Merge branch 'main' into ea/pt_fe

docs/Dockerfile

@@ -18,7 +18,7 @@ RUN apt-get update && apt-get install -y \
 RUN apt-get update && apt-get install -y \
     software-properties-common \
     npm
-RUN npm install npm@latest -g && \
+RUN npm install npm@9.8.1 -g && \
     npm install n -g && \
     n latest
 

optimum/commands/neural_compressor/quantize.py

@@ -28,7 +28,7 @@ def parse_args_inc_quantize(parser: "ArgumentParser"):
     required_group = parser.add_argument_group("Required arguments")
     required_group.add_argument(
         "--model",
-        type=Path,
+        type=str,
         required=True,
         help="Path to the repository where the model to quantize is located.",
     )
@@ -46,7 +46,7 @@ def parse_args_inc_quantize(parser: "ArgumentParser"):
         default="auto",
         help=(
             "The task to export the model for. If not specified, the task will be auto-inferred based on the model. Available tasks depend on the model, but are among:"
-            f" {str(list(TasksManager._TASKS_TO_AUTOMODELS.keys()))}."
+            f" {str(TasksManager.get_all_tasks())}."
         ),
     )
 
@@ -83,7 +83,7 @@ def run(self):
 
         if task == "auto":
             try:
-                task = TasksManager.infer_task_from_model(str(model_id))
+                task = TasksManager.infer_task_from_model(model_id)
             except Exception as e:
                 return (
                     f"### Error: {e}. Please pass explicitely the task as it could not be infered.",

optimum/intel/neural_compressor/modeling_base.py

@@ -54,7 +54,6 @@
     """,
 )
 class INCBaseModel:
-    _AUTOMODELS_TO_TASKS = {cls_name: task for task, cls_name in TasksManager._TASKS_TO_AUTOMODELS.items()}
     base_model_prefix = "inc_model"
 
     def __init__(
@@ -256,12 +255,5 @@ def _from_transformers(
             **kwargs,
         )
 
-    @classmethod
-    def _auto_model_to_task(cls, auto_model_class):
-        """
-        Get the task corresponding to a class (for example AutoModelForXXX in transformers).
-        """
-        return cls._AUTOMODELS_TO_TASKS[auto_model_class.__name__]
-
     def eval(self):
         self.model.eval()

optimum/intel/openvino/modeling_base.py

@@ -66,7 +66,6 @@ class PreTrainedModel(OptimizedModel):
     """,
 )
 class OVBaseModel(PreTrainedModel):
-    _AUTOMODELS_TO_TASKS = {cls_name: task for task, cls_name in TasksManager._TASKS_TO_AUTOMODELS.items()}
     auto_model_class = None
     export_feature = None
 
@@ -391,13 +390,6 @@ def _ensure_supported_device(self, device: str = None):
     def forward(self, *args, **kwargs):
         raise NotImplementedError
 
-    @classmethod
-    def _auto_model_to_task(cls, auto_model_class):
-        """
-        Get the task corresponding to a class (for example AutoModelForXXX in transformers).
-        """
-        return cls._AUTOMODELS_TO_TASKS[auto_model_class.__name__]
-
     def can_generate(self) -> bool:
         """
         Returns whether this model can generate sequences with `.generate()`.

optimum/intel/openvino/modeling_diffusion.py

@@ -30,7 +30,7 @@
     StableDiffusionXLPipeline,
 )
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
-from diffusers.utils import CONFIG_NAME
+from diffusers.utils import CONFIG_NAME, is_invisible_watermark_available
 from huggingface_hub import snapshot_download
 from openvino._offline_transformations import compress_model_transformation
 from openvino.runtime import Core
@@ -41,6 +41,7 @@
 from optimum.pipelines.diffusers.pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipelineMixin
 from optimum.pipelines.diffusers.pipeline_stable_diffusion_xl import StableDiffusionXLPipelineMixin
 from optimum.pipelines.diffusers.pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipelineMixin
+from optimum.pipelines.diffusers.pipeline_utils import VaeImageProcessor
 from optimum.utils import (
     DIFFUSION_MODEL_TEXT_ENCODER_2_SUBFOLDER,
     DIFFUSION_MODEL_TEXT_ENCODER_SUBFOLDER,
@@ -106,6 +107,8 @@ def __init__(
         else:
             self.vae_scale_factor = 8
 
