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* Adding some quality of life for `pipeline` function. * Update docs/source/main_classes/pipelines.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/__init__.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Improve the tests. Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
408 lines
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ReStructuredText
408 lines
18 KiB
ReStructuredText
..
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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Pipelines
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-----------------------------------------------------------------------------------------------------------------------
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The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of
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the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
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Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
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:doc:`task summary <../task_summary>` for examples of use.
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There are two categories of pipeline abstractions to be aware about:
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- The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines.
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- The other task-specific pipelines:
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- :class:`~transformers.AudioClassificationPipeline`
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- :class:`~transformers.AutomaticSpeechRecognitionPipeline`
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- :class:`~transformers.ConversationalPipeline`
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- :class:`~transformers.FeatureExtractionPipeline`
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- :class:`~transformers.FillMaskPipeline`
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- :class:`~transformers.ImageClassificationPipeline`
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- :class:`~transformers.ImageSegmentationPipeline`
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- :class:`~transformers.ObjectDetectionPipeline`
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- :class:`~transformers.QuestionAnsweringPipeline`
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- :class:`~transformers.SummarizationPipeline`
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- :class:`~transformers.TableQuestionAnsweringPipeline`
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- :class:`~transformers.TextClassificationPipeline`
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- :class:`~transformers.TextGenerationPipeline`
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- :class:`~transformers.Text2TextGenerationPipeline`
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- :class:`~transformers.TokenClassificationPipeline`
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- :class:`~transformers.TranslationPipeline`
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- :class:`~transformers.ZeroShotClassificationPipeline`
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The pipeline abstraction
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
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pipeline but can provide additional quality of life.
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Simple call on one item:
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.. code-block::
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>>> pipe = pipeline("text-classification")
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>>> pipe("This restaurant is awesome")
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[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
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If you want to use a specific model from the `hub <https://huggingface.co>`__ you can ignore the task if the model on
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the hub already defines it:
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.. code-block::
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>>> pipe = pipeline(model="roberta-large-mnli")
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>>> pipe("This restaurant is awesome")
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[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
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To call a pipeline on many items, you can either call with a `list`.
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.. code-block::
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>>> pipe = pipeline("text-classification")
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>>> pipe(["This restaurant is awesome", "This restaurant is aweful"])
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[{'label': 'POSITIVE', 'score': 0.9998743534088135},
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{'label': 'NEGATIVE', 'score': 0.9996669292449951}]
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To iterate of full datasets it is recommended to use a :obj:`dataset` directly. This means you don't need to allocate
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the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
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GPU. If it doesn't don't hesitate to create an issue.
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.. code-block::
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import datasets
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from transformers import pipeline
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from transformers.pipelines.base import KeyDataset
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import tqdm
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pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
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dataset = datasets.load_dataset("superb", name="asr", split="test")
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# KeyDataset (only `pt`) will simply return the item in the dict returned by the dataset item
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# as we're not interested in the `target` part of the dataset.
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for out in tqdm.tqdm(pipe(KeyDataset(dataset, "file"))):
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print(out)
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# {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
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# {"text": ....}
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# ....
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.. autofunction:: transformers.pipeline
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Pipeline batching
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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All pipelines (except `zero-shot-classification` and `question-answering` currently) can use batching. This will work
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whenever the pipeline uses its streaming ability (so when passing lists or :obj:`Dataset`).
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.. code-block::
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from transformers import pipeline
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from transformers.pipelines.base import KeyDataset
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import datasets
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import tqdm
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dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
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pipe = pipeline("text-classification", device=0)
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for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
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print(out)
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# [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
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# Exactly the same output as before, but the content are passed
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# as batches to the model
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.. warning::
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However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending
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on hardware, data and the actual model being used.
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Example where it's most a speedup:
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.. code-block::
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from transformers import pipeline
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from torch.utils.data import Dataset
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import tqdm
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pipe = pipeline("text-classification", device=0)
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class MyDataset(Dataset):
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def __len__(self):
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return 5000
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def __getitem__(self, i):
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return "This is a test"
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dataset = MyDataset()
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for batch_size in [1, 8, 64, 256]:
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print("-" * 30)
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print(f"Streaming batch_size={batch_size}")
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for out in tqdm.tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):
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pass
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.. code-block::
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# On GTX 970
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------------------------------
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Streaming no batching
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100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]
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------------------------------
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Streaming batch_size=8
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100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]
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------------------------------
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Streaming batch_size=64
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100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]
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------------------------------
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Streaming batch_size=256
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100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
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(diminishing returns, saturated the GPU)
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Example where it's most a slowdown:
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.. code-block::
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class MyDataset(Dataset):
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def __len__(self):
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return 5000
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def __getitem__(self, i):
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if i % 64 == 0:
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n = 100
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else:
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n = 1
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return "This is a test" * n
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This is a occasional very long sentence compared to the other. In that case, the **whole** batch will need to be 400
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tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
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bigger batches, the program simply crashes.
