Update imports in examples/notebooks

.github/workflows/test_openvino_examples.yml

@@ -7,11 +7,11 @@ on:
   push:
     paths:
     - '.github/workflows/test_openvino_examples.yml'
-    - 'examples/openvino/*'
+    - 'examples/openvino/**'
   pull_request:
     paths:
     - '.github/workflows/test_openvino_examples.yml'
-    - 'examples/openvino/*'
+    - 'examples/openvino/**'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
@@ -22,9 +22,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.8", "3.11"]
 
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
 
     steps:
     - uses: actions/checkout@v2
@@ -35,12 +35,12 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install optimum[openvino] jstyleson nncf pytest
-        pip install -r examples/openvino/audio-classification/requirements.txt
-        pip install -r examples/openvino/image-classification/requirements.txt
-        pip install -r examples/openvino/question-answering/requirements.txt
-        pip install -r examples/openvino/text-classification/requirements.txt
+        pip install .[openvino] jstyleson pytest
+        pip install -r examples/openvino/audio-classification/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r examples/openvino/image-classification/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r examples/openvino/question-answering/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r examples/openvino/text-classification/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
 
     - name: Test examples
       run: |
-        python -m pytest examples/openvino/test_examples.py
+        python -m pytest examples/openvino/test_examples.py

.github/workflows/test_openvino_notebooks.yml

@@ -23,9 +23,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.8", "3.11"]
 
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
 
     steps:
     - uses: actions/checkout@v2

examples/openvino/audio-classification/requirements.txt

@@ -1,4 +1,5 @@
 datasets>=1.14.0
 evaluate
 librosa
-torchaudio
+torchaudio
+accelerate

examples/openvino/audio-classification/run_audio_classification.py

@@ -35,7 +35,7 @@
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
-from optimum.intel.openvino import OVConfig, OVTrainer, OVTrainingArguments
+from optimum.intel import OVConfig, OVTrainer, OVTrainingArguments
 
 
 logger = logging.getLogger(__name__)

examples/openvino/image-classification/requirements.txt

@@ -2,3 +2,4 @@ datasets >= 1.8.0
 torch >= 1.9.0
 torchvision>=0.6.0
 evaluate
+accelerate

examples/openvino/image-classification/run_image_classification.py

@@ -52,7 +52,7 @@
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
-from optimum.intel.openvino import OVConfig, OVTrainer, OVTrainingArguments
+from optimum.intel import OVConfig, OVTrainer, OVTrainingArguments
 
 
 logger = logging.getLogger(__name__)

examples/openvino/question-answering/requirements.txt

@@ -1,3 +1,4 @@
 datasets >= 1.8.0
 torch >= 1.9.0
 evaluate
+accelerate

examples/openvino/question-answering/run_qa.py

@@ -49,7 +49,7 @@
 from transformers.utils.versions import require_version
 from utils_qa import postprocess_qa_predictions
 
-from optimum.intel.openvino import OVConfig, OVTrainingArguments
+from optimum.intel import OVConfig, OVTrainingArguments
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/openvino/question-answering/trainer_qa.py

@@ -20,7 +20,7 @@
 import torch.nn.functional as F
 from transformers.trainer_utils import PredictionOutput
 
-from optimum.intel.openvino.trainer import OVTrainer
+from optimum.intel import OVTrainer
 
 
 class QuestionAnsweringOVTrainer(OVTrainer):

examples/openvino/text-classification/requirements.txt

@@ -4,4 +4,5 @@ scipy
 scikit-learn
 protobuf
 torch >= 1.3
-evaluate
+evaluate
+accelerate

examples/openvino/text-classification/run_glue.py

@@ -46,7 +46,7 @@
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
-from optimum.intel.openvino import OVConfig, OVTrainer, OVTrainingArguments
+from optimum.intel import OVConfig, OVTrainer, OVTrainingArguments
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

