Drop noisy generate warnings when do_sample=False (or num_beams=1)

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -4,13 +4,25 @@ LABEL maintainer="Hugging Face"
 ARG DEBIAN_FRONTEND=noninteractive
 
 RUN apt update && \
-    apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg git-lfs && \
+    apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg git-lfs libjpeg-turbo8-dev libpng-dev zlib1g-dev && \
     apt clean && \
     rm -rf /var/lib/apt/lists/*
 
 RUN git lfs install
 
 RUN python3 -m pip install --no-cache-dir --upgrade pip numpy importlib-metadata setuptools wheel ninja pytesseract "itsdangerous<2.1.0"
+
+# Rebuild torchvision so decode_image has libjpeg and ROCm image ops stay on GPU.
+RUN python3 -m pip install --no-cache-dir "setuptools<81" pybind11
+RUN TV_VERSION=$(python3 -c "import torchvision; print(torchvision.__version__.split('+')[0])") && \
+    python3 -m pip uninstall -y torchvision && \
+    git clone --depth 1 --branch "v${TV_VERSION}" https://github.com/pytorch/vision.git /tmp/vision && \
+    cd /tmp/vision && \
+    sed -i -E 's|list\(CSRS_DIR\.glob\("([^"]+\.cpp)"\)\)|[p for p in CSRS_DIR.glob("\1") if not p.name.endswith("_hip.cpp")]|g' setup.py && \
+    FORCE_CUDA=1 TORCHVISION_USE_FFMPEG=0 TORCHVISION_USE_VIDEO_CODEC=0 \
+    python3 -m pip install --no-cache-dir --no-build-isolation -v . && \
+    cd / && rm -rf /tmp/vision
+
 RUN python3 -m pip install --no-cache-dir --no-build-isolation git+https://github.com/facebookresearch/detectron2.git
 
 ARG REF=main

examples/modular-transformers/configuration_duplicated_method.py

@@ -14,7 +14,7 @@
 
 
 @auto_docstring(checkpoint="meta-duplicated_method/DuplicatedMethod-2-7b-hf")
-@strict(accept_kwargs=True)
+@strict
 class DuplicatedMethodConfig(PreTrainedConfig):
     r"""
     ```python

examples/modular-transformers/configuration_my_new_model.py

@@ -14,7 +14,7 @@
 
 
 @auto_docstring(checkpoint="meta-my_new_model/MyNewModel-2-7b-hf")
-@strict(accept_kwargs=True)
+@strict
 class MyNewModelConfig(PreTrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel

examples/modular-transformers/configuration_my_new_model2.py

@@ -13,7 +13,7 @@
 
 
 @auto_docstring(checkpoint="meta-my_new_model2/MyNewModel2-2-7b-hf")
-@strict(accept_kwargs=True)
+@strict
 class MyNewModel2Config(PreTrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma

examples/modular-transformers/configuration_new_model.py

@@ -13,7 +13,7 @@
 
 
 @auto_docstring(checkpoint="google/new_model-7b")
-@strict(accept_kwargs=True)
+@strict
 class NewModelConfig(PreTrainedConfig):
     r"""
     use_bidirectional_attention (`bool`, *optional*):

examples/modular-transformers/image_processing_new_imgproc_model.py

@@ -4,277 +4,24 @@
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_new_imgproc_model.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-import numpy as np
 import torch
 
