Remove accelerate and onnxruntime from required dependencies

.github/workflows/test_openvino.yml

@@ -32,7 +32,7 @@ jobs:
         python -m pip install --upgrade pip
         # install PyTorch CPU version to avoid installing CUDA packages on GitHub runner without GPU
         pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install .[openvino,openvino-tokenizers,nncf,tests,diffusers]
+        pip install .[openvino,openvino-tokenizers,tests,diffusers] onnxruntime
     - name: Test with Pytest
       run: |
         pytest tests/openvino/ --ignore test_modeling_basic

optimum/intel/__init__.py

@@ -18,6 +18,7 @@
 from transformers.utils import OptionalDependencyNotAvailable, _LazyModule
 
 from .utils import (
+    is_accelerate_available,
     is_diffusers_available,
     is_ipex_available,
     is_neural_compressor_available,
@@ -57,13 +58,19 @@
     if not (is_openvino_available() and is_nncf_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    _import_structure["utils.dummy_openvino_and_nncf_objects"] = [
-        "OVQuantizer",
-        "OVTrainer",
-        "OVTrainingArguments",
-    ]
+    _import_structure["utils.dummy_openvino_and_nncf_objects"] = ["OVQuantizer", "OVTrainingArguments"]
+else:
+    _import_structure["openvino"].extend(["OVQuantizer", "OVTrainingArguments"])
+
+
+try:
+    if not (is_openvino_available() and is_nncf_available() and is_accelerate_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    _import_structure["utils.dummy_openvino_and_nncf_objects"] = ["OVTrainer"]
 else:
-    _import_structure["openvino"].extend(["OVQuantizer", "OVTrainer", "OVTrainingArguments"])
+    _import_structure["openvino"].extend(["OVTrainer"])
+
 
 try:
     if not (is_openvino_available() and is_diffusers_available()):
@@ -177,13 +184,17 @@
         if not (is_openvino_available() and is_nncf_available()):
             raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
-        from .utils.dummy_openvino_and_nncf_objects import (
-            OVQuantizer,
-            OVTrainer,
-            OVTrainingArguments,
-        )
+        from .utils.dummy_openvino_and_nncf_objects import OVQuantizer, OVTrainingArguments
+    else:
+        from .openvino import OVQuantizer, OVTrainingArguments
+
+    try:
+        if not (is_openvino_available() and is_nncf_available() and is_accelerate_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from .utils.dummy_openvino_and_nncf_objects import OVTrainer
     else:
-        from .openvino import OVQuantizer, OVTrainer, OVTrainingArguments
+        from .openvino import OVTrainer
 
     try:
         if not (is_openvino_available() and is_diffusers_available()):

optimum/intel/openvino/__init__.py

@@ -14,7 +14,7 @@
 
 import logging
 
-from ..utils.import_utils import is_diffusers_available, is_nncf_available
+from ..utils.import_utils import is_accelerate_available, is_diffusers_available, is_nncf_available
 from .utils import (
     OV_DECODER_NAME,
     OV_DECODER_WITH_PAST_NAME,
@@ -37,9 +37,11 @@
     patch_torch_operators()
 
     from .quantization import OVQuantizer
-    from .trainer import OVTrainer
     from .training_args import OVTrainingArguments
 
+    if is_accelerate_available():
+        from .trainer import OVTrainer
+
 
 from .configuration import OVConfig, OVWeightQuantizationConfig
 from .modeling import (

optimum/intel/openvino/quantization.py

@@ -17,7 +17,7 @@
 import logging
 import os
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import nncf
 import openvino
@@ -56,8 +56,7 @@
 
 
 if is_datasets_available():
-    if TYPE_CHECKING:
-        from datasets import Dataset
+    from datasets import Dataset
 
 register_module(ignored_algorithms=[])(Conv1D)
 
@@ -147,6 +146,7 @@ def __init__(self, model: transformers.PreTrainedModel, task: Optional[str] = No
             )
         self.task = task or feature
         self.seed = seed
+        # TODO : deprecate input_names
         self.input_names = None
         signature = inspect.signature(self.model.forward)
         self._signature_columns = list(signature.parameters.keys())
@@ -526,9 +526,15 @@ def _get_calibration_dataloader(
         data_collator: Optional[DataCollator] = None,
     ) -> OVDataLoader:
         data_collator = data_collator if data_collator is not None else default_data_collator
+
+        if not is_datasets_available() or not isinstance(calibration_dataset, Dataset):
+            logger.warning(
+                "`remove_unused_columns` set to `False` as calibration_dataset is not an instance of `datasets.Dataset`"
+            )
+            remove_unused_columns = False
+
         if remove_unused_columns:
             calibration_dataset = self._remove_unused_columns(calibration_dataset)
-        self.input_names = calibration_dataset.column_names
         generator = torch.Generator()
         generator.manual_seed(self.seed)
         sampler = RandomSampler(calibration_dataset, generator=generator)

