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Rework the pipeline tutorial (#20437)

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* [WIP] Rework the pipeline tutorial

- Switch to `asr` instead of another NLP task.
- It also has simpler to understand results.
- Added a section with interaction with `datasets`.
- Added a section with writing a simple webserver.

* Apply suggestions from code review

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Addressing comments.

* Links.

* Fixing docs format.

* Adding pipeline_webserver to _toctree.

* Warnig -> Tip warnings={true}.

* Fix link ?

* Links ?

* Fixing link, adding chunk batching.

* Oops.

* Apply suggestions from code review

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/pipeline_tutorial.mdx

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Apply suggestions from code review

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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Nicolas Patry
2022-12-06 10:47:31 +01:00
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@@ -146,6 +146,8 @@
title: BERTology title: BERTology
- local: perplexity - local: perplexity
title: Perplexity of fixed-length models title: Perplexity of fixed-length models
- local: pipeline_webserver
title: Pipelines for webserver inference
title: Conceptual guides title: Conceptual guides
- sections: - sections:
- sections: - sections:

240
docs/source/en/pipeline_tutorial.mdx
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@@ -33,100 +33,189 @@ While each task has an associated [`pipeline`], it is simpler to use the general
```py ```py
>>> from transformers import pipeline >>> from transformers import pipeline
>>> generator = pipeline(task="text-generation") >>> generator = pipeline(task="automatic-speech-recognition")
``` ```
2. Pass your input text to the [`pipeline`]: 2. Pass your input text to the [`pipeline`]:
```py ```py
>>> generator( >>> generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone" {'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP LIVE UP THE TRUE MEANING OF ITS TREES'}
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
``` ```
If you have more than one input, pass your input as a list: Not the result you had in mind? Check out some of the [most downloaded automatic speech recognition models](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads) on the Hub to see if you can get a better transcription.
Let's try [openai/whisper-large](https://huggingface.co/openai/whisper-large):
```py ```py
>>> generator( >>> generator = pipeline(model="openai/whisper-large")
... [ >>> generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone", {'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
... "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
... ]
... ) # doctest: +SKIP
``` ```
Any additional parameters for your task can also be included in the [`pipeline`]. The `text-generation` task has a [`~generation.GenerationMixin.generate`] method with several parameters for controlling the output. For example, if you want to generate more than one output, set the `num_return_sequences` parameter: Now this result looks more accurate!
We really encourage you to check out the Hub for models in different languages, models specialized in your field, and more.
You can check out and compare model results directly from your browser on the Hub to see if it fits or
handles corner cases better than other ones.
And if you don't find a model for your use case, you can always start [training](training) your own!
If you have several inputs, you can pass your input as a list:
```py ```py
>>> generator( generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone", [
... num_return_sequences=2, "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac",
... ) # doctest: +SKIP "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac",
]
)
``` ```
### Choose a model and tokenizer If you want to iterate over a whole dataset, or want to use it for inference in a webserver, check out dedicated parts
The [`pipeline`] accepts any model from the [Hub](https://huggingface.co/models). There are tags on the Hub that allow you to filter for a model you'd like to use for your task. Once you've picked an appropriate model, load it with the corresponding `AutoModelFor` and [`AutoTokenizer`] class. For example, load the [`AutoModelForCausalLM`] class for a causal language modeling task: [Using pipelines on a dataset](#using-pipelines-on-a-dataset)
[Using pipelines for a webserver](./pipeline_webserver)
## Parameters
[`pipeline`] supports many parameters; some are task specific, and some are general to all pipelines.
In general you can specify parameters anywhere you want:
```py ```py
>>> from transformers import AutoTokenizer, AutoModelForCausalLM generator(model="openai/whisper-large", my_parameter=1)
out = generate(...) # This will use `my_parameter=1`.
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2") out = generate(..., my_parameter=2) # This will override and use `my_parameter=2`.