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
         self.tokenizer = tokenizer
         self.tokenizer_2 = tokenizer_2
         self.scheduler = scheduler
@@ -687,12 +690,21 @@ class OVStableDiffusionXLPipelineBase(OVStableDiffusionPipelineBase):
     auto_model_class = StableDiffusionXLPipeline
     export_feature = "stable-diffusion-xl"
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, add_watermarker: Optional[bool] = None, **kwargs):
         super().__init__(*args, **kwargs)
-        # additional invisible-watermark dependency for SD XL
-        from optimum.pipelines.diffusers.watermark import StableDiffusionXLWatermarker
 
-        self.watermark = StableDiffusionXLWatermarker()
+        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+        if add_watermarker:
+            if not is_invisible_watermark_available():
+                raise ImportError(
+                    "`add_watermarker` requires invisible-watermark to be installed, which can be installed with `pip install invisible-watermark`."
+                )
+            from optimum.pipelines.diffusers.watermark import StableDiffusionXLWatermarker
+
+            self.watermark = StableDiffusionXLWatermarker()
+        else:
+            self.watermark = None
 
 
 class OVStableDiffusionXLPipeline(OVStableDiffusionXLPipelineBase, StableDiffusionXLPipelineMixin):

optimum/intel/version.py

@@ -12,4 +12,4 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-__version__ = "1.10.2.dev0"
+__version__ = "1.12.0.dev0"

setup.py

@@ -12,7 +12,7 @@
     assert False, "Error: Could not open '%s' due %s\n" % (filepath, error)
 
 INSTALL_REQUIRE = [
-    "optimum>=1.10.0",
+    "optimum>=1.13.0",
     "transformers>=4.20.0",
     "datasets>=1.4.0",
     "sentencepiece",
@@ -31,6 +31,7 @@
     "torchaudio",
     "rjieba",
     "timm",
+    "invisible-watermark>=0.2.0",
 ]
 
 QUALITY_REQUIRE = ["black~=23.1", "ruff>=0.0.241"]
@@ -44,7 +45,7 @@
     "openvino": ["openvino==2023.1.0.dev20230811", "onnx", "onnxruntime"],
     "nncf": ["nncf @ git+https://github.com/openvinotoolkit/nncf.git@release_v260"],
     "ipex": ["transformers<4.32.0", "intel-extension-for-pytorch", "onnx"],
-    "diffusers": ["diffusers", "invisible-watermark>=0.2.0"],
+    "diffusers": ["diffusers"],
     "quality": QUALITY_REQUIRE,
     "tests": TESTS_REQUIRE,
 }

tests/openvino/test_modeling.py

@@ -452,8 +452,9 @@ class OVModelForCausalLMIntegrationTest(unittest.TestCase):
         "gpt2",
         "gpt_neo",
         "gpt_neox",
-        # "llama",
+        "llama",
         "marian",
+        "mpt",
         "opt",
         "pegasus",
     )

tests/openvino/test_stable_diffusion.py

@@ -18,6 +18,7 @@
 from typing import Dict
 
 import numpy as np
+import PIL
 import torch
 from diffusers import (
     StableDiffusionPipeline,
@@ -60,17 +61,32 @@ def _generate_inputs(batch_size=1):
     return inputs
 
 
-def _create_image(height=128, width=128):
-    image = load_image(
-        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        "/in_paint/overture-creations-5sI6fQgYIuo.png"
-    )
-    return image.resize((width, height))
+def _create_image(height=128, width=128, batch_size=1, channel=3, input_type="pil"):
+    if input_type == "pil":
+        image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/overture-creations-5sI6fQgYIuo.png"
+        ).resize((width, height))
+    elif input_type == "np":
+        image = np.random.rand(height, width, channel)
+    elif input_type == "pt":
+        image = torch.rand((channel, height, width))
+
+    return [image] * batch_size
+
+
+def to_np(image):
+    if isinstance(image[0], PIL.Image.Image):
+        return np.stack([np.array(i) for i in image], axis=0)
+    elif isinstance(image, torch.Tensor):
+        return image.cpu().numpy().transpose(0, 2, 3, 1)
+    return image
 
 
 class OVStableDiffusionPipelineBaseTest(unittest.TestCase):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion",)
     MODEL_CLASS = OVStableDiffusionPipeline
+    TASK = "text-to-image"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_num_images_per_prompt(self, model_arch: str):
@@ -104,6 +120,36 @@ def callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
         self.assertTrue(callback_fn.has_been_called)
         self.assertEqual(callback_fn.number_of_steps, inputs["num_inference_steps"])
 