optimum/intel/utils/__init__.py

@@ -16,6 +16,7 @@
     _neural_compressor_version,
     _torch_version,
     compare_versions,
+    is_accelerate_available,
     is_diffusers_available,
     is_ipex_available,
     is_neural_compressor_available,

optimum/intel/utils/dummy_openvino_and_nncf_objects.py

@@ -27,14 +27,14 @@ def from_pretrained(cls, *args, **kwargs):
 
 
 class OVTrainer(metaclass=DummyObject):
-    _backends = ["openvino", "nncf"]
+    _backends = ["openvino", "nncf", "accelerate"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["openvino", "nncf"])
+        requires_backends(self, ["openvino", "nncf", "accelerate"])
 
     @classmethod
     def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["openvino", "nncf"])
+        requires_backends(cls, ["openvino", "nncf", "accelerate"])
 
 
 class OVQuantizer(metaclass=DummyObject):

optimum/intel/utils/import_utils.py

@@ -156,6 +156,16 @@
         _datasets_available = False
 
 
+_accelerate_available = importlib.util.find_spec("accelerate") is not None
+_accelerate_version = "N/A"
+
+if _accelerate_available:
+    try:
+        _accelerate_version = importlib_metadata.version("accelerate")
+    except importlib_metadata.PackageNotFoundError:
+        _accelerate_available = False
+
+
 def is_transformers_available():
     return _transformers_available
 
@@ -196,6 +206,10 @@ def is_datasets_available():
     return _datasets_available
 
 
+def is_accelerate_available():
+    return _accelerate_available
+
+
 # This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319
 def compare_versions(library_or_version: Union[str, Version], operation: str, requirement_version: str):
     """
@@ -317,13 +331,19 @@ def is_timm_version(operation: str, version: str):
 `pip install datasets`. Please note that you may need to restart your runtime after installation.
 """
 
+ACCELERATE_IMPORT_ERROR = """
+{0} requires the accelerate library but it was not found in your environment. You can install it with pip:
+`pip install accelerate`. Please note that you may need to restart your runtime after installation.
+"""
+
 BACKENDS_MAPPING = OrderedDict(
     [
         ("diffusers", (is_diffusers_available, DIFFUSERS_IMPORT_ERROR)),
         ("ipex", (is_ipex_available, IPEX_IMPORT_ERROR)),
         ("nncf", (is_nncf_available, NNCF_IMPORT_ERROR)),
         ("openvino", (is_openvino_available, OPENVINO_IMPORT_ERROR)),
         ("neural_compressor", (is_neural_compressor_available, NEURAL_COMPRESSOR_IMPORT_ERROR)),
+        ("accelerate", (is_accelerate_available, ACCELERATE_IMPORT_ERROR)),
     ]
 )
 

setup.py

@@ -18,10 +18,10 @@
     "datasets>=1.4.0",
     "sentencepiece",
     "scipy",
-    "accelerate",  # transformers 4.29 require accelerate for PyTorch
 ]
 
 TESTS_REQUIRE = [
+    "accelerate",
     "pytest",
     "parameterized",
     "Pillow",
@@ -39,8 +39,8 @@
 QUALITY_REQUIRE = ["black~=23.1", "ruff>=0.0.241"]
 
 EXTRAS_REQUIRE = {
-    "neural-compressor": ["neural-compressor>=2.2.0", "onnx", "onnxruntime<1.15.0"],
-    "openvino": ["openvino>=2023.3", "onnx", "onnxruntime", "nncf>=2.8.1"],
+    "neural-compressor": ["neural-compressor>=2.2.0", "onnx", "onnxruntime<1.15.0", "accelerate"],
+    "openvino": ["openvino>=2023.3", "onnx", "nncf>=2.8.1"],
     "openvino-tokenizers": ["openvino-tokenizers[transformers]"],
     "nncf": ["nncf>=2.8.1"],
     "ipex": ["intel-extension-for-pytorch", "onnx"],

tests/openvino/test_stable_diffusion.py

@@ -28,7 +28,6 @@
 from diffusers.utils import load_image
 from diffusers.utils.testing_utils import floats_tensor
 from openvino.runtime.ie_api import CompiledModel
-from packaging.version import Version, parse
 from parameterized import parameterized
 from utils_tests import MODEL_NAMES, SEED
 