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2") out = generate(...) # This will go back to using `my_parameter=1`.
``` ```
Create a [`pipeline`] for your task, and specify the model and tokenizer you've loaded: Let's check out 3 important ones:
### Device
If you use `device=n`, the pipeline automatically puts the model on the specified device.
This will work regardless of whether you are using PyTorch or Tensorflow.
```py ```py
>>> from transformers import pipeline generator(model="openai/whisper-large", device=0)
>>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
``` ```
Pass your input text to the [`pipeline`] to generate some text: If the model is too large for a single GPU, you can set `device_map="auto"` to allow 🤗 [Accelerate](https://huggingface.co/docs/accelerate) to automatically determine how to load and store the model weights.
```py ```py
>>> generator( #!pip install accelerate
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone" generator(model="openai/whisper-large", device_map="auto")
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
``` ```
## Audio pipeline ### Batch size
The [`pipeline`] also supports audio tasks like audio classification and automatic speech recognition. By default, pipelines will not batch inference for reasons explained in detail [here](https://huggingface.co/docs/transformers/main_classes/pipelines#pipeline-batching). The reason is that batching is not necessarily faster, and can actually be quite slower in some cases.
For example, let's classify the emotion in this audio clip: But if it works in your use case, you can use:
```py ```py
>>> from datasets import load_dataset generator(model="openai/whisper-large", device=0, batch_size=2)
>>> import torch audio_filenames = [f"audio_{i}.flac" for i in range(10)]
texts = generator(audio_filenames)
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> audio_file = ds[0]["audio"]["path"]
``` ```
Find an [audio classification](https://huggingface.co/models?pipeline_tag=audio-classification) model on the Model Hub for emotion recognition and load it in the [`pipeline`]: This runs the pipeline on the 10 provided audio files, but it will pass them in batches of 2
to the model (which is on a GPU, where batching is more likely to help) without requiring any further code from you.
The output should always match what you would have received without batching. It is only meant as a way to help you get more speed out of a pipeline.
Pipelines can also alleviate some of the complexities of batching because, for some pipelines, a single item (like a long audio file) needs to be chunked into multiple parts to be processed by a model. The pipeline performs this [*chunk batching*](./main_classes/pipelines#pipeline-chunk-batching) for you.
### Task specific parameters
All tasks provide task specific parameters which allow for additional flexibility and options to help you get your job done.
For instance, the [`transformers.AutomaticSpeechRecognitionPipeline.__call__`] method has a `return_timestamps` parameter which sounds promising for subtitling videos:
```py ```py
>>> from transformers import pipeline >>> # Not using whisper, as it cannot provide timestamps.
>>> generator = pipeline(model="facebook/wav2vec2-large-960h-lv60-self", return_timestamps="word")
>>> audio_classifier = pipeline( >>> generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
... task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition" {'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED',
... ) 'chunks': [
{'text': 'I', 'timestamp': (1.22, 1.24)},
{'text': 'HAVE', 'timestamp': (1.42, 1.58)},
{'text': 'A', 'timestamp': (1.66, 1.68)},
{'text': 'DREAM', 'timestamp': (1.76, 2.14)},
{'text': 'BUT', 'timestamp': (3.68, 3.8)},
{'text': 'ONE', 'timestamp': (3.94, 4.06)},
{'text': 'DAY', 'timestamp': (4.16, 4.3)},
{'text': 'THIS', 'timestamp': (6.36, 6.54)},
{'text': 'NATION', 'timestamp': (6.68, 7.1)},
{'text': 'WILL', 'timestamp': (7.32, 7.56)},
{'text': 'RISE', 'timestamp': (7.8, 8.26)},
{'text': 'UP', 'timestamp': (8.38, 8.48)},
{'text': 'AND', 'timestamp': (10.08, 10.18)},
{'text': 'LIVE', 'timestamp': (10.26, 10.48)},
{'text': 'OUT', 'timestamp': (10.58, 10.7)},
{'text': 'THE', 'timestamp': (10.82, 10.9)},
{'text': 'TRUE', 'timestamp': (10.98, 11.18)},
{'text': 'MEANING', 'timestamp': (11.26, 11.58)},
{'text': 'OF', 'timestamp': (11.66, 11.7)},
{'text': 'ITS', 'timestamp': (11.76, 11.88)},
{'text': 'CREED', 'timestamp': (12.0, 12.38)}
]}
``` ```
Pass the audio file to the [`pipeline`]: As you can see, the model inferred the text and also outputted **when** the various words were pronounced
in the sentence.
There are many parameters available for each task, so check out each task's API reference to see what you can tinker with!
For instance, the [`~transformers.AutomaticSpeechRecognitionPipeline`] has a `chunk_length_s` parameter which is helpful for working on really long audio files (for example, subtitling entire movies or hour-long videos) that a model typically cannot handle on its own.