notebooks/openvino/optimum_openvino_inference.ipynb

@@ -76,7 +76,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForQuestionAnswering\n",
+    "from optimum.intel import OVModelForQuestionAnswering\n",
     "\n",
     "# Load PyTorch model from the Hub and export to OpenVINO in the background\n",
     "model = OVModelForQuestionAnswering.from_pretrained(\"distilbert-base-uncased-distilled-squad\", export=True)\n",
@@ -182,7 +182,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForQuestionAnswering\n",
+    "from optimum.intel import OVModelForQuestionAnswering\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model = OVModelForQuestionAnswering.from_pretrained(\"distilbert-base-uncased-distilled-squad-ov-fp32\")\n",
@@ -240,7 +240,7 @@
    ],
    "source": [
     "import torch\n",
-    "from optimum.intel.openvino import OVModelForQuestionAnswering\n",
+    "from optimum.intel import OVModelForQuestionAnswering\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model = OVModelForQuestionAnswering.from_pretrained(\"distilbert-base-uncased-distilled-squad-ov-fp32\")\n",
@@ -324,7 +324,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForQuestionAnswering\n",
+    "from optimum.intel import OVModelForQuestionAnswering\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model = OVModelForQuestionAnswering.from_pretrained(\n",
@@ -529,7 +529,7 @@
    ],
    "source": [
     "from IPython.display import Audio\n",
-    "from optimum.intel.openvino import OVModelForAudioClassification\n",
+    "from optimum.intel import OVModelForAudioClassification\n",
     "from transformers import AutoFeatureExtractor, pipeline\n",
     "from datasets import load_dataset\n",
     "\n",
@@ -638,7 +638,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForCausalLM\n",
+    "from optimum.intel import OVModelForCausalLM\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model_id = \"helenai/gpt2-ov\"\n",
@@ -704,7 +704,7 @@
    ],
    "source": [
     "from IPython.display import Image\n",
-    "from optimum.intel.openvino import OVModelForImageClassification\n",
+    "from optimum.intel import OVModelForImageClassification\n",
     "from transformers import AutoImageProcessor, pipeline\n",
     "\n",
     "model_id = \"helenai/microsoft-swin-tiny-patch4-window7-224-ov\"\n",
@@ -766,7 +766,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForMaskedLM\n",
+    "from optimum.intel import OVModelForMaskedLM\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model_id = \"helenai/bert-base-uncased-ov\"\n",
@@ -835,7 +835,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForQuestionAnswering\n",
+    "from optimum.intel import OVModelForQuestionAnswering\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "# Load the model and tokenizer saved in Part 1 of this notebook. Or use the line below to load them from the hub\n",
@@ -890,7 +890,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForSeq2SeqLM\n",
+    "from optimum.intel import OVModelForSeq2SeqLM\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model_id = \"helenai/t5-small-ov\"\n",
@@ -998,7 +998,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForSequenceClassification\n",
+    "from optimum.intel import OVModelForSequenceClassification\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model_id = \"helenai/papluca-xlm-roberta-base-language-detection-ov\"\n",
@@ -1047,7 +1047,7 @@
     }
    ],
    "source": [
-    "from optimum.intel.openvino import OVModelForTokenClassification\n",
+    "from optimum.intel import OVModelForTokenClassification\n",
     "from transformers import AutoTokenizer, pipeline\n",
     "\n",
     "model_id = \"helenai/dslim-bert-base-NER-ov-fp32\"\n",

notebooks/openvino/question_answering_quantization.ipynb

@@ -51,7 +51,7 @@
     "import transformers\n",
     "from evaluate import evaluator\n",
     "from openvino.runtime import Core\n",
-    "from optimum.intel.openvino import OVModelForQuestionAnswering, OVQuantizer, OVQuantizationConfig, OVConfig\n",
+    "from optimum.intel import OVModelForQuestionAnswering, OVQuantizer, OVQuantizationConfig, OVConfig\n",
     "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\n",
     "\n",
     "transformers.logging.set_verbosity_error()\n",
@@ -286,7 +286,7 @@
     "**NOTE:** if you notice very low accuracy after post-training quantization, it is likely caused by an overflow issue which affects processors that do not contain VNNI (Vector Neural Network Instruction). NNCF has an `overflow_fix` option to address this. It will effectively use 7-bits for quantizing instead of 8-bits to prevent the overflow. To use this option, modify the code in the next cell to add an explicit quantization configuration, and set `overflow_fix` to `\"enable\"`:\n",
     "\n",
     "```\n",
-    "from optimum.intel.openvino import OVConfig, OVQuantizationConfig\n",
+    "from optimum.intel import OVConfig, OVQuantizationConfig\n",
     "\n",
     "ov_config = OVConfig(quantization_config=OVQuantizationConfig(overflow_fix=\"enable\")\n",
     "quantizer = OVQuantizer.from_pretrained(model)\n",

notebooks/openvino/requirements.txt

@@ -1,4 +1,4 @@
-optimum-intel[openvino, nncf]
+optimum-intel[openvino]
 datasets
 evaluate[evaluator]
 ipywidgets

notebooks/openvino/stable_diffusion_optimization.ipynb

@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from optimum.intel.openvino import OVStableDiffusionPipeline\n",
+    "from optimum.intel import OVStableDiffusionPipeline\n",
     "from diffusers.training_utils import set_seed\n",
     "from IPython.display import display"
    ]

optimum/intel/openvino/modeling_seq2seq.py

@@ -224,7 +224,7 @@
 
     ```python
     >>> from transformers import {processor_class}
-    >>> from optimum.intel.openvino import {model_class}
+    >>> from optimum.intel import {model_class}
     >>> from datasets import load_dataset
 
     >>> processor = {processor_class}.from_pretrained("{checkpoint}")
@@ -241,7 +241,7 @@
 
     ```python
     >>> from transformers import {processor_class}, pipeline
-    >>> from optimum.intel.openvino import {model_class}
+    >>> from optimum.intel import {model_class}
     >>> from datasets import load_dataset
 
     >>> processor = {processor_class}.from_pretrained("{checkpoint}")

optimum/intel/openvino/quantization.py

@@ -235,7 +235,7 @@ def quantize(
 
         Examples:
         ```python
-        >>> from optimum.intel.openvino import OVQuantizer, OVModelForCausalLM
+        >>> from optimum.intel import OVQuantizer, OVModelForCausalLM
         >>> from transformers import AutoModelForCausalLM
         >>> model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b")
         >>> quantizer = OVQuantizer.from_pretrained(model, task="text-generation")
@@ -245,7 +245,7 @@ def quantize(
         ```
 
         ```python
-        >>> from optimum.intel.openvino import OVQuantizer, OVModelForSequenceClassification
+        >>> from optimum.intel import OVQuantizer, OVModelForSequenceClassification
         >>> from transformers import AutoModelForSequenceClassification
         >>> model = OVModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english", export=True)
         >>> # or