@@ -46,13 +45,8 @@
     OVModelVaeDecoder,
     OVModelVaeEncoder,
 )
-from optimum.onnxruntime import (
-    ORTStableDiffusionImg2ImgPipeline,
-    ORTStableDiffusionInpaintPipeline,
-    ORTStableDiffusionXLImg2ImgPipeline,
-    ORTStableDiffusionXLPipeline,
-)
-from optimum.utils.import_utils import _diffusers_version
+from optimum.intel.utils.import_utils import is_diffusers_version
+from optimum.utils.import_utils import is_onnxruntime_available
 
 
 F32_CONFIG = {"INFERENCE_PRECISION_HINT": "f32"}
@@ -167,7 +161,6 @@ def generate_inputs(self, height=128, width=128, batch_size=1):
 class OVStableDiffusionImg2ImgPipelineTest(OVStableDiffusionPipelineBaseTest):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion",)
     MODEL_CLASS = OVStableDiffusionImg2ImgPipeline
-    ORT_MODEL_CLASS = ORTStableDiffusionImg2ImgPipeline
     TASK = "image-to-image"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
@@ -298,11 +291,13 @@ def test_height_width_properties(self, model_arch: str):
 class OVStableDiffusionInpaintPipelineTest(OVStableDiffusionPipelineBaseTest):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion",)
     MODEL_CLASS = OVStableDiffusionInpaintPipeline
-    ORT_MODEL_CLASS = ORTStableDiffusionInpaintPipeline
     TASK = "inpaint"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    @unittest.skipIf(not is_onnxruntime_available(), "this test requires onnxruntime")
     def test_compare_diffusers_pipeline(self, model_arch: str):
+        from optimum.onnxruntime import ORTStableDiffusionInpaintPipeline
+
         model_id = MODEL_NAMES[model_arch]
         pipeline = self.MODEL_CLASS.from_pretrained(model_id, export=True, ov_config=F32_CONFIG)
         batch_size, num_images, height, width = 1, 1, 64, 64
@@ -329,7 +324,7 @@ def test_compare_diffusers_pipeline(self, model_arch: str):
         outputs = pipeline(**inputs, latents=latents).images
         self.assertEqual(outputs.shape, (batch_size * num_images, height, width, 3))
 
-        ort_pipeline = self.ORT_MODEL_CLASS.from_pretrained(model_id, export=True)
+        ort_pipeline = ORTStableDiffusionInpaintPipeline.from_pretrained(model_id, export=True)
         ort_outputs = ort_pipeline(**inputs, latents=latents).images
         self.assertTrue(np.allclose(outputs, ort_outputs, atol=1e-1))
 
@@ -358,7 +353,6 @@ def generate_inputs(self, height=128, width=128, batch_size=1):
 class OVtableDiffusionXLPipelineTest(unittest.TestCase):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion-xl",)
     MODEL_CLASS = OVStableDiffusionXLPipeline
-    ORT_MODEL_CLASS = ORTStableDiffusionXLPipeline
     PT_MODEL_CLASS = StableDiffusionXLPipeline
     TASK = "text-to-image"
 
@@ -444,7 +438,6 @@ def test_num_images_per_prompt_static_model(self, model_arch: str):
 class OVStableDiffusionXLImg2ImgPipelineTest(unittest.TestCase):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion-xl", "stable-diffusion-xl-refiner")
     MODEL_CLASS = OVStableDiffusionXLImg2ImgPipeline
-    ORT_MODEL_CLASS = ORTStableDiffusionXLImg2ImgPipeline
     PT_MODEL_CLASS = StableDiffusionXLImg2ImgPipeline
     TASK = "image-to-image"
 
@@ -489,7 +482,7 @@ class OVLatentConsistencyModelPipelineTest(unittest.TestCase):
     TASK = "text-to-image"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
-    @unittest.skipIf(parse(_diffusers_version) <= Version("0.21.4"), "not supported with this diffusers version")
+    @unittest.skipIf(is_diffusers_version("<=", "0.21.4"), "not supported with this diffusers version")
     def test_compare_to_diffusers(self, model_arch: str):
         ov_pipeline = self.MODEL_CLASS.from_pretrained(MODEL_NAMES[model_arch], export=True, ov_config=F32_CONFIG)
         self.assertIsInstance(ov_pipeline.text_encoder, OVModelTextEncoder)
@@ -532,7 +525,7 @@ def test_compare_to_diffusers(self, model_arch: str):
         self.assertEqual(pipeline.device.type, ov_pipeline.device)
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
-    @unittest.skipIf(parse(_diffusers_version) <= Version("0.21.4"), "not supported with this diffusers version")
+    @unittest.skipIf(is_diffusers_version("<=", "0.21.4"), "not supported with this diffusers version")
     def test_num_images_per_prompt_static_model(self, model_arch: str):
         model_id = MODEL_NAMES[model_arch]
         pipeline = self.MODEL_CLASS.from_pretrained(model_id, export=True, compile=False, dynamic_shapes=False)