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    def test_shape(self, model_arch: str):
+        height, width, batch_size = 128, 64, 1
+        pipeline = self.MODEL_CLASS.from_pretrained(MODEL_NAMES[model_arch], export=True)
+
+        if self.TASK == "image-to-image":
+            input_types = ["np", "pil", "pt"]
+        elif self.TASK == "text-to-image":
+            input_types = ["np"]
+        else:
+            input_types = ["pil"]
+
+        for input_type in input_types:
+            if self.TASK == "image-to-image":
+                inputs = self.generate_inputs(height=height, width=width, batch_size=batch_size, input_type=input_type)
+            else:
+                inputs = self.generate_inputs(height=height, width=width, batch_size=batch_size)
+            for output_type in ["np", "pil", "latent"]:
+                inputs["output_type"] = output_type
+                outputs = pipeline(**inputs).images
+                if output_type == "pil":
+                    self.assertEqual((len(outputs), outputs[0].height, outputs[0].width), (batch_size, height, width))
+                elif output_type == "np":
+                    self.assertEqual(outputs.shape, (batch_size, height, width, 3))
+                else:
+                    self.assertEqual(
+                        outputs.shape,
+                        (batch_size, 4, height // pipeline.vae_scale_factor, width // pipeline.vae_scale_factor),
+                    )
+
     def generate_inputs(self, height=128, width=128, batch_size=1):
         inputs = _generate_inputs(batch_size)
         inputs["height"] = height
@@ -115,13 +161,16 @@ class OVStableDiffusionImg2ImgPipelineTest(OVStableDiffusionPipelineBaseTest):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion",)
     MODEL_CLASS = OVStableDiffusionImg2ImgPipeline
     ORT_MODEL_CLASS = ORTStableDiffusionImg2ImgPipeline
+    TASK = "image-to-image"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_compare_diffusers_pipeline(self, model_arch: str):
         model_id = MODEL_NAMES[model_arch]
         pipeline = self.MODEL_CLASS.from_pretrained(model_id, export=True)
-        inputs = self.generate_inputs()
+        height, width, batch_size = 128, 128, 1
+        inputs = self.generate_inputs(height=height, width=width, batch_size=batch_size)
         inputs["prompt"] = "A painting of a squirrel eating a burger"
+        inputs["image"] = floats_tensor((batch_size, 3, height, width), rng=random.Random(SEED))
         np.random.seed(0)
         output = pipeline(**inputs).images[0, -3:, -3:, -1]
         # https://github.com/huggingface/diffusers/blob/v0.17.1/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py#L71
@@ -139,16 +188,17 @@ def test_num_images_per_prompt_static_model(self, model_arch: str):
         outputs = pipeline(**inputs, num_images_per_prompt=num_images, generator=np.random.RandomState(0)).images
         self.assertEqual(outputs.shape, (batch_size * num_images, height, width, 3))
 
-    def generate_inputs(self, height=128, width=128, batch_size=1):
+    def generate_inputs(self, height=128, width=128, batch_size=1, input_type="np"):
         inputs = _generate_inputs(batch_size)
-        inputs["image"] = floats_tensor((batch_size, 3, height, width), rng=random.Random(SEED))
+        inputs["image"] = _create_image(height=height, width=width, batch_size=batch_size, input_type=input_type)
         inputs["strength"] = 0.75
         return inputs
 
 
 class OVStableDiffusionPipelineTest(unittest.TestCase):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion",)
     MODEL_CLASS = OVStableDiffusionPipeline
+    TASK = "text-to-image"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_compare_to_diffusers(self, model_arch: str):
@@ -247,6 +297,7 @@ class OVStableDiffusionInpaintPipelineTest(OVStableDiffusionPipelineBaseTest):
     SUPPORTED_ARCHITECTURES = ("stable-diffusion",)
     MODEL_CLASS = OVStableDiffusionInpaintPipeline
     ORT_MODEL_CLASS = ORTStableDiffusionInpaintPipeline
+    TASK = "inpaint"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_compare_diffusers_pipeline(self, model_arch: str):
@@ -262,6 +313,17 @@ def test_compare_diffusers_pipeline(self, model_arch: str):
             generator=np.random.RandomState(0),
         )
         inputs = self.generate_inputs(height=height, width=width)
+
+        inputs["image"] = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/overture-creations-5sI6fQgYIuo.png"
+        ).resize((width, height))
+
+        inputs["mask_image"] = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
+        ).resize((width, height))
+
         outputs = pipeline(**inputs, latents=latents).images
         self.assertEqual(outputs.shape, (batch_size * num_images, height, width, 3))
 