If you can't find a parameter that would really help you out, feel free to [request it](https://github.com/huggingface/transformers/issues/new?assignees=&labels=feature&template=feature-request.yml)!
## Using pipelines on a dataset
The pipeline can also run inference on a large dataset. The easiest way we recommend doing this is by using an iterator:
```py ```py
>>> preds = audio_classifier(audio_file) def data():
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] for i in range(1000):
>>> preds yield f"My example {i}"
[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
pipe = pipe(model="gpt2", device=0)
generated_characters = 0
for out in pipe(data()):
generated_characters += len(out["generated_text"])
``` ```
The iterator `data()` yields each result, and the pipeline automatically
recognizes the input is iterable and will start fetching the data while
it continues to process it on the GPU (this uses [DataLoader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) under the hood).
This is important because you don't have to allocate memory for the whole dataset
and you can feed the GPU as fast as possible.
Since batching could speed things up, it may be useful to try tuning the `batch_size` parameter here.
The simplest way to iterate over a dataset is to just load one from 🤗 [Datasets](https://github.com/huggingface/datasets/):
```py
# KeyDataset is a util that will just output the item we're interested in.
from transformers.pipelines.pt_utils import KeyDataset
pipe = pipeline(model="hf-internal-testing/tiny-random-wav2vec2", device=0)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:10]")
for out in pipe(KeyDataset(dataset["audio"])):
print(out)
```
## Using pipelines for a webserver
<Tip>
Creating an inference engine is a complex topic which deserves it's own
page.
</Tip>
[Link](./pipeline_webserver)
## Vision pipeline ## Vision pipeline
Using a [`pipeline`] for vision tasks is practically identical. Using a [`pipeline`] for vision tasks is practically identical.
@@ -138,7 +227,7 @@ Specify your task and pass your image to the classifier. The image can be a link
```py ```py
>>> from transformers import pipeline >>> from transformers import pipeline
>>> vision_classifier = pipeline(task="image-classification") >>> vision_classifier = pipeline(model="google/vit-base-patch16-224")
>>> preds = vision_classifier( >>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" ... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... ) ... )
@@ -147,25 +236,38 @@ Specify your task and pass your image to the classifier. The image can be a link
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}] [{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
``` ```
## Multimodal pipeline ### Text pipeline
The [`pipeline`] supports more than one modality. For example, a visual question answering (VQA) task combines text and image. Feel free to use any image link you like and a question you want to ask about the image. The image can be a URL or a local path to the image. Using a [`pipeline`] for NLP tasks is practically identical.
For example, if you use the same image from the vision pipeline above:
```py
>>> image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
>>> question = "Where is the cat?"
```
Create a pipeline for `vqa` and pass it the image and question:
```py ```py
>>> from transformers import pipeline >>> from transformers import pipeline
>>> vqa = pipeline(task="vqa") >>> # This model is a `zero-shot-classification` model.
>>> preds = vqa(image=image, question=question) >>> # It will classify text, except you are free to choose any label you might imagine
>>> preds = [{"score": round(pred["score"], 4), "answer": pred["answer"]} for pred in preds] >>> classifier = pipeline(model="facebook/bart-large-mnli")
>>> preds >>> classifier(
[{'score': 0.911, 'answer': 'snow'}, {'score': 0.8786, 'answer': 'in snow'}, {'score': 0.6714, 'answer': 'outside'}, {'score': 0.0293, 'answer': 'on ground'}, {'score': 0.0272, 'answer': 'ground'}] ... "I have a problem with my iphone that needs to be resolved asap!!",
... candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
... )
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!',
'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'],
'scores': [0.504,0.479,0.013,0.003,0.002]}
```
### Multimodal pipeline
The [`pipeline`] supports more than one modality. For example, a visual question answering (VQA) task combines text and image. Feel free to use any image link you like and a question you want to ask about the image. The image can be a URL or a local path to the image.
For example, if you use this [invoice image](https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png):
```py
>>> from transformers import pipeline
>>> vqa = pipeline(model="impira/layoutlm-document-qa")
>>> vqa(
... image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png",
... question="What is the invoice number?",
... )
[{'score': 0.635722279548645, 'answer': '1110212019', 'start': 22, 'end': 22}]
``` ```