-from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
-from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format
-from ...image_utils import (
-    OPENAI_CLIP_MEAN,
-    OPENAI_CLIP_STD,
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    infer_channel_dimension_format,
-    is_scaled_image,
-    make_flat_list_of_images,
-    to_numpy_array,
-    valid_images,
-    validate_preprocess_arguments,
-)
-from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging
-
-
-if is_vision_available():
-    import PIL
-
-
-logger = logging.get_logger(__name__)
-
-
-class ImgprocModelImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a IMGPROC_MODEL image processor.
-
-    Args:
-        do_resize (`bool`, *optional*, defaults to `True`):
-            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
-            `do_resize` parameter in the `preprocess` method.
-        size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
-            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
-            method.
-        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
-            Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
-            overridden by the `resample` parameter in the `preprocess` method.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
-            `do_rescale` parameter in the `preprocess` method.
-        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
-            Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
-            overridden by the `rescale_factor` parameter in the `preprocess` method.
-        do_normalize (`bool`, *optional*, defaults to `True`):
-            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
-            method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
-        image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
-            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
-            overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
-            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-            Can be overridden by the `image_std` parameter in the `preprocess` method.
-        do_convert_rgb (`bool`, *optional*, defaults to `True`):
-            Whether to convert the image to RGB.
-    """
-
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-        self,
-        do_resize: bool = True,
-        size: dict[str, int] | None = None,
-        resample: PILImageResampling = PILImageResampling.BICUBIC,
-        do_rescale: bool = True,
-        rescale_factor: int | float = 1 / 255,
-        do_normalize: bool = True,
-        image_mean: float | list[float] | None = None,
-        image_std: float | list[float] | None = None,
-        do_convert_rgb: bool = True,
-        **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-        size = size if size is not None else {"height": 384, "width": 384}
-        size = get_size_dict(size, default_to_square=True)
-
-        self.do_resize = do_resize
-        self.size = size
-        self.resample = resample
-        self.do_rescale = do_rescale
-        self.rescale_factor = rescale_factor
-        self.do_normalize = do_normalize
-        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
-        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
-        self.do_convert_rgb = do_convert_rgb
-
-    def resize(
-        self,
-        image: np.ndarray,
-        size: dict[str, int],
-        resample: PILImageResampling = PILImageResampling.BICUBIC,
-        data_format: str | ChannelDimension | None = None,
-        input_data_format: str | ChannelDimension | None = None,
-        **kwargs,
-    ) -> np.ndarray:
-        """
-        Resize an image to `(size["height"], size["width"])`.
-
-        Args:
-            image (`np.ndarray`):
-                Image to resize.
-            size (`dict[str, int]`):
-                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
-                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
-            data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the output image. If unset, the channel dimension format of the input
-                image is used. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the input image. If unset, the channel dimension format is inferred
-                from the input image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-
-        Returns:
-            `np.ndarray`: The resized image.
-        """
-        size = get_size_dict(size)
-        if "height" not in size or "width" not in size:
-            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
-        output_size = (size["height"], size["width"])
-        return resize(
-            image,
-            size=output_size,
-            resample=resample,
-            data_format=data_format,
-            input_data_format=input_data_format,
-            **kwargs,
-        )
-
-    @filter_out_non_signature_kwargs()
-    def preprocess(
-        self,
-        images: ImageInput,
-        do_resize: bool | None = None,
-        size: dict[str, int] | None = None,
-        resample: PILImageResampling | None = None,
-        do_rescale: bool | None = None,
-        rescale_factor: float | None = None,
-        do_normalize: bool | None = None,
-        image_mean: float | list[float] | None = None,
-        image_std: float | list[float] | None = None,
-        return_tensors: str | TensorType | None = None,
-        do_convert_rgb: bool | None = None,
-        data_format: ChannelDimension = ChannelDimension.FIRST,
-        input_data_format: str | ChannelDimension | None = None,
-    ) -> PIL.Image.Image:
-        """
-        Preprocess an image or batch of images.
-
-        Args:
-            images (`ImageInput`):
-                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
-                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
-            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
-                Whether to resize the image.
-            size (`dict[str, int]`, *optional*, defaults to `self.size`):
-                Controls the size of the image after `resize`. The shortest edge of the image is resized to
-                `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
-                is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
-                edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
-            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
-                Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
-            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
-                Whether to rescale the image values between [0 - 1].
-            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
-                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
-            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
-                Whether to normalize the image.
-            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
-                Image mean to normalize the image by if `do_normalize` is set to `True`.
-            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
-                Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
-            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
-                Whether to convert the image to RGB.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
-                The channel dimension format for the output image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - Unset: Use the channel dimension format of the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the input image. If unset, the channel dimension format is inferred
-                from the input image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-        """
-        do_resize = do_resize if do_resize is not None else self.do_resize
-        resample = resample if resample is not None else self.resample
-        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
-        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
-        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
-
-        size = size if size is not None else self.size
-        size = get_size_dict(size, default_to_square=False)
-        images = self.fetch_images(images)
-        images = make_flat_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor")
-
-        validate_preprocess_arguments(
-            do_rescale=do_rescale,
-            rescale_factor=rescale_factor,
-            do_normalize=do_normalize,
-            image_mean=image_mean,
-            image_std=image_std,
-            do_resize=do_resize,
-            size=size,
-            resample=resample,
-        )
-        # PIL RGBA images are converted to RGB
-        if do_convert_rgb:
-            images = [convert_to_rgb(image) for image in images]
-
-        # All transformations expect numpy arrays.
-        images = [to_numpy_array(image) for image in images]
-
-        if do_rescale and is_scaled_image(images[0]):
-            logger.warning_once(
-                "It looks like you are trying to rescale already rescaled images. If the input"
-                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
-            )
-
-        if input_data_format is None:
-            # We assume that all images have the same channel dimension format.
-            input_data_format = infer_channel_dimension_format(images[0])
-
-        if do_resize:
-            images = [
-                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
-                for image in images
-            ]
-
-        if do_rescale:
-            images = [
-                self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
-                for image in images
-            ]
-
-        if do_normalize:
-            images = [
-                self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
-                for image in images
-            ]
-
-        images = [
-            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
-        ]
-
-        encoded_outputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
-
-        return encoded_outputs
+from ...image_processing_backends import TorchvisionBackend
+from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling
+from ...utils import auto_docstring
+
+
+@auto_docstring
+class ImgprocModelImageProcessor(TorchvisionBackend):
+    resample = PILImageResampling.BICUBIC
+    image_mean = OPENAI_CLIP_MEAN
+    image_std = OPENAI_CLIP_STD
+    size = {"height": 384, "width": 384}
+    default_to_square = True
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
 
