||
|
||
with CaptureLogger(logger) as cl:
|
||
_ = text_generator(prompt, max_length=10, max_new_tokens=None)
|
||
self.assertNotIn(logger_msg, cl.out)
|
||
|
||
def test_return_dict_in_generate(self):
|
||
text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=2)
|
||
out = text_generator(
|
||
["This is great !", "Something else"], return_dict_in_generate=True, output_logits=True, output_scores=True
|
||
)
|
||
self.assertEqual(
|
||
out,
|
||
[
|
||
[
|
||
{
|
||
"generated_text": ANY(str),
|
||
"logits": ANY(list),
|
||
"scores": ANY(list),
|
||
},
|
||
],
|
||
[
|
||
{
|
||
"generated_text": ANY(str),
|
||
"logits": ANY(list),
|
||
"scores": ANY(list),
|
||
},
|
||
],
|
||
],
|
||
)
|
||
|
||
@require_torch
|
||
def test_pipeline_assisted_generation(self):
|
||
"""Tests that we can run assisted generation in the pipeline"""
|
||
model = "hf-internal-testing/tiny-random-MistralForCausalLM"
|
||
pipe = pipeline("text-generation", model=model, assistant_model=model, max_new_tokens=2)
|
||
|
||
# We can run the pipeline
|
||
prompt = "Hello world"
|
||
_ = pipe(prompt)
|
||
|
||
# It is running assisted generation under the hood (e.g. flags incompatible with assisted gen will crash)
|
||
with self.assertRaises(ValueError):
|
||
_ = pipe(prompt, generate_kwargs={"num_beams": 2})
|