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@@ -0,0 +1,161 @@
# Using pipelines for a webserver
<Tip>
Creating an inference engine is a complex topic, and the "best" solution
will most likely depend on your problem space. Are you on CPU or GPU? Do
you want the lowest latency, the highest throughput, support for
many models, or just highly optimize 1 specific model?
There are many ways to tackle this topic, so what we are going to present is a good default
to get started which may not necessarily be the most optimal solution for you.
</Tip>
The key thing to understand is that we can use an iterator, just like you would [on a
dataset](pipeline_tutorial#using-pipelines-on-a-dataset), since a webserver is basically a system that waits for requests and
treats them as they come in.
Usually webservers are multiplexed (multithreaded, async, etc..) to handle various
requests concurrently. Pipelines on the other hand (and mostly the underlying models)
are not really great for parallelism; they take up a lot of RAM, so it's best to give them all the available resources when they are running or it's a compute-intensive job.
We are going to solve that by having the webserver handle the light load of receiving
and sending requests, and having a single thread handling the actual work.
This example is going to use `starlette`. The actual framework is not really
important, but you might have to tune or change the code if you are using another
one to achieve the same effect.
Create `server.py`:
```py
from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route
from transformers import pipeline
import asyncio
async def homepage(request):
payload = await request.body()
string = payload.decode("utf-8")
response_q = asyncio.Queue()
await request.app.model_queue.put((string, response_q))
output = await response_q.get()
return JSONResponse(output)
async def server_loop(q):
pipe = pipeline(model="bert-base-uncased")
while True:
(string, response_q) = await q.get()
out = pipe(string)
await response_q.put(out)
app = Starlette(
routes=[
Route("/", homepage, methods=["POST"]),
],
)
@app.on_event("startup")
async def startup_event():
q = asyncio.Queue()
app.model_queue = q
asyncio.create_task(server_loop(q))
```
Now you can start it with:
```bash
uvicorn server:app
```
And you can query it:
```bash
curl -X POST -d "test [MASK]" http://localhost:8000/
#[{"score":0.7742936015129089,"token":1012,"token_str":".","sequence":"test."},...]
```
And there you go, now you have a good idea of how to create a webserver!
What is really important is that we load the model only **once**, so there are no copies
of the model on the webserver. This way, no unnecessary RAM is being used.
Then the queuing mechanism allows you to do fancy stuff like maybe accumulating a few
items before inferring to use dynamic batching:
```py
(string, rq) = await q.get()
strings = []
queues = []
while True:
try:
(string, rq) = await asyncio.wait_for(q.get(), timeout=0.001) # 1ms
except asyncio.exceptions.TimeoutError:
break
strings.append(string)
queues.append(rq)
strings
outs = pipe(strings, batch_size=len(strings))
for (rq, out) in zip(queues, outs):
await rq.put(out)
```
<Tip warning={true}>
Do not activate this without checking it makes sense for your load!
</Tip>
The proposed code is optimized for readability, not for being the best code.
First of all, there's no batch size limit which is usually not a
great idea. Next, the timeout is reset on every queue fetch, meaning you could
wait much more than 1ms before running the inference (delaying the first request
by that much).
It would be better to have a single 1ms deadline.
This will always wait for 1ms even if the queue is empty, which might not be the
best since you probably want to start doing inference if there's nothing in the queue.
But maybe it does make sense if batching is really crucial for your use case.
Again, there's really no one best solution.
## Few things you might want to consider
### Error checking
There's a lot that can go wrong in production: out of memory, out of space,
loading the model might fail, the query might be wrong, the query might be
correct but still fail to run because of a model misconfiguration, and so on.
Generally, it's good if the server outputs the errors to the user, so
adding a lot of `try..except` statements to show those errors is a good
idea. But keep in mind it may also be a security risk to reveal all those errors depending
on your security context.
### Circuit breaking
Webservers usually look better when they do circuit breaking. It means they
return proper errors when they're overloaded instead of just waiting for the query indefinitely. Return a 503 error instead of waiting for a super long time or a 504 after a long time.
This is relatively easy to implement in the proposed code since there is a single queue.
Looking at the queue size is a basic way to start returning errors before your
webserver fails under load.
### Blocking the main thread
Currently PyTorch is not async aware, and computation will block the main
thread while running. That means it would be better if PyTorch was forced to run
on its own thread/process. This wasn't done here because the code is a lot more
complex (mostly because threads and async and queues don't play nice together).
But ultimately it does the same thing.
This would be important if the inference of single items were long (> 1s) because
in this case, it means every query during inference would have to wait for 1s before
even receiving an error.
### Dynamic batching
In general, batching is not necessarily an improvement over passing 1 item at
a time (see [batching details](./main_classes/pipelines#pipeline-batching) for more information). But it can be very effective
when used in the correct setting. In the API, there is no dynamic
batching by default (too much opportunity for a slowdown). But for BLOOM inference -
which is a very large model - dynamic batching is **essential** to provide a decent experience for everyone.