Add new modalities

README.md

@@ -4,6 +4,8 @@
 </div>
 
 
+
+
 # SageMaker Hugging Face Inference Toolkit 
 
 [![Latest Version](https://img.shields.io/pypi/v/sagemaker_huggingface_inference_toolkit.svg)](https://pypi.python.org/pypi/sagemaker_huggingface_inference_toolkit) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker_huggingface_inference_toolkit.svg)](https://pypi.python.org/pypi/sagemaker_huggingface_inference_toolkit) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)
@@ -111,7 +113,7 @@ HF_API_TOKEN="api_XXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
 
 ## 🧑🏻‍💻 User defined code/modules
 
-The Hugging Face Inference Toolkit allows user to override the default methods of the `HuggingFaceHandlerService`. Therefor the need to create a named `code/` with a `inference.py` file in it. 
+The Hugging Face Inference Toolkit allows user to override the default methods of the `HuggingFaceHandlerService`. Therefor the need to create a named `code/` with a `inference.py` file in it. You can find an example for it in [sagemaker/17_customer_inference_script](https://github.com/huggingface/notebooks/blob/master/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb)
 For example:  
 ```bash
 model.tar.gz/
@@ -144,3 +146,13 @@ requests to us.
 ## 📜  License
 
 SageMaker Hugging Face Inference Toolkit is licensed under the Apache 2.0 License.
+
+---
+
+## 🧑🏻‍💻 Development Environment
+
+Install all test and development packages with 
+
+```bash
+pip3 install -e ".[test,dev]"
+```

setup.py

@@ -30,22 +30,34 @@
 # We don't declare our dependency on transformers here because we build with
 # different packages for different variants
 
-VERSION = "1.3.1"
+VERSION = "2.0.0"
+
+
+# Ubuntu packages
+# libsndfile1-dev: torchaudio requires the development version of the libsndfile package which can be installed via a system package manager. On Ubuntu it can be installed as follows: apt install libsndfile1-dev
+# ffmpeg: ffmpeg is required for audio processing. On Ubuntu it can be installed as follows: apt install ffmpeg
+# libavcodec-extra : libavcodec-extra  inculdes additional codecs for ffmpeg
 
 install_requires = [
     "sagemaker-inference>=1.5.11",
     "huggingface_hub>=0.0.8",
     "retrying",
     "numpy",
+    # vision
+    "Pillow",
+    # speech + torchaudio
+    "librosa",
+    "pyctcdecode>=0.3.0",
+    "phonemizer",
 ]
 
 extras = {}
 
 # Hugging Face specific dependencies
-extras["transformers"] = ["transformers[sklearn,sentencepiece]>=4.5.1"]
+extras["transformers"] = ["transformers[sklearn,sentencepiece]>=4.17.0"]
 
 # framework specific dependencies
-extras["torch"] = ["torch>=1.8.0"]
+extras["torch"] = ["torch>=1.8.0", "torchaudio"]
 extras["tensorflow"] = ["tensorflow>=2.4.0"]
 
 # MMS Server dependencies

src/sagemaker_huggingface_inference_toolkit/content_types.py

@@ -0,0 +1,37 @@
+# Copyright 2021 The HuggingFace Team, Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This module contains constants that define MIME content types."""
+# Default Mime-Types
+JSON = "application/json"
+CSV = "text/csv"
+OCTET_STREAM = "application/octet-stream"
+ANY = "*/*"
+NPY = "application/x-npy"
+UTF8_TYPES = [JSON, CSV]
+# Vision Mime-Types
+JPEG = "image/jpeg"
+PNG = "image/png"
+TIFF = "image/tiff"
+BMP = "image/bmp"
+GIF = "image/gif"
+WEBP = "image/webp"
+X_IMAGE = "image/x-image"
+VISION_TYPES = [JPEG, PNG, TIFF, BMP, GIF, WEBP, X_IMAGE]
+# Speech Mime-Types
+FLAC = "audio/x-flac"
+MP3 = "audio/mpeg"
+WAV = "audio/wave"
+OGG = "audio/ogg"
+X_AUDIO = "audio/x-audio"
+AUDIO_TYPES = [FLAC, MP3, WAV, OGG, X_AUDIO]

src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py

@@ -11,17 +11,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import base64