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.. code-block::
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------------------------------
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Streaming no batching
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100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s]
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------------------------------
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Streaming batch_size=8
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100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s]
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------------------------------
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Streaming batch_size=64
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100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s]
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------------------------------
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Streaming batch_size=256
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0%| | 0/1000 [00:00<?, ?it/s]
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Traceback (most recent call last):
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File "/home/nicolas/src/transformers/test.py", line 42, in <module>
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for out in tqdm.tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
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....
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q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
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RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)
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There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
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thumb:
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For users, a rule of thumb is:
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- **Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
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only way to go.**
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- If you are latency constrained (live product doing inference), don't batch
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- If you are using CPU, don't batch.
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- If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:
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- If you have no clue about the size of the sequence_length ("natural" data), by default don't batch, measure and
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try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don't
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control the sequence_length.)
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- If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
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it until you get OOMs.
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- The larger the GPU the more likely batching is going to be more interesting
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- As soon as you enable batching, make sure you can handle OOMs nicely.
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Pipeline custom code
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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If you want to override a specific pipeline.
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Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
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cases, so :obj:`transformers` could maybe support your use case.
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If you want to try simply you can:
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- Subclass your pipeline of choice
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.. code-block::
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class MyPipeline(TextClassificationPipeline):
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def postprocess(...):
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...
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scores = scores * 100
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...
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my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
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# or if you use `pipeline` function, then:
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my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
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That should enable you to do all the custom code you want.
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Implementing a pipeline
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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:doc:`Implementing a new pipeline <../add_new_pipeline>`
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The task specific pipelines
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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AudioClassificationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.AudioClassificationPipeline
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:special-members: __call__
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:members:
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AutomaticSpeechRecognitionPipeline
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=======================================================================================================================
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.. autoclass:: transformers.AutomaticSpeechRecognitionPipeline
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:special-members: __call__
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:members:
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ConversationalPipeline
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=======================================================================================================================
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.. autoclass:: transformers.Conversation
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.. autoclass:: transformers.ConversationalPipeline
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:special-members: __call__
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:members:
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FeatureExtractionPipeline
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=======================================================================================================================
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.. autoclass:: transformers.FeatureExtractionPipeline
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:special-members: __call__
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:members:
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FillMaskPipeline
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=======================================================================================================================
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.. autoclass:: transformers.FillMaskPipeline
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:special-members: __call__
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:members:
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ImageClassificationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.ImageClassificationPipeline
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:special-members: __call__
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:members:
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ImageSegmentationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.ImageSegmentationPipeline
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:special-members: __call__
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:members:
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NerPipeline
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=======================================================================================================================
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.. autoclass:: transformers.NerPipeline
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See :class:`~transformers.TokenClassificationPipeline` for all details.
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ObjectDetectionPipeline
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=======================================================================================================================
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.. autoclass:: transformers.ObjectDetectionPipeline
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:special-members: __call__
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:members:
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QuestionAnsweringPipeline
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=======================================================================================================================
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.. autoclass:: transformers.QuestionAnsweringPipeline
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:special-members: __call__
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:members:
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SummarizationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.SummarizationPipeline
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:special-members: __call__
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:members:
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TableQuestionAnsweringPipeline
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=======================================================================================================================
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.. autoclass:: transformers.TableQuestionAnsweringPipeline
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:special-members: __call__
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TextClassificationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.TextClassificationPipeline
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:special-members: __call__
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:members:
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TextGenerationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.TextGenerationPipeline
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:special-members: __call__
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:members:
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Text2TextGenerationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.Text2TextGenerationPipeline
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:special-members: __call__
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:members:
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TokenClassificationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.TokenClassificationPipeline
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:special-members: __call__
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:members:
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TranslationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.TranslationPipeline
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:special-members: __call__
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:members:
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ZeroShotClassificationPipeline
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=======================================================================================================================
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.. autoclass:: transformers.ZeroShotClassificationPipeline
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:special-members: __call__
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:members:
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Parent class: :obj:`Pipeline`
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.Pipeline
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:members:
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