     def new_image_processing_method(self, pixel_values: torch.FloatTensor):
         return pixel_values / 2

examples/modular-transformers/modeling_dummy_bert.py

@@ -214,23 +214,22 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor]:
         # determine input shapes
-        bsz, tgt_len = hidden_states.shape[:-1]
-        src_len = encoder_hidden_states.shape[1]
+        input_shape = hidden_states.shape[:-1]
 
-        q_input_shape = (bsz, tgt_len, -1, self.attention_head_size)
-        kv_input_shape = (bsz, src_len, -1, self.attention_head_size)
+        hidden_shape = (*input_shape, -1, self.attention_head_size)
 
         # get query proj
-        query_layer = self.query(hidden_states).view(*q_input_shape).transpose(1, 2)
+        query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
 
         is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False
         if past_key_values is not None and is_updated:
             # reuse k,v, cross_attentions
             key_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].keys
             value_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].values
         else:
-            key_layer = self.key(encoder_hidden_states).view(*kv_input_shape).transpose(1, 2)
-            value_layer = self.value(encoder_hidden_states).view(*kv_input_shape).transpose(1, 2)
+            kv_shape = (*encoder_hidden_states.shape[:-1], -1, self.attention_head_size)
+            key_layer = self.key(encoder_hidden_states).view(kv_shape).transpose(1, 2)
+            value_layer = self.value(encoder_hidden_states).view(kv_shape).transpose(1, 2)
 
             if past_key_values is not None:
                 # save all states to the cache
@@ -254,7 +253,7 @@ def forward(
             scaling=self.scaling,
             **kwargs,
         )
-        attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
         return attn_output, attn_weights