 import csv
 import datetime
 import json
-from io import StringIO
+from io import BytesIO, StringIO
 
 import numpy as np
-from sagemaker_inference import content_types, errors
-from sagemaker_inference.decoder import _npy_to_numpy, _npz_to_sparse
+from sagemaker_inference import errors
+from sagemaker_inference.decoder import _npy_to_numpy
 from sagemaker_inference.encoder import _array_to_npy
 
 from mms.service import PredictionException
+from PIL import Image
+from sagemaker_huggingface_inference_toolkit import content_types
 
 
 def decode_json(content):
@@ -51,6 +54,28 @@ def decode_csv(string_like):  # type: (str) -> np.array
         return {"inputs": request_list}
 
 
+def decode_image(bpayload: bytearray):
+    """Convert a .jpeg / .png / .tiff... object to a proper inputs dict.
+    Args:
+        bpayload (bytes): byte stream.
+    Returns:
+        (dict): dictonatry for input
+    """
+    image = Image.open(BytesIO(bpayload)).convert("RGB")
+    return {"inputs": image}
+
+
+def decode_audio(bpayload: bytearray):
+    """Convert a .wav / .flac / .mp3 object to a proper inputs dict.
+    Args:
+        bpayload (bytes): byte stream.
+    Returns:
+        (dict): dictonatry for input
+    """
+
+    return {"inputs": bytes(bpayload)}
+
+
 # https://github.com/automl/SMAC3/issues/453
 class _JSONEncoder(json.JSONEncoder):
     """
@@ -66,6 +91,11 @@ def default(self, obj):
             return obj.tolist()
         elif isinstance(obj, datetime.datetime):
             return obj.__str__()
+        elif isinstance(obj, Image.Image):
+            with BytesIO() as out:
+                obj.save(out, format="PNG")
+                png_string = out.getvalue()
+                return base64.b64encode(png_string).decode("utf-8")
         else:
             return super(_JSONEncoder, self).default(obj)
 
@@ -111,8 +141,21 @@ def encode_csv(content):  # type: (str) -> np.array
 _decoder_map = {
     content_types.NPY: _npy_to_numpy,
     content_types.CSV: decode_csv,
-    content_types.NPZ: _npz_to_sparse,
     content_types.JSON: decode_json,
+    # image mime-types
+    content_types.JPEG: decode_image,
+    content_types.PNG: decode_image,
+    content_types.TIFF: decode_image,
+    content_types.BMP: decode_image,
+    content_types.GIF: decode_image,
+    content_types.WEBP: decode_image,
+    content_types.X_IMAGE: decode_image,
+    # audio mime-types
+    content_types.FLAC: decode_audio,
+    content_types.MP3: decode_audio,
+    content_types.WAV: decode_audio,
+    content_types.OGG: decode_audio,
+    content_types.X_AUDIO: decode_audio,
 }
 
 

src/sagemaker_huggingface_inference_toolkit/handler_service.py

@@ -19,11 +19,11 @@
 import time
 from abc import ABC
 
-from sagemaker_inference import content_types, environment, utils
+from sagemaker_inference import environment, utils
 from transformers.pipelines import SUPPORTED_TASKS
 
 from mms.service import PredictionException
-from sagemaker_huggingface_inference_toolkit import decoder_encoder
+from sagemaker_huggingface_inference_toolkit import content_types, decoder_encoder
 from sagemaker_huggingface_inference_toolkit.transformers_utils import (
     _is_gpu_available,
     get_pipeline,

src/sagemaker_huggingface_inference_toolkit/transformers_utils.py

@@ -255,8 +255,21 @@ def get_pipeline(task: str, device: int, model_dir: Path, **kwargs) -> Pipeline:
         raise EnvironmentError(
             "The task for this model is not set: Please set one: https://huggingface.co/docs#how-is-a-models-type-of-inference-api-and-widget-determined"
         )
+    # define tokenizer or feature extractor as kwargs to load it the pipeline correctly
+    if task in {
+        "automatic-speech-recognition",