@@ -285,16 +347,8 @@ def test_num_images_per_prompt_static_model(self, model_arch: str):
 
     def generate_inputs(self, height=128, width=128, batch_size=1):
         inputs = super(OVStableDiffusionInpaintPipelineTest, self).generate_inputs(height, width, batch_size)
-        inputs["image"] = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/overture-creations-5sI6fQgYIuo.png"
-        ).resize((width, height))
-
-        inputs["mask_image"] = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
-        ).resize((width, height))
-
+        inputs["image"] = _create_image(height=height, width=width, batch_size=1, input_type="pil")[0]
+        inputs["mask_image"] = _create_image(height=height, width=width, batch_size=1, input_type="pil")[0]
         return inputs
 
 
@@ -303,6 +357,7 @@ class OVtableDiffusionXLPipelineTest(unittest.TestCase):
     MODEL_CLASS = OVStableDiffusionXLPipeline
     ORT_MODEL_CLASS = ORTStableDiffusionXLPipeline
     PT_MODEL_CLASS = StableDiffusionXLPipeline
+    TASK = "text-to-image"
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
     def test_compare_to_diffusers(self, model_arch: str):
@@ -387,6 +442,7 @@ class OVStableDiffusionXLImg2ImgPipelineTest(unittest.TestCase):
     MODEL_CLASS = OVStableDiffusionXLImg2ImgPipeline
     ORT_MODEL_CLASS = ORTStableDiffusionXLImg2ImgPipeline
     PT_MODEL_CLASS = StableDiffusionXLImg2ImgPipeline
+    TASK = "image-to-image"
 
     def test_inference(self):
         model_id = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
@@ -396,10 +452,12 @@ def test_inference(self):
             pipeline.save_pretrained(tmp_dir)
             pipeline = self.MODEL_CLASS.from_pretrained(tmp_dir)
 
-        inputs = self.generate_inputs()
+        batch_size, height, width = 1, 128, 128
+        inputs = self.generate_inputs(height=height, width=width, batch_size=batch_size)
+        inputs["image"] = floats_tensor((batch_size, 3, height, width), rng=random.Random(SEED))
         np.random.seed(0)
         output = pipeline(**inputs).images[0, -3:, -3:, -1]
-        expected_slice = np.array([0.5675, 0.5108, 0.4758, 0.5280, 0.5080, 0.5473, 0.4789, 0.4286, 0.4861])
+        expected_slice = np.array([0.5683, 0.5121, 0.4767, 0.5253, 0.5072, 0.5462, 0.4766, 0.4279, 0.4855])
         self.assertTrue(np.allclose(output.flatten(), expected_slice, atol=1e-3))
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES)
@@ -413,8 +471,8 @@ def test_num_images_per_prompt_static_model(self, model_arch: str):
         outputs = pipeline(**inputs, num_images_per_prompt=num_images, generator=np.random.RandomState(0)).images
         self.assertEqual(outputs.shape, (batch_size * num_images, height, width, 3))
 
-    def generate_inputs(self, height=128, width=128, batch_size=1):
+    def generate_inputs(self, height=128, width=128, batch_size=1, input_type="np"):
         inputs = _generate_inputs(batch_size)
-        inputs["image"] = floats_tensor((batch_size, 3, height, width), rng=random.Random(SEED))
+        inputs["image"] = _create_image(height=height, width=width, batch_size=batch_size, input_type=input_type)
         inputs["strength"] = 0.75
         return inputs

tests/openvino/utils_tests.py

@@ -57,6 +57,7 @@
     "mobilenet_v1": "google/mobilenet_v1_0.75_192",
     "mobilenet_v2": "hf-internal-testing/tiny-random-MobileNetV2Model",
     "mobilevit": "hf-internal-testing/tiny-random-mobilevit",
+    "mpt": "hf-internal-testing/tiny-random-MptForCausalLM",
     "mt5": "stas/mt5-tiny-random",
     "nystromformer": "hf-internal-testing/tiny-random-NystromformerModel",
     "pegasus": "hf-internal-testing/tiny-random-pegasus",