+        "image-segmentation",
+        "image-classification",
+        "audio-classification",
+        "object-detection",
+        "zero-shot-image-classification",
+    }:
+        kwargs["feature_extractor"] = model_dir
+    else:
+        kwargs["tokenizer"] = model_dir
 
-    hf_pipeline = pipeline(task=task, model=model_dir, tokenizer=model_dir, device=device, **kwargs)
+    # load pipeline
+    hf_pipeline = pipeline(task=task, model=model_dir, device=device, **kwargs)
 
     # wrapp specific pipeline to support better ux
     if task == "conversational":

tests/integ/config.py

@@ -1,11 +1,14 @@
+import os
+
 from integ.utils import (
+    validate_automatic_speech_recognition,
+    validate_classification,
     validate_feature_extraction,
     validate_fill_mask,
     validate_ner,
     validate_question_answering,
     validate_summarization,
     validate_text2text_generation,
-    validate_text_classification,
     validate_text_generation,
     validate_translation,
     validate_zero_shot_classification,
@@ -53,6 +56,14 @@
         "pytorch": "gpt2",
         "tensorflow": "gpt2",
     },
+    "image-classification": {
+        "pytorch": "google/vit-base-patch16-224",
+        "tensorflow": "google/vit-base-patch16-224",
+    },
+    "automatic-speech-recognition": {
+        "pytorch": "facebook/wav2vec2-base-100h",
+        "tensorflow": "facebook/wav2vec2-base-960h",
+    },
 }
 
 task2input = {
@@ -78,6 +89,8 @@
         "inputs": "question: What is 42 context: 42 is the answer to life, the universe and everything."
     },
     "text-generation": {"inputs": "My name is philipp and I am"},
+    "image-classification": open(os.path.join(os.getcwd(), "tests/resources/image/tiger.jpeg"), "rb").read(),
+    "automatic-speech-recognition": open(os.path.join(os.getcwd(), "tests/resources/audio/sample1.flac"), "rb").read(),
 }
 
 task2output = {
@@ -98,6 +111,16 @@
     "feature-extraction": None,
     "fill-mask": None,
     "text-generation": None,
+    "image-classification": [
+        {"score": 0.8858247399330139, "label": "tiger, Panthera tigris"},
+        {"score": 0.10940514504909515, "label": "tiger cat"},
+        {"score": 0.0006216464680619538, "label": "jaguar, panther, Panthera onca, Felis onca"},
+        {"score": 0.0004262699221726507, "label": "dhole, Cuon alpinus"},
+        {"score": 0.00030842673731967807, "label": "lion, king of beasts, Panthera leo"},
+    ],
+    "automatic-speech-recognition": {
+        "text": "GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP OAUDIENCES IN DROFTY SCHOOL ROOMS DAY AFTER DAY FOR A FORT NIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS"
+    },
 }
 
 task2performance = {
@@ -181,10 +204,26 @@
             "average_request_time": 3,
         },
     },
+    "image-classification": {
+        "cpu": {
+            "average_request_time": 4,
+        },
+        "gpu": {
+            "average_request_time": 1,
+        },
+    },
+    "automatic-speech-recognition": {
+        "cpu": {
+            "average_request_time": 6,
+        },
+        "gpu": {
+            "average_request_time": 6,
+        },
+    },
 }
 
 task2validation = {
-    "text-classification": validate_text_classification,
+    "text-classification": validate_classification,
     "zero-shot-classification": validate_zero_shot_classification,
     "feature-extraction": validate_feature_extraction,
     "ner": validate_ner,
@@ -194,4 +233,6 @@
     "translation_xx_to_yy": validate_translation,
     "text2text-generation": validate_text2text_generation,
     "text-generation": validate_text_generation,
+    "image-classification": validate_classification,
+    "automatic-speech-recognition": validate_automatic_speech_recognition,
 }